Present the classification and characteristics of virtual machines

Virtual machines (VMs) are software-based emulations of physical computers that run operating
systems and applications. They enable multiple operating systems and applications to run on a
single physical host, providing flexibility, resource isolation, and efficient utilization of hardware
resources. VMs are a foundational technology in cloud computing and virtualization. Here are
the classifications and characteristics of virtual machines:
Classification of Virtual Machines:
 Full Virtualization: In full virtualization, VMs run on a hypervisor, which emulates the
entire hardware environment, including CPU, memory, storage, and peripherals. Each
VM operates as an independent and isolated instance, unaware of other VMs running on
the same physical host.
 Para-virtualization: Para-virtualization is a variation of full virtualization in which the
guest operating system is modified to be aware of the virtualization layer. This allows
better performance and efficiency compared to full virtualization but requires guest OS
modifications.
 Hardware-assisted Virtualization: Hardware-assisted virtualization relies on hardware
features provided by modern CPUs (such as Intel VT-x or AMD-V) to enhance
virtualization performance. It improves the efficiency of VM operations, reducing the
overhead of the hypervisor.

Characteristics of Virtual Machines:

 Isolation: VMs provide strong isolation between different instances. Each VM operates
independently, with its own dedicated resources, ensuring that failures or issues in one
VM do not affect others.
 Resource Allocation and Management: VMs enable flexible resource allocation, allowing
users to assign specific amounts of CPU, memory, storage, and network resources to each
VM based on its requirements.
 Mobility and Portability: VMs can be easily moved or migrated between physical hosts
without significant downtime. This mobility and portability enhance scalability, load
balancing, and disaster recovery capabilities.
 Snapshot and Cloning: VMs support snapshotting, allowing users to capture the current
state of a VM at a specific point in time. Cloning enables the creation of identical copies
of VMs for testing or backup purposes.
 Hardware Independence: VMs are hardware-independent, which means applications and
operating systems within a VM are not tied to specific physical hardware. This allows
easy migration between different hardware platforms.
  Flexibility and Multi-Tenancy: VMs provide a flexible and efficient way to create
multiple virtual environments on a single physical host, making them ideal for multi-
tenancy scenarios in cloud computing.
 Disaster Recovery and Fault Tolerance: VM snapshots and the ability to move VMs
between hosts facilitate disaster recovery and fault tolerance strategies, enabling quick
recovery from system failures.
 Cost Savings and Resource Utilization: VMs optimize hardware resource utilization by
consolidating multiple workloads on a single physical host. This leads to cost savings in
terms of hardware and energy consumption.
Overall, virtual machines offer a powerful solution for creating, managing, and scaling
computing resources efficiently in both on-premises data centers and cloud environments. Their
versatility and isolation capabilities make them a cornerstone technology in modern IT
infrastructures.
Explain the architecture and virtualization strategies of Xen
Xen is an open-source virtualization platform that provides powerful virtualization capabilities
for running multiple guest operating systems on a single physical host. It was originally
developed by the University of Cambridge and is now maintained by the Linux Foundation.
Xen's architecture and virtualization strategies offer high performance, strong isolation, and
efficient resource utilization. Here's an overview of Xen's architecture and virtualization
strategies:
Xen Architecture:
The Xen architecture consists of the following key components:
a. Xen Hypervisor: The Xen hypervisor is the core component responsible for managing and
controlling the virtualization layer. It runs directly on the physical hardware and provides an
interface to create and manage multiple virtual machines (VMs). The hypervisor is lightweight
and runs in privileged mode, allowing it to directly access and manage hardware resources.
b. Dom0 (Domain 0): Domain 0 is a special VM that acts as the management domain. It runs a
privileged operating system (usually a modified Linux distribution) and has direct access to
hardware resources, such as network interfaces and storage controllers. Dom0 is responsible for
managing other guest VMs, loading their drivers, and providing I/O services.
c. Unprivileged Domains (DomU): Unprivileged domains, also known as DomU, are the guest
VMs running on the Xen hypervisor. These VMs do not have direct access to hardware resources
and rely on Dom0 for I/O operations and access to hardware devices.
Virtualization Strategies:
Xen employs two main virtualization strategies to achieve high performance and efficient
resource utilization:
a. Paravirtualization: In paravirtualization, the guest operating systems are modified to be aware
of the virtualization environment provided by Xen. The paravirtualized guest OSes use
hypercalls (a special form of system calls) to communicate directly with the Xen hypervisor for
privileged operations, such as memory management and I/O.
Paravirtualization eliminates the need for binary translation of privileged instructions, resulting
in lower overhead and improved performance compared to full virtualization. However, it
requires modifications to the guest OS, which may limit its compatibility with certain operating
systems.
b. Hardware-Assisted Virtualization (HVM): Xen also supports hardware-assisted virtualization,
which allows unmodified guest operating systems to run on the Xen hypervisor. This approach
utilizes the hardware virtualization extensions, such as Intel VT-x and AMD-V, to provide
virtualization support at the hardware level.
HVM allows running guest OSes without modification, making it more compatible with various
operating systems. It achieves near-native performance but may introduce slightly higher
overhead compared to
paravirtualization.
Xen supports both paravirtualized
VMs and HVM VMs, providing
flexibility to choose the appropriate
virtualization strategy based on
specific requirements and workloads.
Overall, Xen's architecture and
virtualization strategies make it a
powerful and efficient virtualization
platform, well-suited for data centers
and cloud environments. Its strong
isolation, high performance, and
support for various virtualization
modes make it a popular choice for
virtualizing workloads in diverse
computing environments.
Write about five classes of cloud resource management policies
Policies and mechanisms for resource management
A policy generally refers to the principal guiding decisions, whereas mechanisms represent the
means to implement policies. Cloud resource management policies can be loosely grouped into
five classes:
1. Admission control.
2. Capacity allocation.
3. Load balancing.
4. Energy optimization.
5. Quality-of-service (QoS) guarantees.
1. Admission control: prevent the system from accepting workload in violation of high-level
system policies. For example, a system may not accept an additional workload that would
prevent it from completing work already in progress or contracted.
2. Capacity allocation: allocate resources for individual activations of a service. Loading
resources subjected to multiple global optimization constraints require a search of very large
search space when the state of individual systems change rapidly.
3. Load balancing: distribute the workload evenly among the servers. Resources are properly
utilized and overload on some servers is drastically reduced.
4. Energy optimization: minimization of energy consumption. In cloud computing a critical goal
is minimizing the cost of service and in particular minimizing the energy consumption.
5. Quality of service (QoS) guarantees: ability to satisfy timing or other conditions specified by a
Service Level Agreement.
Discuss about feedback control based on dynamic thresholds
Feedback control based on dynamic thresholds is a management approach used in various
systems, including cloud resource management, network traffic control, and performance
monitoring. In this approach, thresholds, which are pre-defined values that trigger specific
actions, are dynamically adjusted based on real-time feedback and system conditions. By
continuously adapting these thresholds, the system can efficiently respond to changing
workloads and optimize resource allocation and performance.
Here's how feedback control based on dynamic thresholds typically works:
  Monitoring and Feedback Loop: The system continuously monitors relevant metrics and
performance indicators, such as CPU utilization, memory usage, network traffic, response
times, or application-specific metrics. These measurements act as feedback for the
control loop. The feedback loop analyzes the real-time data to understand the current
state of the system.
 Threshold Evaluation: Based on the feedback received from the monitoring process, the
control system evaluates the current performance against predefined thresholds. These
thresholds can be set as static values or can be calculated based on historical data,
machine learning algorithms, or other predictive techniques. The thresholds serve as
decision points to determine if specific actions need to be taken.
 Dynamic Threshold Adjustment: Instead of using fixed, static threshold values, the
feedback control system adjusts the thresholds dynamically. This adjustment takes into
account the changing workload patterns, resource availability, and other environmental
factors. By adapting thresholds in real-time, the system becomes more responsive to
fluctuations and can make more accurate decisions.
 Action Execution: When the monitored metrics cross the dynamically adjusted
thresholds, the control system triggers predefined actions. These actions can include
scaling resources up or down, reallocating resources, invoking auto-scaling mechanisms,
or performing other automated tasks to maintain the desired performance and efficiency
levels.
 Continuous Loop: Feedback control based on dynamic thresholds is an iterative process.
The system continually monitors, evaluates, adjusts thresholds, and executes actions as
needed. This continuous loop ensures that the system remains adaptable to changing
conditions, varying workloads, and resource demands.
Benefits of Feedback Control with Dynamic Thresholds:
 Adaptability: Dynamic thresholds allow the system to adapt to varying workloads and
changing conditions, providing better responsiveness to real-time demands.
 Efficiency: By adjusting thresholds dynamically, resources are allocated optimally,
preventing underutilization or overprovisioning, which can lead to cost savings and
improved performance.
 Fault Tolerance: Dynamic thresholds can be used to trigger automatic responses to handle
potential failures or performance degradation, enhancing system reliability and
availability.
 Resource Optimization: The control system can make intelligent decisions to optimize
resource usage, such as automatically scaling resources when necessary, leading to better
overall performance.
 Proactive Management: By incorporating feedback from the system, potential issues can
be identified and addressed before they escalate into critical problems.
However, it's essential to design the feedback control system carefully, as overly aggressive or
poorly tuned adjustments in thresholds can lead to excessive resource churn or unstable behavior.
Continuous monitoring, performance analysis, and fine-tuning of the control mechanisms are
necessary to ensure the system functions optimally and aligns with the organization's objectives.
Write about the hardware support in cloud computing with different case studies XEN and
V BLADES
Hardware support in cloud computing refers to the underlying infrastructure and technologies
that enable cloud service providers to deliver scalable, reliable, and efficient cloud services. Two
notable examples of hardware support in cloud computing are the XEN hypervisor and V Blades
(Virtual Blades) architecture. Let's explore each of them with their respective case studies:
XEN Hypervisor:
Overview: XEN is an open-source virtualization technology that allows for the creation and
management of virtual machines (VMs) on a physical host. It is a type 1 hypervisor, meaning it
runs directly on the hardware, making it efficient and well-suited for cloud environments.
Hardware Support: XEN leverages hardware
virtualization extensions provided by
modern processors, such as Intel VT-x and
AMD-V, to enhance virtualization
performance. These extensions enable the
hypervisor to create and manage VMs more
efficiently, reducing the overhead associated
with traditional software-based
virtualization.
Case Study: Amazon EC2: Amazon Elastic
Compute Cloud (EC2) is one of the most
prominent cloud computing services that
rely on XEN as their hypervisor. EC2
provides resizable compute capacity in the
form of virtual instances. Customers can
choose from various instance types optimized for different workloads.
Use Case: A user can request an EC2 instance, specifying the required resources (CPU, memory,
storage, etc.), and XEN's hardware support enables the hypervisor to efficiently create and
manage the requested VM on the underlying physical hardware. Users can also take advantage of
features like live migration, where a running VM can be moved between physical hosts without
downtime, thanks to XEN's hardware virtualization capabilities.
V Blades (Virtual Blades) Architecture:
Overview: V Blades, or Virtual Blades, is an architecture that combines the benefits of
virtualization and blade server technology. Blade servers are modular, high-density computing
devices that can be easily inserted and removed
from a chassis.
Hardware Support: V Blades architecture relies on
blade servers equipped with hardware virtualization
support, similar to the hardware extensions used by
the XEN hypervisor. The combination of blade
servers and virtualization support allows for flexible
resource allocation, rapid provisioning, and easy
management of virtualized environments.
Case Study: HP BladeSystem: HP BladeSystem is
an example of an infrastructure solution that
implements the V Blades architecture. It offers a
shared blade server infrastructure that can host multiple virtualized workloads.
Use Case: In the HP BladeSystem environment, multiple blade servers with hardware
virtualization support can be inserted into a common blade chassis. The chassis provides shared
resources, such as power, cooling, and networking. Through the V Blades architecture, virtual
machines can be efficiently provisioned and managed on the blade servers, allowing for
streamlined scaling and resource allocation based on workload demands.
Both XEN and V Blades exemplify how hardware support in cloud computing is vital for
enabling efficient virtualization, resource management, and scalability. These technologies have
played a significant role in shaping the cloud computing landscape and facilitating the delivery
of diverse cloud services to users worldwide.
How to achieve scheduling with deadlines? Explain the scheduling map reduce applications
Achieving scheduling with deadlines is crucial in real-time systems and applications where tasks
must be completed within specific time constraints. Here are the key steps to achieve scheduling
with deadlines:
 Task Profiling: Understand the characteristics and requirements of each task or job in the
application. This
includes determining
the task's execution
time, its deadline,
and any
dependencies it may
have on other tasks.
 Deadline Analysis:
Analyze the
deadlines of all tasks
to ensure that the
overall system can
meet the critical
timing requirements.
Verify that the deadlines are feasible and that there is enough time available for each task
to execute without violating any constraints.
 Priority Assignment: Assign priorities to tasks based on their deadlines and criticality.
Tasks with closer deadlines and higher criticality should have higher priority. Priority
assignment ensures that critical tasks are executed on time.
 Scheduling Algorithm Selection: Choose an appropriate scheduling algorithm that
considers task deadlines. Real-time operating systems often employ priority-based
scheduling algorithms like Rate Monotonic Scheduling (RMS) or Earliest Deadline First
(EDF) to meet timing requirements.
 Preemption and Interrupt Handling: In preemptive scheduling, ensure that tasks with
higher priorities can preempt lower priority tasks if necessary. Also, handle any interrupts
or events that may impact the execution of tasks.
 Resource Allocation: Plan the allocation of system resources, such as CPU, memory, and
I/O, to ensure that each task gets the required resources to meet its deadline. Over-
allocating resources to some tasks may lead to insufficient resources for others, affecting
overall timing constraints.
 Task Monitoring and Feedback: Continuously monitor the execution of tasks and gather
feedback on actual completion times. Use this feedback to fine-tune the scheduling
algorithm and improve the overall system's performance.
Now, let's discuss scheduling in MapReduce applications:
MapReduce Scheduling: MapReduce is a programming model and processing framework used to
process large-scale data sets in a distributed and parallel manner. Scheduling in MapReduce
applications involves the efficient allocation of tasks across multiple nodes in a cluster to achieve
timely completion of the job.
1. Task Division: In a MapReduce job, the data is divided into multiple input splits, and map
tasks are created to process these splits in parallel. The scheduling framework divides the input
data and distributes the map tasks across available worker nodes in the cluster.
2. Task Scheduling: The scheduler assigns map tasks to available nodes based on various factors,
such as node availability, data locality (preferentially scheduling tasks on nodes that already have
the data), and task priorities. The scheduler also considers the estimated execution time of each
task.
3. Data Locality: Data locality is a crucial aspect of MapReduce scheduling. The framework
attempts to schedule map tasks on nodes where the input data is already present. This reduces
network transfer overhead and improves overall processing efficiency.
4. Reducer Scheduling: Once all the map tasks are completed, the framework schedules reduce
tasks. The reducer tasks aggregate the intermediate results generated by map tasks and produce
the final output.
5. Speculative Execution: MapReduce frameworks often use speculative execution, wherein
slow-running tasks are duplicated and run in parallel on different nodes. The framework takes
the output of the first task that completes, discarding the others. Speculative execution helps
handle slow nodes or tasks, reducing the overall job completion time.
6. Dynamic Resource Allocation: In some modern MapReduce frameworks, dynamic resource
allocation techniques are employed. These approaches allow the system to adjust the allocation
of resources to tasks during job execution based on the changing workload and system
conditions.
By efficiently scheduling map and reduce tasks, MapReduce applications can utilize the cluster's
resources effectively and achieve timely processing of large-scale data sets. Effective scheduling
ensures that deadlines for the completion of tasks and the overall job are met while optimizing
resource utilization and reducing job completion time.
Describe the architecture for two level resource allocations. What is the importance of
resource bundling in it?
The architecture for two-level resource allocations involves the allocation of resources in a
hierarchical manner, typically for managing computing resources in a cloud environment. This
approach divides resources into two levels: a higher-level resource pool or cluster and lower-
level resource partitions or subclusters. Resource bundling plays a crucial role in optimizing
resource allocation and utilization within this architecture.

Architecture for Two-Level Resource Allocations:

Higher-Level Resource Pool or Cluster:
This level represents the entire resource pool available in the cloud environment, which may
consist of multiple physical servers, data centers, or cloud regions.
It is responsible for managing and distributing resources to different lower-level partitions based
on demand and resource availability.
The higher-level resource pool handles broad resource allocation decisions and balances
workloads across different subclusters.
Lower-Level Resource Partitions or Subclusters:
This level consists of smaller resource partitions or subclusters, each containing a subset of the
overall resources.
Subclusters are created based on specific requirements, such as application types, user groups, or
organizational departments.
Each subcluster operates independently and manages its resources, including virtual machines,
containers, and applications.
Importance of Resource Bundling:
Resource bundling refers to the process of grouping or bundling resources together within a
subcluster to improve resource utilization and efficiency. It involves allocating a combination of
compute, memory, storage, and networking resources in a way that maximizes the utilization of
physical hardware while meeting the needs of applications and workloads. The significance of
resource bundling includes:
Optimized Utilization: Resource bundling ensures that physical resources are efficiently utilized
by allocating the right amount of resources to each subcluster. Overallocation or underutilization
of resources is minimized.
Isolation and Performance: By grouping resources within subclusters, it's possible to isolate
workloads from one another. This isolation prevents resource contention and ensures consistent
performance for applications within each subcluster.
Tailored Allocation: Different workloads have varying resource requirements. Resource
bundling allows administrators to allocate resources that match the needs of specific
applications, ensuring optimal performance.
Resource Scaling: Resource bundling enables flexible scaling by allowing the addition or
removal of resource bundles based on demand. This dynamic scaling ensures that subclusters can
adapt to changing workloads.
Resource Efficiency: By bundling resources, administrators can take advantage of resource
sharing and pooling mechanisms, reducing waste and optimizing the use of available resources.
Cost Optimization: Efficient resource utilization through bundling can lead to cost savings by
reducing the need for excessive provisioning and improving overall resource efficiency.
Simplified Management: Managing resources at the subcluster level provides a higher degree of
granularity, making it easier to allocate, monitor, and optimize resources for specific groups of
applications or users.
In conclusion, the architecture for two-level resource allocations, coupled with resource
bundling, helps organizations optimize resource utilization, enhance workload performance, and
achieve cost-effective resource management in cloud environments. By carefully bundling
resources at the subcluster level, cloud administrators can ensure that workloads are efficiently
managed and isolated while making the most of the available physical infrastructure.
Discuss the role of virtualization and virtual machines in cloud computing. Explain its
performance issues.
Virtualization and virtual machines play a crucial role in cloud computing, enabling the efficient
sharing and utilization of resources among multiple users and applications. Let's discuss their
roles and then delve into some common performance issues associated with virtualization and
virtual machines in cloud environments.
Role of Virtualization and Virtual Machines in Cloud Computing:
 Resource Isolation and Sharing: Virtualization allows the creation of multiple virtual
machines (VMs) on a single physical server. Each VM operates independently, with its
own operating system (OS) and applications. This isolation ensures that VMs do not
interfere with each other, providing a secure and dedicated environment for different
users and applications.
 Server Consolidation: Virtualization enables server consolidation, where multiple VMs
can run on a single physical server. This consolidation leads to better resource utilization,
reduced power consumption, and cost savings, as it minimizes the number of physical
servers required to host various workloads.
  Flexibility and Scalability: Virtual machines can be easily provisioned, deprovisioned,
and migrated between physical servers, providing flexibility and scalability in cloud
environments. This agility allows cloud providers to adapt quickly to changing workloads
and optimize resource usage.
 Hardware Independence: VMs are abstracted from the underlying hardware, allowing
applications to run on different hardware
platforms without modification. This
hardware independence simplifies
application deployment and enhances
portability.
 Snapshot and Recovery: Virtualization
facilitates the creation of snapshots of
VMs, capturing their entire state at a
specific point in time. These snapshots
enable quick and efficient backups and
recovery of VMs in case of failures or
disasters.
 Testing and Development: Virtual machines provide an ideal environment for testing and
development purposes. Developers can create isolated VMs to test new applications or
software configurations without affecting the production environment.
Performance Issues in Virtualization and Virtual Machines:
 Overhead: Virtualization introduces some overhead due to the additional layer of
virtualization software. This overhead can impact VM performance, especially for CPU
and memory-intensive workloads.
 Resource Contention: In a shared environment, VMs may compete for resources such as
CPU, memory, and I/O. If not properly managed, resource contention can lead to
performance degradation.
 I/O Bottlenecks: VMs share physical storage resources, leading to potential I/O
bottlenecks, especially in cases of high storage activity from multiple VMs.
 Latency: Virtualization can introduce latency due to the additional processing required
for virtualization and network communication between VMs.
 VM Sprawl: Uncontrolled VM creation can lead to VM sprawl, where numerous VMs
are created but not adequately managed, leading to inefficient resource usage and
performance issues.
 VM Placement and Migration: Improper VM placement or frequent VM migrations can
impact performance, as VMs may not be optimally positioned to minimize resource
contention.
 Security Concerns: Virtual machine security is crucial to prevent unauthorized access to
VMs and potential attacks against the hypervisor, which can impact overall cloud
security and performance.
Cloud providers address these performance issues by employing various techniques, such as
workload balancing, resource monitoring, and performance tuning. They use sophisticated
management tools and virtualization technologies like hardware-assisted virtualization to
optimize performance and ensure a smooth and efficient cloud computing experience for their
users.
Differentiate between full virtualization and para virtualization

Features Full Virtualization ParaVirtualization

Definition It is the first generation of The interaction of the guest operating system
 software solutions for server with the hypervisor to improve performance
virtualization. and productivity is known as
paravirtualization.

Security It is less secure than It is more secure than full virtualization.

paravirtualization.

Performance Its performance is slow than Its performance is high than full
paravirtualization. virtualization.

Guest OS It supports all the Guest OS The Guest OS has to be modified in

Modification without any change. paravirtualization, and only a few OS support
it.

Guest OS It enables the Guest OS to run It enables the Guest OS to interact with the
hypervisor independently. hypervisor.
independent

Potable and It is more portable and It is less portable and compatible.

Compatible compatible.

Isolation It offers optimum isolation. It offers less isolation.

Efficient It is less efficient than It is more simplified than full virtualization.

paravirtualization.

Characteristic It is software based. It is cooperative virtualization.

Examples It is used in Microsoft, It is mainly used in VMware and Xen

VMware, and Parallels systems. systems.

Comparison:
 Full virtualization offers better compatibility because guest OSes do not require
modifications, making it suitable for running a wide range of operating systems,
including proprietary ones.
 Para-virtualization offers better performance but requires modifications to the guest OS.
It is ideal for open-source operating systems and environments where modifications can
be made.
 Full virtualization is more straightforward to set up since no guest OS modifications are
required.
 Para-virtualization requires guest OS modifications, making initial setup more involved.
However, once the modifications are made, the performance benefits become apparent.
With an example explain the working of the start-time fair queuing algorithm
The Start-Time Fair Queuing (STFQ) algorithm can also be applied in cloud computing to fairly
allocate resources, such as CPU time or network bandwidth, among different virtual machines
(VMs) or tasks within a cloud environment. Let's illustrate the working of the STFQ algorithm in
cloud computing with a CPU time allocation example:
Consider a cloud server with three VMs (A, B, and C) running different workloads. Each VM
requires CPU time to execute its tasks, and they have different resource requirements and
priorities.
VM A: High priority,
requires 30 CPU time
units. VM B: Medium
priority, requires 40 CPU
time units. VM C: Low
priority, requires 50 CPU
time units.
Step 1: Assign Start
Times The STFQ
algorithm assigns start
times to each VM based
on their priorities. Higher
priority VMs are given an earlier start time than lower priority VMs. The start times dictate when
each VM can begin using CPU time.
Let's assume that VM A has the highest priority, VM B has medium priority, and VM C has the
lowest priority. The start times are assigned as follows:
VM A: Start Time = 0 (Highest priority) VM B: Start Time = 30 (VM A's CPU time
requirement) VM C: Start Time = 70 (VM B's start time + VM B's CPU time requirement)
Step 2: CPU Time Allocation Based on Start Times The cloud server starts allocating CPU
time to each VM based on the assigned start times. It follows the STFQ algorithm to ensure fair
allocation of CPU time among the VMs.
At time t = 0, VM A begins executing its tasks since its start time is 0. VM A utilizes 30 units of
CPU time.
At time t = 30, VM B starts executing its tasks since its start time is 30. VM B utilizes 40 units of
CPU time.
At time t = 70, VM C starts executing its tasks since its start time is 70. VM C utilizes 50 units of
CPU time.
Step 3: Fair Resource Allocation The STFQ algorithm ensures that the total resource allocation
for each VM is proportional to its assigned resource requirement, taking into account its priority.
In our example, the CPU time allocation is as follows:
VM A: 30 units of CPU time (assigned requirement: 30 units) VM B: 40 units of CPU time
(assigned requirement: 40 units) VM C: 50 units of CPU time (assigned requirement: 50 units)
The CPU time allocation is proportional to the assigned CPU time requirements, indicating that
the STFQ algorithm has fairly allocated CPU time among the three VMs, considering their
priorities.
In a real cloud computing environment, the STFQ algorithm continuously monitors the resource
usage of VMs and dynamically adjusts the start times to ensure fair and efficient resource
allocation as workloads change over time. This approach prevents any single VM from
dominating the available resources, leading to better overall performance and fairness for all
users and applications in the cloud.