Artificial Intelligence Review (2022) 55:2529–2573

https://doi.org/10.1007/s10462-021-10071-7

Recent advancement in VM task allocation system for cloud

computing: review from 2015 to2021

Arif Ullah1 · Nazri Mohd Nawi1 · Soukaina Ouhame2

Accepted: 11 September 2021 / Published online: 23 September 2021

© The Author(s), under exclusive licence to Springer Nature B.V. 2021

Abstract
Cloud computing is new technology that has considerably changed human life at different
aspect over the last decade. Especially after the COVID-19 pandemic, almost all life activity
shifted into cloud base. Cloud computing is a utility where different hardware and software
resources are accessed on pay per user ground base. Most of these resources are avail-
able in virtualized form and virtual machine (VM) is one of the main elements of vis-
ualization.VM used in data center for distribution of resource and application according
to benefactor demand. Cloud data center faces different issue in respect of performance
and efficiency for improvement of these issues different approaches are used. Virtual
machine play important role for improvement of data center performance therefore dif-
ferent approach are used for improvement of virtual machine efficiency (i-e) load balanc-
ing of resource and task. For the improvement of this section different parameter of VM
improve like makespan, quality of service, energy, data accuracy and network utiliza-
tion. Improvement of different parameter in VM directly improve the performance of cloud
computing. Therefore, we conducting this review paper that we can discuss about various
improvements that took place in VM from 2015 to 20,201. This review paper also contain
information about various parameter of cloud computing and final section of paper present
the role of machine learning algorithm in VM as well load balancing approach along with
the future direction of VM in cloud data center.

Keywords Cloud computing · VM · Load balancing approach · Data distribution ·

Virtualization

* Arif Ullah
arifullahms88@gamil.com
Nazri Mohd Nawi
nazri@uthm.edu.my
1
Soft Computing and Data Mining Centre (SMC), Faculty of Computer Science and Information
Technology, Universiti Tun Hussein Onn Malaysia (UTHM), Parit Raja, Malaysia
2
Department of Computer Science, Faculty of Science, Ibn Tofail University, Kénitra, Morocco

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1 Introduction

Cloud computing is new technology that has extensively transformed human life from the
last decade by providing different services and resource with the help of internet using
virtualization system. Cloud resources are variable to every user by rent and release with
specific rule and regulation with the help of internet. Cloud computing is emerging tech-
nology used for storing and accessing resource and application over the web based inter-
net (Buyya et al. 2018). Cloud data center consists of physical and virtual infrastructure
resources which include server, network system and different resources. Cloud data center
is so important that different user demands can access data with the help of these data
center in accurate and fast time. It contains a large amount of data and information which
works under certain rules and regulations (Ouhame et al. 2020; Ullah et al. 2020). Data
center normally used to control various activities such as virtual machine creation and
destruction, routing of user request, network management, resource management and load
balancing technique these all activity are performed with the help of virtualization. Virtu-
alization is the processes in cloud computing in which we create virtual of resource based
on software like hardware resource and software resource. Virtualization is one of the main
element of cloud computing where this technique help to enhance the accuracy an effi-
ciency of cloud computing. Virtual machine (VM) is one the main element of virtualiza-
tion (Ziyath and Senthilkumar 2020; Mishra et al. 2020). When a single resource of cloud
computing can appears as multiple resource this process can be achieved with the help
of virtual machine. For the improvement in VM activity different kind of load balancing
technique are used. A valuable load balancing technique in cloud computing can enhance
the accuracy and efficiency of cloud computing performance. Because now a day user
demands for fast and accurate service within the given time (Devi and Uthariaraj 2016).
Load balancing technique is one of the main parameter for providing fast service in cloud
environments. Cloud technology growing rapidly and used in different field of life like edu-
cation, engineering technology, data-intensive applications, health, life science, geospa-
tial sciences and different scientific and business domains. Cloud computing become an
admired technology in worldwide due to its offer a huge amount of storage and resource
to different companies and organization to access these resource with proper management,
rule and security. Some of the main characteristics are virtualization, viability, large net-
work access, automatic system, security, economical and scalability (Chang et al. 2010;
Ferrer et al. 2019). The rest of this paper is organized as: The necessary backgrounds for
the cloud computing and load balancing approaches are discussed in Sect. 1. Preliminary is
discussed in Sect. 2. In Sect. 3, present the related work. In Sect. 4, Methodology of paper.
In Sect. 5, we present about simulation environment. Section 6, present result and discus-
sion and Sect. 7, conclusion. For the comfort of readers we provided a list of the most fre-
quently used acronyms in the paper are mention in Table 1.

1.1 Contribution of paper

The major contributions of this review paper are summarized as below.

i. Present details information about cloud computing.

ii. Existent details information about load balancing approach and different parameters.
iii. Extant details information about VM role in cloud data center.
iv. Summarizations of main contribution in load balancing technique from 2015 to 2021.

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Recent advancement in VM task allocation system for cloud… 2531

Table 1  List of acronyms Symbol Description Symbol Description

CC Cloud computing IaaS Infrastructure as a Service

VM Virtual machine CDC Cloud Data center
LB load balancing IaaS Infrastructure as a Service
DC Datacenter PaaS Platform as a Service
MP Memory page VMM Virtual machine monitor
QoS Quality of Service PM Physical machine
IoT Internet of thing LAN Local area network
5G Fifth generation CD Cloud datacenter

v. Present-day research direction about load balancing approach and role of VM in cloud
computing.

1.2 Source of information

Data related to our review were extracted from 106 published papers. This collection of
papers has been compiled by consulting various peer-reviewed data sources (Table 1).
These papers highlight the recent advancement in VM task allocation system for cloud
computing from 2015 to 2021.The frequency of publication of this work per year for the
last six years was calculated to visualize the evolution of research on this promising the-
matic of the advancement in VM task allocation system for cloud computing which present
in Fig. 1.
Figure 1 present paper selection criteria and Table 2 show the data source using these
search engine different paper are download from 2015 to 2021.
Table 2 show the data source using these search engine different paper are download
from 2015 to 2021 based on those paper this review paper was conducted.

2 Preliminary

In this section we define those element which are related about cloud computing and load
balancing approach. These parameters are used in VM’s for improvement of cloud data
center performance.

2.1 Cloud computing

According to Nadimi et al. (2020), cloud computing becoming a crucial technology due
to hosting various IT resource for different organization. By providing different service
on demand through with the help of virtualizations rule based on pay and get rule. Cloud
computing is new trend of technology which change human life over the last decade. This
achievement has been done due to the deliveries of virtualized IT resource with the help of
internet. The user can demand to these services with specific rule of pay and gain on real
time environments (Abd Elaziz and Attiya 2020). Cloud resources are as long as collective
tools and any user can charter and let loose these resources with help of internet. The tech-
nology become popular due to the combination of high bandwidth announcement and

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2532 A. Ullah et al.

Fig. 1  Paper selection procedure

Table 2  Database source and Source URL

source URL
Google Scholar https://​schol​ar.​google.​com/
ACM Digital Library http://​dl.​acm.​org/
DBLP URL http://​dblp.​uni-​trier.​de/
Springer www.​sprin​ger.​com
Taylor & Francis http://​taylo​randf​rancis.​com
Wiley Online Library http://​onlin​elibr​ary.​wiley.​com
IEEE Explore http://​ieeex​plore.​ieee.​org/

low cast computing with storage (Ibrahim 2021).The entire bustle of cloud computing are
performed with the help if internet to a certain extent having these service on local or per-
sonal computer. Four main recognized type of cloud computing are private, hybrids, public
and community cloud. Poles apart users and organizations used cloud computing accord-
ing to their persevere and obligation (Zhou 2020). There are four types of cloud comput-
ing which are used in different field of life with specific rule and respective specification.
Cloud computing consist of different types of layer and these have specific role. Applica-
tion layer it consists of a cloud application which is used in a different field. It is the highest
level of the hierarchy and works as an automatic scaling feature. Application layer defines
the commands, responses, data types, and status reporting supported by the protocol. This
layer is the only layer that directly connected or interacts with the end user. It provides
different application for user. Such as simple mail transfer protocol, file transfer, surfing

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Recent advancement in VM task allocation system for cloud… 2533

the internet, chatting with friends, email clients, network data sharing, and various forms
of file and data operations (Mezgár and Rauschecker 2014; Samimi and Patel 2011). Plat-
form layer this layer consists of an operating system and application framework and sits
on the top of the infrastructure layer. The primary purpose of this layer is to minimize the
burden of developing an application or reduce the complicity of development rule work
under virtual machine (VM). Different API and application are used for storage data-based
and logical web application (González-Martínez et al. 2015).Infrastructure layer this layer
creates a pool of resource for storage computing resources with the help of a technology
known as virtualization. It allows (IaaS) customers to create and discard virtual machines
and networks as per their business requirements. They pay for the services they consumed
(IaaS) removes the necessity for the consumer to invest in procuring and operating physical
servers, data storage systems and other networking resources (Rimal, et al. 2010).Hardware
layer this layer is responsible for the management of all physical resource of cloud com-
puting. Such as physical servicer, routers, switches, power and cooling system along with
different resources (Khan et al. 2017). Figure 2 present the structure of cloud computing.
Hardware layer is typically implemented in the cloud data center where it consists of
thousands of different physical resources and they are connected with different rule and
regulation. All layers are important due to their different operation and connectivity with
each other (Lee et al. 2018).Platform layer is the important layer of cloud computing
because it includes different operating system and software development framework that
provide resource to the end user. In little year cloud computing has experienced remarka-
ble growth in economic model and development models. All activities are happened due
to platform layer of cloud computing. Different virtualization systems are controlled and

Fig. 2  Structure of cloud computing

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2534 A. Ullah et al.

Fig. 3  Types of cloud computing

developed with the help of this layer because it deals with software and virtualization
system. Virtual machine is one of the main elements of this layer (Mastelic et al. 2014).
Cloud computing consist of different types Fig. 3 shows types of cloud computing.
Figure 3 show the different type of cloud computing and these are used in different area
of life for different purpose. Private cloud it is designed for a single organization and also
known as the internal cloud. It is established within the organization or connected with the
third party and it is much secured as compared to other types of computing. Private cloud
computing is established for the requirement of third party or the demand of third party.
It is more secure and reliable due to the restriction and rules therefore it become more
expensive than other type of cloud computing (Kuyoro et al. 2011; Kotha et al. 2021).
Hybrid cloud it is the combination of public and private cloud with the respective strength
and weaknesses. Organizations attempt to achieve the best from both types of the cloud
and also known as the federation cloud. It is the intermediate between private and public
because sometimes the user who uses public cloud shift to the private cloud because they
suddenly need more secure data. This process can be done with the help of the hybrid
cloud (Liu et al. 2011).Community cloud is cloud service model which provide service to
a limited number of individuals or organization that managed and secured by all partici-
pating organizations or a third-party managed service provider. Community clouds are a
hybrid form of private clouds built and operated specifically for a targeted group. These
communities have similar cloud requirements and their ultimate goal is to work together to
achieve their business objectives (Hashem et al. 2015).

2.2 Cloud computing characteristics

According to Abd Elaziz et al. (2020), cloud computing is a general term for anything
that involves in delivering hosted services over the internet. Cloud providers are com-
peting with each other and they constantly expand their services in order to differentiate
themselves. Cloud computing is named as such because the information being accessed is
found remotely in the cloud or a virtual space. Cloud computing has succeeded in bring-
ing change in different field of life. Availability cloud computing provides high availability
and some benefits for every type of user in a different field. Availability is one of the main

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Recent advancement in VM task allocation system for cloud… 2535

pillars of information system where it refers to the uptime of system and network of sys-
tem where they collectively provide a service during its usage. The traditional systems are
limited on local installations when they shift in to cloud computing where entire organiza-
tion are able to use availability service of cloud computing along with end user. When it
time for IT infrastructure establishment then an organization make decision on the bases
of availability because it is the main key decision factor. Availability have been the major
concern in distributed system because highly available service in cloud computing and are
main element for satisfaction of cloud user (Zarandi et al. 2020). Scalability is the attribute
that presents the ability of the software, network and process of an organization to man-
age the increasing of user demand. Normally scalability means frequent speed in cloud
computing in which the ability of system or product to continue working after its context
changed like volume or size in order to meet the user need. Scalability is a sign of stability
and competitiveness which means the organization or network system are ready to handle
the influx of demand according to change need and update of the system. Due to the prop-
erty of scalability in cloud computing lots of companies are shifting to cloud computing
(Phanden et al. 2011).Cloud security also known as cloud computing security that consists
of different policies, controls, procedures and technologies that work together to protect
cloud-based systems, data and infrastructure from unauthorized access. Cloud security is
a joint responsibility of cloud provider and business owner or end-user. Security addresses
both physical and logical issue in different model and layer (Giret et al. 2015). Cloud auto-
mation is a broad term which refers to the processes tools and resources that used by an
organization to reduce the manual efforts and it associated with the managing cloud com-
puting workloads. It can be applied to different types of cloud computing. Cloud automa-
tion is a fundamental building block for cloud computing. It can be applied in a software
layer where a complex system is used to configure and roll out the system balances for the
network system. The aim is to make all activities related to computing is as fast, efficient,
and handoff as possible thought for the use of the various systems (Elsherbiny et al. 2018).
One of the main character of cloud computing is virtualization. It is one of the main ele-
ments of cloud computing with refers virtual rather than actual of something. Where IBM
introduce virtualization concept in 1960 and early 1970 and this technology reliable and
time sharing (Wang et al. 2020). Normally different types of virtualization used in cloud
computing some of the important type of virtualizations are mention Fig. 4.

Fig. 4  Types of virtualizations

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2536 A. Ullah et al.

Hardware virtualizations is the type of virtualizations where process several different

servicer are combine and work together as single or different VM work as servers. These
processes have different type and used for different purpose (Smith and Nair 2005).Full vir-
tualization in this type of virtualization where VM can run any operating system using the
system resource or hardware. Main types of full virtualization are Xen server, Xen, KVM
and Virtual box (Obasuyi and Sari 2015). Virtualization the guest operating system does
not run fully on the virtual machine. It does not work fully and it gets help from hypervi-
sor or with the VMM for working. It just improves the functionality of the operating sys-
tem. Partial virtualization is the process in which software modification took place any tag
(Guo et al. 2010).Network virtualization this type of virtualization we combine software
network resource and hardware network resource which are in the same network under the
same administrative units. These types of virtualization allow network optimization and
scalability in large network as well as use full for improvements network efficiency and
productivity. This type of virtualization furthered divided in two types. External; in this
type of virtualization it combines several networks in to single unit. Internal; in this type
of virtualization virtual network interface cards are used for network life functionality in to
single system this process is known as internal network virtualizations (Sharif et al. 2009).
Storage virtualization is the process of arranging different physical storage from multiple
network storage in to single form. The technology that refers to identify available storage
and capacity form different physical device in to a pool of storage device which can be
used as a virtual environment. Server virtualization is the processing of making a physi-
cal server in to the virtual server. The server administers uses a software application that
divided the server in multiple isolated virtual servers and that also acts as a physical server
(Sheikholeslami and Navimipour 2017). Desktop virtualization’ the process of isolating a
logical operating system in to client that can access it. There are many concepts of desk-
top virtualization which are dividing into different categories according to user demand.
Hosted virtualization in this scenario the virtual machine is completely intellection of a
real physical machine. All the feature of a real physical machine like memory, operating
system and storage are also in hosted virtualization. It can be achieved by the configuration
of the real system (Bhandia et al. 2019). In memory virtualization technique whenever the
memory required for the system processing and actual memory is less than the virtualiza-
tion process is used for memory. In data virtualization, the collection of data for different
location and the user can access them easily. It provides front and back ends application
method (Abramson et al. 2006).

2.3 Virtual machine (VM)

In cloud datacenter if a single resource can materialize as multiple resources and this route
can be achieved with the help of virtual machine (VM). Efficient VM is very important
for energy saves and improvement the working of cloud computing. The VM normally
replaces the physical resource with their ability and operating system that makes the same
environment as hardware. The VM provides a better security model as compared to the
normal system VM which is also known as a guest machine. VM are an efficient isolated
duplicate of real machine which allow the multiplexing of the underling physical machine.
VM technologies allow great deals of elasticity in dynamic management of workload on
servers (Ullah et al. 2020). Figure 5 present the working criteria of VM in cloud data
center.

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Recent advancement in VM task allocation system for cloud… 2537

Fig. 5  Working section of VM (Manasrah et al. 2017)

Virtual machine is a software based component which is an abstraction of the under-

lying hardware provided by the virtualization technology. Availability can be efficiently
achieved by managing the virtual machines properly (Ullah et al. 2020).One of the main
element of VM are hypervisor or VM manager where the model or program that handles
more than one VM operating system on single host. It is a responsibility of hypervisor to
provide resources and a processor to each virtual operating system on the same host. VM
manager has two main types, bare metal hypervisor, and hosted hypervisor (Nasim and
Kassler 2014). Bare metal hypervisor is type of hypervisor it work on hardware and control
over all its accessible resources like memory, CPU etc., as no intercessor is required to
access the resources like Hyper-V, VMware. Hosted hypervisor is the type of hypervisor
which is used for installed in the operating system of a server and that operating system has
a control over it example are virtual box, xen, VM ware player (Hwang et al. 2015). Migra-
tion of VM’s is the process in which one host shift to another without disrupting existing
work this is process is known as VM migration. There are two types of VM migration
which are online and offline migration. Online migration concept was introduce by Chris-
topher Clark in this process running VM can be transferred to other host without affecting
the system or the server this process can be done by making the system off the VM. When
it transfer to the host it restart again all these activity done in dynamic system (Lee et al.
2010). Offline migration in this process the transfer of VM to another host during running
of the system can interruption the network or server during the transfer of the system.VM
migration is one of the main elements of virtualization system in cloud computing which
allow the movements of VM from one host to another with different rules.VM migration
need both sender side and receiver site for transfer of file and VM and state file. VM migra-
tion play different rule in cloud computing but one of the main rule is VM replacement
system as resource allocation and communication system (Zhou et al. 2010). Preparation
time the time between the start of migration process and VM processor state is sent to the

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2538 A. Ullah et al.

host during the VM run is known as preparation time. The main parameters of preparation
time are given below.

i. Resume time is the between the resuming of the VM running and migration is called
resume time.
ii. Pages transferred is the amount of memory pages transferred comprising the copies
of pages.
iii. Downtime is the time during which the running of virtual machine is stopped it con-
tains sending of state of the processor.
iv. Total migration time between instigating phase of the migration to the end of the
migration process.
v. TMT is used for the resource emancipating on both the source and the terminus node
(Beloglazov and Buyya 2015).

Live virtual machine migration techniques: Pre-copy migration warm-up phase this is
the in early phase for the hypervisor to create the copies of required memory pages to send
to the destination section from the source section. During this route virtual machine is not
terminated by the hypervisor. If some variations are there in the pages of source node dur-
ing the process of moving the replacement copies. Then memory pages will be duplicated
again and again until the data reduplicating rate is less than the rate of moved page which
does not contains the recent value (Choudhary et al. 2017). Figure 6 present the working of
VM section.
Stop-and-copy phase in this process the VM are terminated at the source node the
amount of fata changed that is left will be moved to the destination node and VM start pro-
cessing at the destination (Ullah et al. 2019). Post copy migration in this type of migration

Fig. 6  Working section of VM (Ahmad et al. 2015)

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Recent advancement in VM task allocation system for cloud… 2539

technique VM is suspended from source host for some time then the VM state is trans-
ferred to the destination host. Once it is received at terminus, at the same time it starts
working after resource portion process even though most of the memory state is living on
the source host. When virtual machine tries to fetch the pages which are not being sent
from the endpoint host; it will create page faults (Yang et al. 2017; Hussain et al. 2019).
Hybrid VM migration this type of migration used both pre copy and post copy migration
techniques properties. It is divided into different phase which are as:

i. Preparation phase: System resources required at the object host are reticent.
ii. Bounded pre copy rounds phase: Determine the surrounded pre copy rounds and
working set of VM is reassigned from sender server to the receiver server.
iii. Virtual machines resume phase: At the receiver server, it inaugurations the reassigned
state.
iv. On demand paging phase: On the basis of application, requests of read/write.

Preparation phase: System resources required at the object host are reticent (Navamani
et al. 2018). Figure 7 present the VM migration section.
Post copy variations: Post copy through demand paging in this process the pages are
reassigned only once and it will result in page fault when requesting the referenced pages
from the source node over the network. As a result, it will slow down the dispensation of
the VM as it raises the length of the resume time and creates the enslavements in the form
of a page faults residing for erratic time periods (Shah 2011). Post copy through active
pushing: Enslavements is bargain in the form of an undetached pages living for change-
able time periods; one way is to initiatively push” the pages on the endpoint node from the
source even though the VM continues running at the destination host. Active push evades

Fig. 7  VM migration section

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2540 A. Ullah et al.

the transfer of pages which are criticized in the destination virtual machine. Thus, pages
are only sent once either by petition paging or active ambition (Umar and Baseer 2016;
Beloglazov et al. 2011). VM replacement system consists of two main sections which are
static and dynamic system. Static VM replacement system in which the mapping of the
VM is fixed during exaction time and change able for fix time. Dynamic VM replacement
system in which the VM allow changes at any stage during the expectation time and those
algorithms which are used for replacement of VM are called reactive and proactive VM
placement (Indukuri 2016). Figure 8 show the VM replacement system.
Post copy through pre paging: It is very difficult to know the strict fault tolerance bear-
ing of pages but by approximating the defective speeches to predict VM’s memory access
pattern, we can presume better page forceful sequence to access the arrangements and the
incidence of page faults in improvement (Besta et al. 2019). Table 3 present the different
parameter of VM migration section.
Table 3 present the VM migration parameter while Fig. 9 present VM migration metrics
and these all elements improve the VM working criteria.
Figure 9 shows the VM migration metrics and these all elements improve the VM work-
ing criteria. When user demand increase in cloud data center for accessing data then some
VM become under-loaded or become overloaded which may delay or failure of the system.
In order to avoid this kind of situations then load balancing technique is used.
{⟨ K
⟩ K
∑ ∑
f (VM) ∕Ti − Lv Underloaded ∕Ti − Lv
V=1 u=1

K
∑
Overloaded = ∕Ti − Lv Balanced (1)
i=1

where Ti present the given task, K is the capacity of VM where given data are compare
with these value (i-e),i , u, andv (Ullah et al. 2019). To solve the above situation in VM and
servicer of cloud computing load balancing technique used. When research improve the
above Eq. (1) then the metric of VM improve which are mention in Fig. 9 for improvement
of the above activity load balancing approach used.

2.4 Load balancing technique

Load balancing technique used for management of resource, data and application on cir-
cumstance that maximum throughput with slight time and also diving different type of traf-
fic between VM’s and servicer without any impediment. Due to the growth and improve-
ment in cloud technology there is increase of user and they demand for better services
(Tracz et al. 2019).Virtual load balancer offer more elasticity to balance the workload by
distributing the resource or traffic across multiple VM’s. Virtual load balancing aims to

Fig. 8  Type of VM placement

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 Table 3  VM migration parameter
VM Migration Technique Rewards Minuses

Non-live migration Stop VM at source then transfer Simple concept and easy to implement Down time is more
Post-copy First transfer the execution and then the memory Memory transferred in a single pass and has less More down time as compared to pre copy
Recent advancement in VM task allocation system for cloud…

network overhead
Pre-copy First transfer the memory and then transfer the Down time < 1 s. On aborting Migration, systems do Overhead of duplicate page transmission
Execution not crash due to running VM in source host
2541

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Fig. 9  VM migration metrics

mimic software-driven transportation in the course of virtualization. It runs the software

of a physical load balancing appliance on a virtual machine (Bamgbade et al. 2019). Load
balancing technique has become an important approach that has been used for reduction of
response time and provide maximum throughput with slight time duration. When multiple
requests from user site are receive load balancing approach properly distributed them to
different device according to their accessibility. If load balancing approaches are not used
in any area of cloud computing then user and provider wait for a long time that they request
for any resource and sometime deadlock occur (Mushtaq et al. 2017). Therefore different
kind of load balancing technique used in cloud computing which are mention in Fig. 10
these technique are used different sections of cloud computing for different purpose.
Figure 10 show all type of load balancing which are used for different purpose in cloud
computing but in this paper we just focus on load balancing technique which are used
for VM resource allocation system. Essentially there are two main types of load balanc-
ing which are dynamic load balancing approach and static load balancing approach. In
dynamic load balancing approach where it can be change able at any stage of the network
during the process system. The main benefit of dynamic load balancing is that if any node
fails during the network execution it not affects the system and it not affects the current
stage of the network (Milani and Navimipour 2016). While static load balancing approach
the performance on the current stage affect the network or it static load balancing approach
which is not change able during the network execution. It normally works in homogenous
and stable environment therefore it provides good result. These two types of load balanc-
ing approach used for different propose and these are future divided in to different groups
(Zaki et al. 1996). For improvement in load balancing technique different algorithm are
used or we can say load balancing approach can be implement with the help of algorithms.
Different types of algorithm used for improvement in load balancing approach and Fig. 11
show the type of algorithm which is used in cloud data center for different purpose.
Figure 11 shows all those area where different algorithms are design for improvement in
load balancing purpose for cloud datacenter.

2.5 Load balancing technique facing different kind of issue

Cloud computing change the human life as aspect of different filed and environment by
providing different service and resource according to the user demands. However cloud
computing facing different kind of issues and load balancing technique is one of them.
Load balancing technique also facing different kind of issues which are (1) VM migration:
virtualization is process with the help of this technique several VM’s are created in single

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Recent advancement in VM task allocation system for cloud… 2543

Fig. 10  Type of load balancing technique

physical machine. These VM are self-governing in nature and have different configuration
when a physical machine become overloaded then these VM are used to transfer the data
using VM migration approach this approach facing algorithm connectivity problems at
some points this process is known as VM migration (Xu 2012). (2) Single point failure:
some dynamic load balancing approaches are designed by center node approach system it
means all the decision of the movement of the network or data in the network depends on
that center node. If the center node crashes down then the entire network of system will
be down for that reason there will be a proper algorithm need to developed that manage
and distribute the work load equally not depends on the center node if it down (Milani and
Navimipour 2016). (3) Storage management: cloud technology has solved the traditional
storage system because the traditional storage system consists of various issues but cloud
allows the user to store heterogeneously without any access problems. But cloud storage

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Fig. 11  Algorithm used in cloud datacenter

increase day by day which causes replication and consistency issues in data size which
make duplication data storage policy and replication issues. These issues make data set
availability and increase complexity for load balancing technique because they take more
time for exestuation. (4) Algorithm complexity: in load balancing technique algorithm
must be implementing according to the demand and simple way. When we used complex
algorithm it reduce the performances and accuracy of the system (Imdad et al. 2020). (5)
Load balancer scalability: on demand of user different service is providing within the time
load balancer section work. If good load balancer section used it solve the user demand
it term of power storage and efficiently. (6) Selection policy: when the tasks or data are
selected for transfer from one VM to another it used. This policy identifies the data or
task based on amount of overhead that is required for migration from one point to another
(Khan et al. 2019; Zaki et al. 1996).

3 Related work

In this section we present some of the pervious researcher work they modify, hybrid and
introduce a technique for improvement in load balancing technique in cloud data center.
Wang et al. (2010), proposed load balancing Min Min (LBMM) algorithm where the
framework of this algorithm consist of three levels which are request manager, server man-
ager, VM manager. The request manger is responsible for receiving the workload and then
assign to the server manager where servicer manager distributed the data to different VM
where VM manager executed the data. In this section the author modified the VM manager
in to two sections first it checks the data priority after the priority base sent the data to exe-
cution. The author found out that the proposed method suffered from some limitations such
as the working process of this algorithm is slow due to the three level information where
after the information step it check the parameter then assign the task to VM’s. Sharma
(2015), modified the throttled algorithm where previous algorithm assigned the task to VM
directly but after the modified it first requested to load balancer section of appropriate VM

13
Recent advancement in VM task allocation system for cloud… 2545

for that task it check the index then the task was assigned to VM. The author demonstrated
some limitations of that proposed techniques which is the task are not given priority base.
Domanal and Reddy (2014), defined good load balancing that improved the throughput by
minimizing the response time along with fault tolerance. Normally load balancing prob-
lems occur during the over demand of VM’s. In order to overcome load balancing the
author modified dynamic weighted live migration (DWLM) algorithm for selection and
location polices in virtual machine. The author changed selection part in this algorithm for
allocation of task. Yakhchi et al. (2015), proposed Cuckoo optimization algorithm which
consisted of three different part in which first phase applied COA to detect the over load
VM and transfer to under-loaded VM. First iteration information was stored in habit sec-
ond iteration information were stored in ELR formula that checked the VM and sent data to
load balancer section. Even though the proposed technique performed better as compared
to the normal COA search but the execution time of the algorithm was increased. Monika
et al. (2015), proposed honey bee galvanizing algorithm in which he increased honey bee
forage technique with random stealing is employed for task and cargo leveling. They found
out that if the VM become overload then the task moves to the neighborhood VM. If those
load worth is less than threshold value, then this task were given to that VM. The proposed
technique improved the efficiency parameter as well as the average time of network but
network still facing problems in accuracy. Babu and Samuel (2016), proposed an algorithm
name as enhanced Bee colony algorithm that works the same as the bee work for food
source. Authors proposed load balancing mechanism work in to four different steps which
are VM current load calculation, load balancing decision, VM grouping and task schedul-
ing. The authors modified VM current load calculation section and changed the fitness
value in this section. Even though the modification improved the quality of services and
migration level but still exists the efficiency problem at data allocation section. Babu et al.
(2016), proposed honey bee forging algorithm known as (HBLL-B) where this algorithm
work based on the behavior of honey bee. Two main type of honey bee are their one of then
who find food source and the other one who reap finder honey bee go for search of food
source when they find they come to comb and make dance it show the quality and quantity
of the honey. Then the reaper goes on that food source get the food return to the comb and
make dance if the food source remains. Like the same honey bee forging algorithm work in
cloud computing for load balancing purpose where different VM are like honey bee and
task are food source. Forger chooses on VM to check the profit of food if the profit is less
then cost it stop the working. The author change two main parameter of algorithm which
fitness vale and iteration section. However due to this change the computation of the profit
may cause an additional overhead in which some task was not assigning the VM which
wait for next round. Rani and Kannan (2017), proposed Bat algorithm for load balancing
technique by assuring that every VM take more or equal amount of data and can get at any
stage. Therefore, Bat algorithm was used along with column maxima technique in which
total execution time is minimized and load is balance with total number of VM. However,
the proposed technique was not affective for large number of task because due to the col-
umn metric function which works two dimensions. Holland et al. (2017), proposed Artifi-
cial Bee Colony algorithm for load balancing purpose in VM where it work same as the
bee work in the nectar. The author modified and initialized the population where iteration
move the employed bee on the food, onlooker bee determine nectar amount move source of
searching for new food source memorize the best food source until condition. The
researcher modified the initialize population because it was pervious randomly s (i = 0) of
FS (food source). FS is the size of employed bee equal to onlooker bee each iteration it
took (i = 123) up to food source count. Even though the change of fitness function

13
2546 A. Ullah et al.

probability section improved but second phase onlooker section still not improved. Devi
et al. (2018), proposed two techniques for improving load balancing. The first one is a theo-
retical concept in the network graph where graphic concept is used for monitoring the load
and minimum dominating set (V-MDS) algorithm and second live virtual machine migra-
tion in virtual machine for load balancing improvement using new system and traffic-aware
live VM migration for load balancing (ST-LVM-LB) algorithm and check the result with
dynamic management algorithm (DMA). The researcher proved that the used of graph the-
oretical algorithm improved the load allocation but it took more time in term of verifica-
tion. Fahim et al. (2018), studied about load balancing technique algorithm which are used
for improvement in VM section. Authors compared five best algorithms in term of accu-
racy and performance of task. The study demonstrated that Bat algorithm worked more
accurate in term of accuracy and efficiency in load balancing technique. Gamal et al.
(2019), proposed an algorithm known as osmotic bio inspired algorithm used for load bal-
ancing purpose. The author used two algorithms which were ABC and ACO algorithm for
load balancing in VM where both algorithms communicated with each other with the help
of osmosis theory. One of the limitations of this algorithm was that the communication
process took more time which cause the network slow. Krishna et al. (2020), proposed an
algorithm known as OLOA in which modification took place at fitness function of Bat
algorithm. Because the data distribution between different VM is done with the fitness
function. Therefore, the author modified the fitness function at load balancer. The new
technique improved the selection methods but still processing time was not increased sig-
nificantly. Thanka et al. (2019), proposed hybrid algorithm using ABC and PSO algorithm
where the study demonstrated that PSO algorithm facing the local search problems because
at the last iteration of local search it did not complete the iteration therefore they changed
with ABC algorithm because ABC algorithm is good at local search. The results showed
that the proposed techniques improved in local search and it increased the data collection
system however the data accuracy issue still existed. As the study of related works of
improvement starting from traditional load balancing technique where no priority are given
for any parameter and it concepts are very simple in implementation without error. Then
researchers changed to activity based method by measuring every activity cost for all
objects and outcomes and it showed that they are better than traditional. Next researchers
shift to behavior based algorithm which are inspired from behavior of animals and other
living organic system and the result of those algorithms were better than the previous two
methods. The related work show that different type of algorithm used for improvement in
load balancing section and different research implement different approach therefore we
summarizes 106 paper in our result section that we get a brief information about load bal-
ancing approach in cloud data center as well as we know about VM and it parameter are
changed for improvement of cloud computing. Our main focus will be machine learning
algorithms that are used for VM task allocation system in cloud data center.

4 Methodology

To study briefly about load balancing technique for VM and what methods, are design for
improvement in task allocation system for VM are used in cloud computing from 2015 to
2021 are discusses in this paper. Figure 12 show the paper selection methods form initial
stage to final stage.

13
Recent advancement in VM task allocation system for cloud… 2547

Fig. 12  The paper collection steps for this paper

Figure 12 show the collection of paper where these papers goes to different steps after
that we select 106 for summarization at final step. The main roles of summarization of
these are that we collect information about, technique, which section modification occur,
advantage, disadvantage which author and at which year it present the paper. After collec-
tion of all these information we will able to define the recent advancement took place in
VM load balancing approach for cloud computing.

5 Why VM need load balancing

Load balancing technique provides a service for distribution of load among different VM’s
equality. Main objective of this technique is continuous service in case of failure of any
servicer in the network make any alternative services. In additional load balancing tech-
nique minimize the response time for data and improve task allocation system in VM
which enhance the system performances at low cast. This technique also improve scalabil-
ity and flexibility for those application which size are increase during next work execution
as well as provide priority of different task during execution time with other task in the
queue. Other objective of load balancing technique are reducing energy system improve
network life time, accuracy, efficiency and improve the network quality of services (Oke
et al. 2021; Sheikholeslam and Jafari Navimipour 2018).

13
2548 A. Ullah et al.

Fig. 13  Load balancing policies

6 Load‑balancing policies

As mention in previously different load balancing technique are used for improvement dif-
ferent section of VM in cloud datacenter. For implementing these algorithm for load bal-
ancing technique different policy are used which are mention below: Figure 13 show these
polices and rules.
Selection policy this process used identification method for data transferred system
in the network and the selection of these task are performed based on amount overhead
required migration after that required it transfer the data (Wong et al. 2014). Location
policy: this policy different resource mention that they are under loaded or overloaded of
that bases takes are sent to them. Which node or resource in under loaded on that bases it
request to VM or service for data. During this process three main elements took participate
which are probing, negotiation and random (Zarandi et al. 2020) Random approaches: the
selection approach policy select the destination or receiver node randomly for transfer of
the data in the probing approach the node used other node or resource to select this desti-
nation. Negotiation approach: node or resource negotiates with each other for proper load
balancing approach (Kulik et al. 2002).Transfer policy in this kind of policy discovers the
situation where task are sent to local to local or remote nodes. It consists of two approaches
which are current task and last received tasks are used to identify the tasks information
transferred. All incoming tasks are entire in to the transfer policy after that decision that
task are distributed (Al-Karaki and Kamal 2004). Information policy this is the main pol-
icy of load balancing technique in which that contain all information in the system like
agent, broadcasting, centralized polling and periodic policy are used for distribution of
information between different node and resource in the network (Wang et al. 2019). All the
above load balancing policies have relation with each other because when the task entire
in the system and initially processed with the help of transfer policy. After this process
the next policy decided whether it needs to transform to remote node or not. For checking
the nodes statuses wither it under loaded or overloaded location policy used. If the node is
overloaded then both transfer policy and location policy collect required information from
the information policy to make decision (Al-Hashimi et al. 2019).

13
Recent advancement in VM task allocation system for cloud… 2549

6.1 Load balancing technique performance

Load balancing techniques are design for improvement of different parameter in the net-
work and these parameter results are comparing with pervious result. If the current tech-
nique improves the performance in terms of that parameter in the network it means the
current load balancing technique working properly. Different researcher defines different
parameter to check the performance of load balancing technique. The performances of any
load balancing techniques are evaluated based on these metrics. Exestuation time: is also
known as completion time in which a specific cloudlets or task time need to complete the
job. Or the time taken by a job for running in system is known as exestuation time.
ExeTime = Task(i) (Fnh(Time) Srt(Time) (2)
where (Fnh(Time) denote the finishing time and Srt(Time) present the starting time.
Performance: is the process in which number accomplished tasks or resources on the
demand of user in the network are known as performance (Eusuff and Lansey 2003).
(
i ∗ P(cl)
Perfromance = Task(i) (3)
R
where I denote the instruction P(cl) represent the performance and R exestuation time.
Priority: is the process in which a tasks or job are given priority for exestuation because
it has demand for user site or it need in the network or for different process it need this is
called priority.
∑
Pr iorty =
Task(i)
(Exe(Time + Capacity number of request (4)

Reliability: is the ability of complete takes in the network with the given time and it pro-
vide an assurance of complete given number of task without avoid or reduce the failure rate
the network this is known as reliability.
∑
Task(i) Exe(Time)
Reliability = (5)
Total Time

Response time: when a user request for data or tasks and it start execution and come out
form the waiting queue this process is known as response time.
∑
Task(i) Exe(Time)
Responcetime = (6)
Total(Time)

where SubTime present the submission and StartingTime present the starting time.
Availability: is the committable operation in which cloud provider will able to user
demand or cloud resources are able when user request for specific operation. Availability
is the combation of security, accessibility and serviceability in cloud computing (Taylor
2013).
∑ Mt(ns)
Aviabilty = (7)
Mt(ns) + Mt(hr)

where Mt(ns) is present response time and Mt(ns) is mean time of repair time of resource i.

13
2550 A. Ullah et al.

Bandwidth: is the process which present the maximum data transfer rate in the network
this process present the network connection in given number of time or it present the speed
of the network in the cloud computing.
( )
∑ Size
BW = Resource(i) (8)
Capacity

Cost: is the amounts which we spent for the usage of resource in cloud computing and
different cost are there for cloud provider and different user. These costs depend on the
resource and usage.
∑
Cost Total = resource(i) (Ci ∗ Ti ) (9)

where CI present the cast of resource and I present the unit time and Ti is the utilization of
time.
Energy: is the strength of energy which required for cloud data center on that based
cloud computing start working and different performance are performed based on these
energy. Cloud data center used different kind of energy system.
Finish time

∫
∑
Energy Tool = Resource(i) Ei (FT) (10)
Start time

where Ei represent the energy is consumed by the different resource and I present the start-
ing and finished time. Throughput: is the process in which total number of tasks are exe-
cute successfully within given time period in cloud data center is known as throughput.
∑
Throughtput = Task(i) (ExeTime ) (11)

Workload: present the ability of data center or processor work is known as workload for
calculating of world load in cloud data center.
( )
MaxTask ExeTime) − MinTaskExeTime)
Degree of imbalance = ( ) (12)
AvgTask(i) ExeTime

Utilization: presents the total amount of resource which is actually consumed in data
center during network execution. The main objective is that we reduce this utilization of
resource and improve the network life time. (Neghabi et al. 2020; Johansson et al. 2004).
∑ � �
Task(i) ExeTime
Utilization = � � (13)
MaxTask (i) ExTime

7 Simulation environment

Cloud computing is getting widely attention due to its dynamic nature and flexibility. Due
to these property organizations and researcher are taking an interest in this technology.
And implement by evaluating different data for different experimental purpose. Hence it
becomes very expensive in real-time simulation therefore different simulation tools are

13
Recent advancement in VM task allocation system for cloud… 2551

used. Figure 14 shows the different simulation software which are used in cloud computing
for different simulation purpose.
Figure 14 shows the different simulation tool which I found during the study of differ-
ent paper but most of the researcher used general cloud modeling approach and data center
providing approach software tools in their simulation processes.

8 Result and discussion

In this section we present the summary of 106 papers which are used for load bal-
ancing purpose for resource and task from 2015 and 2021. The summary of these
paper consist of technique name, method, year, advantage, disadvantage and ref-
erences. Table 4 shows the summary of those papers which address the prob-
lems and approaches that are used to solve those issues taking different parameter.
Table 4 present the summary of 106 papers from 2015 to 2021 during the summariza-
tion steps it seem that most of the research work took place 2017 and 2020 because more
number of paper published these two year as mention in Fig. 17. After the study of those
papers it seems that most of the research focuses on to improve the makespan and request
property level. Figure 15 show the number of parameter that different researcher try to
improve these parameter by design their own method and techniques.
Figure 15 present the parameter name and how may time different research try to
improve theses parameter. According to the result more of the researches focus on makes-
pan, power consumption, QoS, response time. After the study of these papers it seems few
parameters are not focusing properly they need more time and discussion. Table 4 presents
the summary of those sections of VM where it needs change or where it section modified
these all are mention in Table 5.
Table 5 show those section of VM where the modification took place with the help of
which algorithm after the improvement of those section which parameter improve all these
information mention in Table 5. Figure 16 shows those sections which are modified by dif-
ferent research for improvement in VM task allocation system for cloud computing.
Figure 16 present those section which are modified by different research during the
work form the result most number of researcher work architecture section then, service
level, VM migration section as so on but few researcher focus on VM replacements system.
Figure 17 present the total number of paper published per year wise.
Figure 17 show the total number of downloaded from 2015 to 2021 must number paper
used in this paper was 2017 and 2020.It means load balancing approach technique most
paper published these two year. Figure 18 show those techniques which are mention by less
number of researches and these techniques are important for future research.
Figure 18 show those sections of cloud computing which need more attentions like
behavior of system, VM replacement policy, and future load prediction 5G network con-
nection, integration with other technology load section, attacker policy and auto scaling.
These all section can be improve with the help of load balancing approach but in this
review paper we find few number of researcher attempt to solve these issues. These are
important and future research section therefore researcher need attentions to this section in
the coming time.

13
2552 A. Ullah et al.

9 Summary

Cloud datacenter consist of different physical and virtual resource as we know that hard-
ware resources configurations are remain the same darning communication. Where when
user request for resources in cloud datacenter for this process service broker policy are
used. Different algorithm work under these servicer broker policies in cloud datacenter and
they provide better result. Applying predication algorithm for cloud computing are recent
study where different researcher applying different machine learning algorithm (ML) used
for forecasting. One of the main approaches they are using is VM migration approach
and adapted for the cloud DC. Where applying forecasting in VM migration improve the
data classification therefore it improve the load banning approaches. Different researcher
implement ML algorithm for predication like CPU, RM, and load balancing approach in
cloud data and provide better result. ML Algorithm also applied in VM section as local
user agent for predication approach these approach was help full in the load predication
in different area like CPU, RM and storage. For improvement in VM resource allocation
host load detection method used in cloud datacenter due to this approach the performance
of cloud computing improve as infrastructures purpose different researcher work on this
section. Researcher also work on VM selection policy as well as service level agreement
(SLA) these all activity are performed for improvement in cloud computing.

9.1 Analysis

According to the literature, we consider that most of the problems of VM task alloca-
tion system for cloud computing are solved using ML algorithm and different models.
For improvement in cloud computing different section of VM are improved like resource
allocation, data distribution, quality of services, VM migration, and VM replacement and
server broker policy. For the improvement of above parameters different technique are used
and load balancing is one of them. So taking load balancing approaches 70% VM migra-
tion section improve 20% server broker policy are improved and 10% resource allocation
are improved in the give related work.

Fig. 14  Different simulation tools

13
 Table 4  Summary of selected paper
Technique Problems addressed Improvements Weakness References

ACO-VMM-Algorithm Overload section in VM Local migration agent Need improvement in Energy section Wen et al. (2015)
VM-Allocation Theory Minimize attacker’ possibility VM allocation policies Not improve the mixture policy Han (2015)
Bee-Colony-Algorithm Load balancing Priority rule used for minimizes the Energy, cost Zuo et al. (2015)
total processing time
Skewers-Algorithm Future load prediction Energy consumption Utilization, Waiting time Balouek-Thomert (2016)
VM Migration Algorithms Examine the migration times of VMs Throughput Energy Chowdhury et al. (2015)
STVMLB Algorithm Load balancing technique Quality of service, substantially Utilization Tyagi and Kumar (2015)
MQLB-RAM Algorithm Overall system efficiency QoS, cost, system and network Accuracy Othman et al. (2015)
Honey Proposer distinction of Virtual Makespan, degree of imbalance, QoS Abdulhamid et al. (2015)
Bee Galvanizing Algorithm Machine resource utilization
GA-GEL-algorithm Distribution of dynamic workload QoS, minimizing the make span Energy Dam et al. (2015)
EAMLB- Algorithm Behavior of system Response time, makespan Utilization Abdulhamid et al. (2015)
Hybrid Approach Load balancing technique Stability, resource utilization QoS Gao and Wu (2015)
DWOLB-Algorithm Service level agreement Energy consumption Need Improvement In accuracy Monil and Rahman (2016)
Recent advancement in VM task allocation system for cloud…

DLBPR- Algorithm Load Balancing technique Waiting time, resource utilization, Energy Zhou et al. (2016)
throughput
EFOALB- Algorithm VM section Energy QoS Rajput and Kushwah (2016)
LBMM-Algorithm Task scheduling Algorithm Makespan, utilization QoS Cassidy (2016)
Genetic-Algorithm
Prediction based proactive load VM migration VM migrations, execution time Utilization Renugadevi and Mala (2015)
balancing approach
TVRSM-Model Service LevelAgreement Energy consumption, VM allocation QoS Bao et al. (2016)
RPQ—Algorithm Services Response time, request priority Utilization Bozakov (2016)
LBA_HB- Algorithm virtual machine allocation Execution time, response time, QoS Abdulhamid et al. (2016)
makespan
Least cost per Connection Algorithm VM load balancing Quality of service Utilization Khan and Ahmad (2017)
SDN-Algorithm VM migration Resources utilization QoS Madni et al. (2017a, b)
EBC- Algorithm Task scheduling QoS, makespan Accuracy
IPSO –Algorithm NP problem scheduling Makespan, response time Utilization Dinh et al. (2017)
2553

13
 Table 4  (continued)
2554

Technique Problems addressed Improvements Weakness References

LBDA- Algorithm VM scheduling Makespan, response time, execution QoS Chen et al. (2017)

13
time
LBA-Algorithm VM load balancing Makespan, horizontal scalability, QoS Carrión et al. (2017)
average resource utilization ratio
LB-ACO-Algorithm Load balancing NP-problem Makespan Utilization Subramanian and Abdulrahman (2017)
ACO-Algorithm Task scheduling strategy Makespan, total costs Utilization Subramanian and Abdulrahman (2017)
Service level Agreement VM placement policy Energy consumption Utilization Mevada et al. (2017)
RR—Algorithm Architecture Waiting time, response time, resource QoS Elmougy et al. (2017)
Hybrid GA-PSO Algorithm Workflow technology Makespan execution cost QoS Zhou and Yao (2017)
SVLL-Algorithm Distributing tasks Waiting time, total finish time QoS Vargas, (2017)
Generic- Algorithm VM scheduling Data allocation Utilization Eswaraprasad and Raja (2017)
RM-Algorithm VM scheduling Execution time QoS Guo and Xue (2017)
VM Placement technique VM Replacement Lifetime Creates fragments Macias et al. (2017)
Placement of the VM Taxonomy Available resources, power Utilization Kaur et al. (2017a, b)
Hybrid HBB-LB-Algorithm Resource allocation Task cost, Speed, Energy consump- QoS Balusamy et al. (2017)
tion
Enhanced Throttled Load Balancing Scheduling Response time, data Processing time, QoS Ghomi, et al. (2017)
cost analysis
EMM-Algorithm Resources scheduling Makespan, cost Utilization Kaur et al. (2017)
Dynamic Threshold algorithm CPU utilization Resource utilization and time QoS Fard et al. (2017a, b)
Genetic-Algorithm Load balancing NP Response time QoS Roy (2017)
PFTF-Algorithm Load balancing Fault tolerance, virtual machine QoS Musumeci et al. (2017)
migration
Naive Bayesian classifier Task scheduling Makespan utilization Ebadifard and Babamir (2017)
VM-Placement Service level agreement Utilized efficiently Accuracy Addya et al. (2017)
Hybrid technique Load balancing Response time, Machine cost QoS Kaur et al. (2017)
EG-Algorithm VM scheduling Makespan utilization Anjum and Patil (2017a, b)
THR_MUG-Algorithm VM scheduling Reduce the number of VM migra- Utilization Wu et al. (2017)
tions, energy consumption
A. Ullah et al.
 Table 4  (continued)
Technique Problems addressed Improvements Weakness References

BFB- Algorithm VM Allocation Energy consumption QoS Bhatti (2017)

Fuzzy based Policy Brokerage strategy Service broker policy Utilization Islam and Waheed (2017)
SLA-Algorithm Service level agreements QoS Utilization Nawaz et al. (2018)
LB-ACO-Algorithm Load balancing Makespan QoS Belgacem et al. (2018)
WQBLB- Algorithm 5G networks Queue size, total service QoS Dighriri et al. (2018)
Graph-based Mathematical Fault tolerance Utilization of resources Utilization Babu (2018)
model,
TMA-Algorithm VM Load Balancer section Response times, processing time Utilization Kotsubanska and Sokolovska (2018)
Hybrid BLB- PSOGSA-Algorithm Scheduling Average VM processing speed, VM Dependent tasks Manasrah and Ba Ali (2018)
processing power
Hybrid PSO-SA-Algorithm VM scheduling Response time, Processing time QoS Zhu et al. (2018)
and cost
Adaptive Energy-Aware Algorithms Service-level agreements Reduce energy consumption Utilization Yadav et al. (2018)
BCO-algorithm Iteration process Makespan, imbalance degree Utilization Belgacem et al. (2018)
IMDLB- Algorithm VM Scheduling QoS Utilization Afzal and Kavitha (2018)
Recent advancement in VM task allocation system for cloud…

Hybrid technique Service broker policies Response time, response time Utilization Yasmeen et al. (2018)
WAMLB- Algorithm Load balancing Weight factor and assignment section Utilization Singh and Prakash (2018)
Graph theoretic Load monitoring Migration cost, time taken for VM QoS Devi et al. (2018)
migrations
Throttled Algorithm VM scheduling Overall response time, request servic- Utilization Ramadhan et al. (2018)
ing, data center loading
Fuzzy Load Balancer Load balancer section Faull tolerance Utilization Rathore (2018)
ICT VM Makespan QoS Nasr et al. (2018)
DSP-Policy Integration of cloud and fog Response time, requests servicing Utilization Fatima et al. (2018)
time,
SCLBA) VM migration CPU utilization QoS Ramesh and Dey (2018)
Hybrid VM Migration technique Pre-copy Quality of service Utilization Anu and Elizabeth (2019)
Multidimensional Queuing Load VM Resource scheduling efficiency QoS Priya et al. (2019)
Optimization algorithm
2555

13
 Table 4  (continued)
2556

Technique Problems addressed Improvements Weakness References

OPH-LB-Algorithm VM Utilization of resources, throughput, Utilization Lv et al. (2019)

13
makespan
DVFS-Algorithm VM Utilization rates of server Utilization Abro et al. (2019)
MMSIA- algorithm Architecture Utilization QoS Safitri et al. (2019)
ABC- algorithm Resource utilization Makespan QoS Thanka et al. (2019)
ETLB-algorithm VM scheduling Response Time Utilization Al-Rahayfeh et al. (2019)
Hybrid ABPS-Algorithm Scheduling Makespan, cost outperforms Utilization Tamiminia et al. (2020)
MEMA-Technique VM dataallocation Security,quality of service, acces- Accuracy Dibaj et al. (2020a)
sibility
ALD—algorithm Utilization of auto scaling Quality of service Energy Rajput and Goyal (2020)
SJF-QMW- Algorithm VM scheduling Throughput, hosting ratio QoS Zhang and Abnoosian(2020}
MEMA-Technique Brokerage strategy Security, capacity, quality of service, Utilization Dibaj (2020b)
cost
Learning agent Machine placement Execution time, Number of HMs Energy and delay Ghasemi and Haghighat (2020)
shutdown
MD-Algorithm VM placement Resource wastage, placement time Energy and delay Singh and Auluck (2020)
MPSO-Algorithm Fitness function/pre-emptive Makespan, Utilization, Waiting Agarwal et al. (2020)
VM
MCCVA-Algorithm Load balancing technique QoS, makespan Utilization, Waiting Ranjan et al. (2020)
Federate Migration Based Load Balancing Scheme VM Migration, VM distribution com- Efficiency Najm and Tamarapalli(2020
munication cost
COA-Algorithm Minimum Migration Time policy Reducing energy, resource utilization QOS Ahmad et al. (2020)
in VM
DLBA-Algorithm Hypervisor scheduling controls Makespan QOS Khan et al. (2020)
GWO—Algorithm Load balancing Makespan Energy Patel et al. (2020)
LBPR-Algorithm Load balancing System performance QOS Wang et al. (2020)
EBFD- Policy VM Placement Policy Total execution time, Decrease the Utilization, Energy Dubey et al. (2020)
VM placement failure rate
A. Ullah et al.
 Table 4  (continued)
Technique Problems addressed Improvements Weakness References

CBLB-Algorithm Homogeneous, Heterogeneous Make span, throughput, CPU utiliza- Energy Dubey et al. (2020)
scheduling tion
CDCSO-Algorithm VM scheduling Energy, cost, time and optimal load QoS Godman et al. (2020)
balancing
Simulated annealing Two phases to Quality of service task allocation Utilization Hanine and Benlahmar (2020)
balance the workload between the
VMs
D2B_CPU Based Degree Balanced with CPU based QoS, reduces degree of imbalance, Energy Joshi and Munisamy (2020)
VM allocation Waiting time of task
Balls into Bins VM replacement Distributed of work Energy Si et al. (2020)
AI-Algorithm Resource allocation Quality of service, Platform for Energy Lin et al. (2020)
PowerDispatching
VM Migration policy VM section QoS, minimal migration cost Energy Moghaddam et al. (2020)
DLB- Algorithm VM section/server machine life-time, power consump- Energy Renugadevi and Mala (2020)
tion
LBOS-Algorithm Resource utilization Calculating weight procedure, Energy QoS Fernande et al. (2020)
Recent advancement in VM task allocation system for cloud…

Framework VM Replacement Energy consumed, VM migration Utilization Xu et al. (2020)

MILP- Model Virtual Machines Placement Power consumption, CPU Utilization, QoS Alharbi et al. (2020)
power savings
HMRR-Algorithm Task scheduling Execution time, Makespan Response QoS Behrens et al. (2020)
time
SJF-QMW-algorithm VM scheduling Throughput, hosting ratio QoS Mallikarjuna(2020)
DLBA-Algorithm VM Makespan QoS Khorsand and Ramezanpour (2020)
Tailoring of VM Size Active physical servers Energy-efficient QoS Hsieh et al. (2020)
Geometric Programming algorithm Pre-copy migration strategy Multi-VM migration Educed energy consumption Singh and Singh (2021)
QoS-DPSO algorithm QoS scheduling model QoS Time consumption Jing et al. (2021)
ML algorithm Resource predication C.P.U.RAM Error size improve Jo and Yoo (2021)
2557

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2558 A. Ullah et al.

9.2 Limitations

Although the use of different machine learning algorithm and other frame work model
improve different section of VM for cloud computing but still limitations remain con-
straints which are as:

i. Still need improvement in the services policies.

ii. Still need improvement in section gent for predication.
iii. Need to focus more on predication technique.
iv. Need more focus on resource scheduling.
v. Need more focus on live VM migration and VM replacement section.
vi. Need improvement towards a cloud migration framework.
vii. Need improvement toward a trusted framework for cloud computing

Request priority; 9 Makespan; 12

Overall response
time; 7
Response time; 6
Cost; 3

Power consumption; QoS; 5

7

Waiting Time, Total

finish time; 7
Security; 5

Throughput, hosting
Energy consumption;
ratio; 8
7
Utilization of auto
scaling; 1 Fault tolerance; 3

Utilization rates of Response time,

server hosts; 6 CPU utilization; 8 Requests; 9

Fig. 15  Different researcher improve these parameters

13
 Table 5  Result selection
Technique Parameter Section Year

ACO-VMM QoS, Number of Migrations, Load Condition Variance VM/Live migration 2015
VM Allocation Theory Minimize the efficiency,Safe rule from attacker VM allocation policies 2015
Bee-Colony algorithm QoS, Minimizes the total processing cost VM/fitness value 2015
Skewers Algorithm Energy consumption Future load prediction in VM 2015
VMM-Algorithms Throughput Examine the migration times of VMs 2015
STVMLB algorithm Quality of Service, substantially VM/load balancing 2015
MQLB-RAM algorithm QoS, Cost, System and network Over all network 2015
HBA-Algorithm Makespan, Degree of Imbalance, Resource Utilization Proposer distinction of VM 2015
GA-GEL-Algorithm QoS, minimizing the makespan Distribution of workload 2015
EAMLB-Algorithm Response time, Makespan Behavior of system 2015
Hybrid Approach Stability, Resource Utilization Load balancing 2015
DWOLB- Algorithm Energy VM/Migration 2016
EFOALB-Algorithm Energy VM section 2016
Recent advancement in VM task allocation system for cloud…

LBMM-Algorithm Makespan, Utilization Task scheduling algorithm 2016

Genetic-Algorithm
RPQ-Algorithm Services Response time, Request priority 2016
LBA_HB- Algorithm VM allocation Execution time, Response time 2016
LCPC- Algorithm VM load balancing Quality of Service 2016
SDN-Algorithm VM migration Resources utilization 2016
EBC-Algorithm QoS Makespan Task scheduling 2016
IPSO-Algorithm Makespan, Response time VM 2017
LBDA- Algorithm Makespan, Response Time, Execution time VM section 2017
LBA- Algorithm Makespan time, Horizontal scalability, Average resource utiliza- VM section 2017
tion ratio
LB-ACO-Algorithm Makespan Load balancing NP-complete problem 2017
ACO-Algorithm Makespan, Total costs Task scheduling strategy 2017
2559

Service level Agreement Energy consumption VM placement policy 2017

13
 Table 5  (continued)
2560

Technique Parameter Section Year

13
RR- Algorithm Waiting time, Response Time, resource Architecture 2017
HybridGA-PSO Algorithm Makespan Workflow technology 2017
Execution cost,
SVLL-Algorithm Distributing tasks Waiting Time, Total finish time 2017
Generic algorithm Data allocation VM scheduling 2017
RM-Algorithm VM scheduling Execution time 2017
VM Placement technique Creates fragments lifetime 2017
Placement of the VM Available resources, power Taxonomy 2017
Hybrid HBB-LB-A Algorithm Task cost, Speed, Energy consumption Resource allocation 2017
Enhanced Throttled Load Balancing Response time, Data Processing time, Cost analysis Scheduling 2017
EMM- Algorithm Makespan, cost Resources scheduling 2017
DT-Algorithm Resource utilization and time CPU utilization 2017
Genetic- Algorithm Response time Load balancing NP 2017
PFTF- Algorithm Fault tolerance, Virtual machine · Migration Load balancing 2017
Naive Bayesian classifier Makespan Task scheduling 2017
VM Placement Utilized efficiently Service level agreement 2017
Hybrid Approach Load balancing Machine cost, Response cost 2017
EG-Algorithm VM Make span 2017
THR_MUG-Algorithm VM scheduling Reduce the number of VM migrations, Energy consumption 2017
BFB-Algorithm VM Allocation Energy consumption 2017
Fuzzy based Policy Brokerage strategy Service broker policy 2017
SLA-Algorithm Service level agreements QoS, Consumption 2017
LB-ACO-Algorithm Load balancing Makespan 2017
LBWQB-Algorithm 5G networks Queue size, Total Service 2017
DBM- Model Utilization of resources Fault tolerance 2017
TMA—Algorithm Response times, processing time VM Load balancer section 2018
A. Ullah et al.
 Table 5  (continued)
Technique Parameter Section Year

Hybrid LB- LB-PSOGSA-Algorithm Average VM processing speed, VM processing power Balancing scheduling 2018
Hybrid PSO-SA-Algorithm Response time, Processing time and cost VM scheduling 2018
Adaptive Energy-Aware Algorithms Reduce energy consumption service-level agreements 2018
BCO-Algorithm Makespan, imbalance degree Iteration process 2018
IMDLB- Algorithm QoS VM scheduling 2018
Hybrid technique Response time Service broker policies 2018
WAMLB- Algorithm Weight factor and assignmentsection Load balancing 2018
Graph theoretic Load monitoring Migration cost, Time taken for VM migrations 2018
Throttled Algorithm VM scheduling Overall response time, Request Servicing, Data center loading 2018
Fuzzy Load Balancer VM Allocation Fault tolerance 2018
ICT Load balancing Makespan 2018
DSP-Policy Integration of cloud and fog Response time, Requests 2018
Servicing time
Recent advancement in VM task allocation system for cloud…

SCLBA- Algorithm VM migration CPU utilization 2018

Hybrid VM Migration technique Pre-copy Quality of service 2018
Multidimensional Queuing Load VM Resource scheduling efficiency 2018
Optimization Algorithm
OPH-LB-Algorithm VM Utilization of resources, Throughput, 2018
DVFS- Algorithm VM Utilization rates of server hosts 2018
MMSIA- Algorithm Utilization, Architecture 2019
ABC- algorithm Makespan Resource utilization 2019
ETLB-Algorithm Response Time VM scheduling 2019
Hybrid –ABPS-Algorithm Makespan, cost outperforms Scheduling 2019
MEMA-Technique Security, Quality of service, Accessibility VM data allocation 2019
ALD-Algorithm Quality of Service Utilization of auto scaling 2019
SJF-QMW-Algorithm VM scheduling Throughput, hosting ratio 2019
2561

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 Table 5  (continued)
2562

Technique Parameter Section Year

13
MEMA- Technique Brokerage strategy Security, Capacity, quality of service, cost 2019
learning Agent Execution time, Number of HMs shutdown, Immigrations cost VM replacement 2020
GWO- Algorithm Makespan VM load balancing 2020
LBPR-Algorithm System performance System Load Balancing 2020
EBFD-Policy Total execution time, decrease the VM placement failure rate VM Replacement 2020
CBLB-Algorithm Make span, Throughput, Homogeneous, Heterogeneous scheduling 2020
CPU utilization
CDCSO- algorithm VM utilization Energy Cost time and optimal load balancing 2020
Simulated Annealing Quality of service two phases to 2020
task allocation balance the workload between the VMs
D2B_CPU Based QoS, reduces degree of imbalance, waiting time of task Degree Balanced with CPU based VM allocation 2020
Balls into Bins Distributed of work VM Replacement 2020
AI-Algorithm Quality of service, Platform for Power Dispatching Resource Allocation 2020
DLB-Algorithm Machine life-time, power consumption VM Section/Server 2020
LBOS-Algorithm Calculating weight procedure, VM Section/Server 2020
Framework Energy consumed, VM migration VM Replacement 2020
MILP- Model VM, Replacement Power consumption, CPU utilization, Power savings 2020
HMMRR-Algorithm Execution time, Makespan response time Task scheduling 2020
SJF-QMW- Algorithm VM scheduling Throughput, Hosting ratio 2020
DLBA-Algorithm VM Makespan 2020
Tailoring of VM Size Active physical servers Energy-efficient 2020
MD-Algorithm Resource wastage Placement Time/pre-emptive VM 2020
MPSO-Algorithm Fitness function/pre-emptive Pre-emptive 2020
VM VM
MCCVA-Algorithm QoS, Makespan VM Section 2020
Federate Migration Based VM replacement VM Migration, VM Distribution Communication Cost 2020
A. Ullah et al.
 Table 5  (continued)
Technique Parameter Section Year

COA- Algorithm Reducing Energy, Resource utilization Migration Time policy in VM 2020
DLBA-Algorithm Makespan Balances the load in VM 2020
HMRR-Algorithm Task scheduling Execution time, Makespan Response time 2020
SJF-QMW-Algorithm VM scheduling Throughput, Hosting ratio 2020
DLBA-Algorithm VM Makespan 2020
Tailoring of VM Size Active physical servers Energy-Efficient 2020
Geometric Programming algorithm Pre-copy migration strategy Multi-VM migration 2021
QoS-DPSO algorithm QoS scheduling model QoS 2021
ML algorithm Resource predication C.P.U.RAM 2021
Recent advancement in VM task allocation system for cloud…
2563

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2564 A. Ullah et al.

Fig. 16  Ratio of different parameter improved

Fig. 17  Paper published per year

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Recent advancement in VM task allocation system for cloud… 2565

Fig. 18  Suggestions for future research

10 Conclusion

Task and resource distribution for VM is prime changing task in cloud computing therefore
it getting more attention for researchers. This review paper present a state of the art survey
about the technique, parameter, rule and specification that are used for load balancing in
VM for cloud datacenter. As we know that different parameter are used to check the perfor-
mance of load balancing approach in cloud data center. For improvement of these param-
eter different kind loads balancing policy and method are used in different section of VM’s.
All those technique and parameter are discussed which are used in different research paper
and in future research direction. We mention those issue which are not given time by many
research and some of them very important because cloud technology now merge with mod-
ern technology like IoT, senior network and 5G therefore now it time we work more to
those issue which we face due to technology integration and energy. This review paper pro-
vide information to those researcher who want to start working in cloud computing.

Authors’ contributions It Collaboration of all author and authors read and approved the final manuscript.

Declarations
Competing interest The authors declare that they have no competing interests.

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