I nter Cloud: Utility-Oriented Federation of

Cloud Computing Envi ronments for Scaling of

Application Services

Rajkumar Buyya
1, 2
, Rajiv Ranjan
3
, Rodrigo N. Calheiros
1


1
Cloud Computing and Distributed Systems (CLOUDS) Laboratory
Department of Computer Science and Software Engineering
The University of Melbourne, Australia

2
Manjrasoft Pty Ltd, Australia
3
School of Computer Science and Engineering
The University of New South Wales, Sydney, Australia
Abstract
Cloud computing providers have setup several data centers at different
geographical locations over the Internet in order to optimally serve needs of their
customers around the world. However, existing systems do not support
mechanisms and policies for dynamically coordinating load distribution among
different Cloud-based data centers in order to determine optimal location for
hosting application services to achieve reasonable QoS levels. Further, the Cloud
computing providers are unable to predict geographic distribution of users
consuming their services, hence the load coordination must happen automatically,
and distribution of services must change in response to changes in the load. To
counter this problem, we advocate creation of federated Cloud computing
environment (InterCloud) that facilitates just-in-time, opportunistic, and scalable
provisioning of application services, consistently achieving QoS targets under
variable workload, resource and network conditions. The overall goal is to create a
computing environment that supports dynamic expansion or contraction of
capabilities (VMs, services, storage, and database) for handling sudden variations
in service demands.
This paper presents vision, challenges, and architectural elements of
InterCloud for utility-oriented federation of Cloud computing environments. The
proposed InterCloud environment supports scaling of applications across multiple
vendor clouds. We have validated our approach by conducting a set of rigorous
performance evaluation study using the CloudSim toolkit. The results demonstrate
that federated Cloud computing model has immense potential as it offers
significant performance gains as regards to response time and cost saving under
dynamic workload scenarios.
1. I ntroduction
In 1969, Leonard Kleinrock [1], one of the chief scientists of the original Ad-
vanced Research Projects Agency Network (ARPANET) project which seeded the
Internet, said: 'As of now, computer networks are still in their infancv, but as thev
grow up and become sophisticated, we will probablv see the spread of computer
utilities which, like present electric and telephone utilities, will service individual
homes and offices across the countrv.` This vision of computing utilities based on
a service provisioning model anticipated the massive transformation of the entire
computing industry in the 21
st
century whereby computing services will be readily
available on demand, like other utility services available in today`s society. Simi-
larly, computing service users (consumers) need to pay providers only when they
access computing services. In addition, consumers no longer need to invest heavi-
ly or encounter difficulties in building and maintaining complex IT infrastructure.
In such a model, users access services based on their requirements without re-
gard to where the services are hosted. This model has been referred to as utility
computing, or recently as Cloud computing [3][7]. The latter term denotes the in-
frastructure as a 'Cloud Irom which businesses and users are able to access ap-
plication services from anywhere in the world on demand. Hence, Cloud compu-
ting can be classified as a new paradigm for the dynamic provisioning of
computing services, typically supported by state-of-the-art data centers containing
ensembles of networked Virtual Machines.
Cloud computing delivers infrastructure, platform, and software (application)
as services, which are made available as subscription-based services in a pay-as-
you-go model to consumers. These services in industry are respectively referred to
as Infrastructure as a Service (IaaS), Platform as a Service (PaaS), and Software as
a Service (SaaS). A Berkeley Report in Feb 2009 states 'Cloud computing, the
long-held dream of computing as a utility, has the potential to transform a large
part oI the IT industry, making soItware even more attractive as a service [2].
Clouds aim to power the next generation data centers by architecting them as a
network of virtual services (hardware, database, user-interface, application logic)
so that users are able to access and deploy applications from anywhere in the
world on demand at competitive costs depending on users QoS (Quality of Ser-
vice) requirements [3]. Developers with innovative ideas for new Internet services
no longer require large capital outlays in hardware to deploy their service or hu-
man expense to operate it [2]. It offers significant benefit to IT companies by free-
ing them from the low level task of setting up basic hardware (servers) and soft-
ware infrastructures and thus enabling more focus on innovation and creating
business value for their services.
The business potential of Cloud computing is recognised by several market re-
search firms including IDC, which reports that worldwide spending on Cloud ser-
vices will grow from $16 billion by 2008 to $42 billion in 2012. Furthermore,
many applications making use of these utility-oriented computing systems such as
clouds emerge simply as catalysts or market makers that bring buyers and sellers
together. This creates several trillion dollars of worth to the utility/pervasive com-
3
puting industry as noted by Sun Microsystems co-founder Bill Joy [4]. He also in-
dicated 'It would take time until these markets to mature to generate this kind oI
value. Predicting now which companies will capture the value is impossible.
Many oI them have not even been created yet.
1.1 Application Scaling and Cloud I nf rastructure: Challenges and Re-
qui rements
Providers such as Amazon [15], Google [16], Salesforce [21], IBM, Microsoft
[17], and Sun Microsystems have begun to establish new data centers for hosting
Cloud computing application services such as social networking and gaming por-
tals, business applications (e.g., SalesForce.com), media content delivery, and sci-
entific workflows. Actual usage patterns of many real-world application services
vary with time, most of the time in unpredictable ways. To illustrate this, let us
consider an 'elastic application in the business/social networking domain that
needs to scale up and down over the course of its deployment.
Social Networ king Web Applications
Social networks such as Facebook and MySpace are popular Web 2.0 based appli-
cations. They serve dynamic content to millions of users, whose access and inte-
raction patterns are hard to predict. In addition, their features are very dynamic in
the sense that new plug-ins can be created by independent developers, added to the
main system and used by other users. In several situations load spikes can take
place, for instance, whenever new system features become popular or a new plug-
in application is deployed. As these social networks are organized in communities
of highly interacting users distributed all over the world, load spikes can take
place at different locations at any time. In order to handle unpredictable seasonal
and geographical changes in system workload, an automatic scaling scheme is pa-
ramount to keep QoS and resource consumption at suitable levels.
Social networking websites are built using multi-tiered web technologies,
which consist of application servers such as IBM WebSphere and persistency lay-
ers such as the MySQL relational database. Usually, each component runs in a
separate virtual machine, which can be hosted in data centers that are owned by
different cloud computing providers. Additionally, each plug-in developer has the
freedom to choose which Cloud computing provider offers the services that are
more suitable to run his/her plug-in. As a consequence, a typical social networking
web application is formed by hundreds of different services, which may be hosted
by dozens of Cloud data centers around the world. Whenever there is a variation
in temporal and spatial locality of workload, each application component must dy-
namically scale to offer good quality of experience to users.
1.2 Federated Cloud I nf rast ructures for Elastic Applications
In order to support a large number of application service consumers from around
the world, Cloud infrastructure providers (i.e., IaaS providers) have established
data centers in multiple geographical locations to provide redundancy and ensure
reliability in case of site failures. For example, Amazon has data centers in the US
(e.g., one in the East Coast and another in the West Coast) and Europe. However,
currently they (1) expect their Cloud customers (i.e., SaaS providers) to express a
preference about the location where they want their application services to be
hosted and (2) don`t provide seamless/automatic mechanisms Ior scaling their
hosted services across multiple, geographically distributed data centers. This ap-
proach has many shortcomings, which include (1) it is difficult for Cloud custom-
ers to determine in advance the best location for hosting their services as they may
not know origin of consumers of their services and (2) Cloud SaaS providers may
not be able to meet QoS expectations of their service consumers originating from
multiple geographical locations. This necessitates building mechanisms for seam-
less federation of data centers of a Cloud provider or providers supporting dynam-
ic scaling of applications across multiple domains in order to meet QoS targets of
Cloud customers.
In addition, no single Cloud infrastructure provider will be able to establish
their data centers at all possible locations throughout the world. As a result Cloud
application service (SaaS) providers will have difficulty in meeting QoS expecta-
tions for all their consumers. Hence, they would like to make use of services of
multiple Cloud infrastructure service providers who can provide better support for
their specific consumer needs. This kind of requirements often arises in enterpris-
es with global operations and applications such as Internet service, media hosting,
and Web 2.0 applications. This necessitates building mechanisms for federation of
Cloud infrastructure service providers for seamless provisioning of services across
different Cloud providers. There are many challenges involved in creating such
Cloud interconnections through federation.
To meet these requirements, next generation Cloud service providers should be
able to: (i) dynamically expand or resize their provisioning capability based on
sudden spikes in workload demands by leasing available computational and sto-
rage capabilities from other Cloud service providers; (ii) operate as parts of a mar-
ket driven resource leasing federation, where application service providers such
as Salesforce.com host their services based on negotiated Service Level Agree-
ment (SLA) contracts driven by competitive market prices; and (iii) deliver on
demand, reliable, cost-effective, and QoS aware services based on virtualization
technologies while ensuring high QoS standards and minimizing service costs.
They need to be able to utilize market-based utility models as the basis for provi-
sioning of virtualized software services and federated hardware infrastructure
among users with heterogeneous applications and QoS targets.
1.3 Research Issues
The diversity and flexibility of the functionalities (dynamically shrinking and
growing computing systems) envisioned by federated Cloud computing model,
combined with the magnitudes and uncertainties of its components (workload,
compute servers, services, workload), pose difficult problems in effective provi-
sioning and delivery oI application services. Provisioning means 'high-level
management of computing, network, and storage resources that allow them to ef-
fectively provide and deliver services to customers. In particular, finding effi-
cient solutions for following challenges is critical to exploiting the potential of fe-
derated Cloud infrastructures:
5
Application Service Behavior Prediction: It is critical that the system is
able to predict the demands and behaviors of the hosted services, so that it intel-
ligently undertake decisions related to dynamic scaling or de-scaling of services
over federated Cloud infrastructures. Concrete prediction or forecasting models
must be built before the behavior of a service, in terms of computing, storage,
and network bandwidth requirements, can be predicted accurately. The real
challenge in devising such models is accurately learning and fitting statistical
functions [31] to the observed distributions of service behaviors such as request
arrival pattern, service time distributions, I/O system behaviors, and network
usage. This challenge is further aggravated by the existence of statistical corre-
lation (such as stationary, short- and long-range dependence, and pseudo-
periodicity) between different behaviors of a service.
Flexible Mapping of Services to Resources: With increased operating costs
and energy requirements of composite systems, it becomes critical to maximize
their efficiency, cost-effectiveness, and utilization [30] . The process of mapping
services to resources is a complex undertaking, as it requires the system to com-
pute the best software and hardware configuration (system size and mix of re-
sources) to ensure that QoS targets of services are achieved, while maximizing
system efficiency and utilization. This process is further complicated by the un-
certain behavior of resources and services. Consequently, there is an immediate
need to devise performance modeling and market-based service mapping tech-
niques that ensure efficient system utilization without having an unacceptable
impact on QoS targets.
Economic Models Driven Opti mization Techniques: The market-driven
decision making problem [6] is a combinatorial optimization problem that
searches the optimal combinations of services and their deployment plans. Un-
like many existing multi-objective optimization solutions, the optimization
models that ultimately aim to optimize both resource-centric (utilization, availa-
bility, reliability, incentive) and user-centric (response time, budget spent, fair-
ness) QoS targets need to be developed.
Integration and Interoperability: For many SMEs, there is a large amount
of IT assets in house, in the form of line of business applications that are unlike-
ly to ever be migrated to the cloud. Further, there is huge amount of sensitive
data in an enterprise, which is unlikely to migrate to the cloud due to privacy
and security issues. As a result, there is a need to look into issues related to inte-
gration and interoperability between the software on premises and the services
in the cloud. In particular [28]: (i) Identity management: authentication and au-
thorization of service users; provisioning user access; federated security model;
(ii) Data Management: not all data will be stored in a relational database in the
cloud, eventual consistency (BASE) is taking over from the traditional ACID
transaction guarantees, in order to ensure sharable data structures that achieve
high scalability. (iii) Business process orchestration: how does integration at a
business process level happen across the software on premises and service in the
Cloud boundary? Where do we store business rules that govern the business
process orchestration?
Scalable Monitoring of System Components: Although the components
that contribute to a federated system may be distributed, existing techniques
usually employ centralized approaches to overall system monitoring and man-
agement. We claim that centralized approaches are not an appropriate solution
for this purpose, due to concerns of scalability, performance, and reliability aris-
ing from the management of multiple service queues and the expected large
volume of service requests. Monitoring of system components is required for ef-
fecting on-line control through a collection of system performance characteris-
tics. Therefore, we advocate architecting service monitoring and management
services based on decentralized messaging and indexing models [27].
1.4 Overall Vision
To meet aforementioned requirements of auto-scaling Cloud applications, future
efforts should focus on design, development, and implementation of software sys-
tems and policies for federation of Clouds across network and administrative
boundaries. The key elements for enabling federation of Clouds and auto-scaling
application services are: Cloud Coordinators, Brokers, and an Exchange. The re-
source provisioning within these federated clouds will be driven by market-
oriented principles for efficient resource allocation depending on user QoS targets
and workload demand patterns. To reduce power consumption cost and improve
service localization while complying with the Service Level Agreement (SLA)
contracts, new on-line algorithms for energy-aware placement and live migration
of virtual machines between Clouds would need to be developed. The approach
for realisation of this research vision consists of investigation, design, and devel-
opment of the following:
x Architectural framework and principles for the development of utility-
oriented clouds and their federation
x A Cloud Coordinator for exporting Cloud services and their management
driven by market-based trading and negotiation protocols for optimal
QoS delivery at minimal cost and energy.
x A Cloud Broker responsible for mediating between service consumers
and Cloud coordinators.
x A Cloud Exchange acts as a market maker enabling capability sharing
across multiple Cloud domains through its match making services.
x A software platform implementing Cloud Coordinator, Broker, and Ex-
change for federation.

The rest of this paper is organized as follows: First, a concise survey on the exist-
ing state-of-the-art in Cloud provisioning is presented. Next, the comprehensive
description related to overall system architecture and its elements that forms the
basis for constructing federated Cloud infrastructures is given. This is followed by
some initial experiments and results, which quantifies the performance gains de-
7
livered by the proposed approach. Finally, the paper ends with brief conclusive
remarks and discussion on perspective future research directions.
2. State-of-the-art in Cloud Provisioning
The key Cloud platforms in Cloud computing domain including Amazon Web
Services [15], Microsoft Azure [17], Google AppEngine [16], Manjrasoft Aneka
[32], Eucalyptus [22], and GoGrid [23] offer a variety of pre-packaged services
for monitoring, managing and provisioning resources and application services.
However, the techniques implemented in each of these Cloud platforms vary (refer
to Table 1).
The three Amazon Web Services (AWS), Elastic Load Balancer [25], Auto
Scaling and CloudWatch [24] together expose functionalities which are required
for undertaking provisioning of application services on Amazon EC2. Elastic
Load Balancer service automatically provisions incoming application workload
across available Amazon EC2 instances. Auto-Scaling service can be used for dy-
namically scaling-in or scaling-out the number of Amazon EC2 instances for han-
dling changes in service demand patterns. And finally the CloudWatch service can
be integrated with above services for strategic decision making based on real-time
aggregated resource and service performance information.

Table 1: Summary of provisioning capabilities exposed by public Cloud platforms

Manjrasoft Aneka is a platform for building and deploying distributed applica-
tions on Clouds. It provides a rich set of APIs for transparently exploiting distri-
buted resources and expressing the business logic of applications by using the pre-
ferred programming abstractions. Aneka is also a market-oriented Cloud platform
since it allows users to build and schedule applications, provision resources and
monitor results using pricing, accounting, and QoS/SLA services in private and/or
public (leased) Cloud environments. Aneka also allows users to build different
run-time environments such as enterprise/private Cloud by harness computing re-
Cloud Platforms Load Balancing Provisioning Auto Scaling
Amazon Elastic Compute Cloud \ \ \
Eucalyptus \ \
Microsoft Windows Azure \
\
(fixed templates so far)
\
(Manual)
Google App Engine \ \ \

Manjrasoft Aneka
\ \ \
GoGrid Cloud Hosting \ \
\
(Programmatic way
only)
sources in network or enterprise data centers, public Clouds such as Amazon EC2,
and hybrid clouds by combining enterprise private Clouds managed by Aneka
with resources from Amazon EC2 or other enterprise Clouds build and managed
using technologies such as XenServer.
Eucalyptus [22] is an open source Cloud computing platform. It is composed of
three controllers. Among the controllers, the Cluster Controller is a key compo-
nent to application service provisioning and load balancing. Each Cluster Control-
ler is hosted on the head node of a cluster to interconnect outer public networks
and inner private networks together. By monitoring the state information of in-
stances in the pool of server controllers, the Cluster Controller can select the
available service/server for provisioning incoming requests. However, as com-
pared to AWS, Eucalyptus still lacks some of the critical functionalities, such as
auto scaling for built-in provisioner.
Fundamentally, Windows Azure Fabric [17] has a weave-like structure, which
is composed of node (servers and load balancers), and edges (power, Ethernet and
serial communications). The Fabric Controller manages a service node through a
built-in service, named Azure Fabric Controller Agent, which runs in the back-
ground, tracking the state of the server, and reporting these metrics to the Control-
ler. If a fault state is reported, the Controller can manage a reboot of the server or
a migration of services from the current server to other healthy servers. Moreover,
the Controller also supports service provisioning by matching the services against
the VMs that meet required demands.
GoGrid Cloud Hosting offers developers the F5 Load Balancers [23] for distri-
buting application service traffic across servers, as long as IPs and specific ports
of these servers are attached. The load balancer allows Round Robin algorithm
and Least Connect algorithm for routing application service requests. Also, the
load balancer is able to sense a crash of the server, redirecting further requests to
other available servers. But currently, GoGrid Cloud Hosting only gives develop-
ers programmatic APIs to implement their custom auto-scaling service.
Unlike other Cloud platforms, Google App Engine offers developers a scalable
platform in which applications can run, rather than providing access directly to a
customized virtual machine. Therefore, access to the underlying operating system
is restricted in App Engine. And load-balancing strategies, service provisioning
and auto scaling are all automatically managed by the system behind the scenes.
However, at this time Google App Engine can only support provisioning of web
hosting type of applications.
However, no single Cloud infrastructure providers have their data centers at all
possible locations throughout the world. As a result Cloud application service
(SaaS) providers will have difficulty in meeting QoS expectations for all their us-
ers. Hence, they would prefer to logically construct federated Cloud infrastruc-
tures (mixing multiple public and private clouds) to provide better support for
their specific user needs. This kind of requirements often arises in enterprises with
global operations and applications such as Internet service, media hosting, and
Web 2.0 applications. This necessitates building technologies and algorithms for
seamless federation of Cloud infrastructure service providers for autonomic provi-
sioning of services across different Cloud providers.
9
3. System Architecture and Elements of I nter Cloud
Figure 1 shows the high level components of the service-oriented architectural
Iramework consisting oI client`s brokering and coordinator services that support
utility-driven federation of clouds: application scheduling, resource allocation and
migration of workloads. The architecture cohesively couples the administratively
and topologically distributed storage and computes capabilities of Clouds as parts
of single resource leasing abstraction. The system will ease the cross-domain ca-
pabilities integration for on demand, flexible, energy-efficient, and reliable access
to the infrastructure based on emerging virtualization technologies [8][9].


Cloud Exchange
(CEx)
Directory
Bank
Auctioneer
Negotiate/Bid
Request
Capacity
Cloud Broker N
User
. . . . . .
Publish Offers
Compute Cloud
Cluster (VM Pool)
Pool node
VM
VM VM
Pool node
VM
VM
VM
Manager
Pool node
VM VM
Cluster (VM Pool)
Pool node
VM
VM VM
Pool node
VM
VM
VM
Manager
Pool node
VM VM
Pool node
VM
VM VM
Pool node Pool node
VM
VM VM
Pool node
VM
VM
Pool node Pool node
VM
VM
VM
Manager
Pool node
VM VM
Pool node Pool node
VM VM
Storage Cloud
Compute Cloud
Storage Cloud
Enterprise
Resource
Server
(Proxy)
Enterprise IT Consumer
User
Cloud Broker 1
Cloud
Coordinator
Cloud
Coordinator
Cloud
Coordinator

Figure 1: Federated network of clouds mediated by a Cloud exchange.

The Cloud Exchange (CEx) acts as a market maker for bringing together ser-
vice producers and consumers. It aggregates the infrastructure demands from the
application brokers and evaluates them against the available supply currently pub-
lished by the Cloud Coordinators. It supports trading of Cloud services based on
competitive economic models [6] such as commodity markets and auctions. CEx
allows the participants (Cloud Coordinators and Cloud Brokers) to locate provid-
ers and consumers with fitting offers. Such markets enable services to be commo-
ditized and thus, would pave the way for creation of dynamic market infrastruc-
ture for trading based on SLAs. An SLA specifies the details of the service to be
provided in terms of metrics agreed upon by all parties, and incentives and penal-
ties for meeting and violating the expectations, respectively. The availability of a
banking system within the market ensures that financial transactions pertaining to
SLAs between participants are carried out in a secure and dependable environ-
ment. Every client in the federated platform needs to instantiate a Cloud Brokering
service that can dynamically establish service contracts with Cloud Coordinators
via the trading functions exposed by the Cloud Exchange.
3.1 Cloud Coordinator (CC)
The Cloud Coordinator service is responsible for the management of domain spe-
cific enterprise Clouds and their membership to the overall federation driven by
market-based trading and negotiation protocols. It provides a programming, man-
agement, and deployment environment for applications in a federation of Clouds.
Figure 2 shows a detailed depiction of resource management components in the
Cloud Coordinator service.
Scheduling & Allocation
Monitoring
Allocator
Scheduler
Market & Policy Engine
Accounting
SLA
Pricing
Billing
Vi rtualization
Sensor
Hypervisor
Virtual
Machine
VM
Manager
Power
Cloud Coordinator
Data Center Resources
Heat
Utilization
Di scovery &
Moni toring
Querying
Updating
Mobility
Manager
Application Composition Engine
User
Interface
Deployer
Database
Application
Server
Remote
i nt eractions
S

e

r

v

i

c

e

s
e-Business
Workflow
e-Science
Workflow
CDN
Parameter
sweep
Web
Hosting
Social
Networking
Programming Layer
Application Programming Interface (API)
Workload
Models
Performance
Models

Figure 2. Cloud Coordinator software architecture.
The Cloud Coordinator exports the services of a cloud to the federation by im-
plementing basic functionalities for resource management such as scheduling, al-
location, (workload and performance) models, market enabling, virtualization, dy-
namic sensing/monitoring, discovery, and application composition as discussed
below:
Scheduling and Al location: This component allocates virtual machines to the
Cloud nodes based on user`s QoS targets and the Clouds energy management
goals. On receiving a user application, the scheduler does the following: (i) con-
sults the Application Composition Engine about availability of software and
hardware infrastructure services that are required to satisfy the request locally, (ii)
asks the Sensor component to submit Ieedback on the local Cloud nodes` energy
consumption and utilization status; and (iii) enquires the Market and Policy En-
gine about accountability of the submitted request. A request is termed as accoun-
table if the concerning user has available credits in the Cloud bank and based on
the specified QoS constraints the establishment of SLA is feasible. In case all
11
three components reply favorably, the application is hosted locally and is periodi-
cally monitored until it finishes execution.
Data center resources may deliver different levels of performance to their
clients; hence, QoS-aware resource selection plays an important role in Cloud
computing. Additionally, Cloud applications can present varying workloads. It is
therefore essential to carry out a study of Cloud services and their workloads in
order to identify common behaviors, patterns, and explore load forecasting ap-
proaches that can potentially lead to more efficient scheduling and allocation. In
this context, there is need to analyse sample applications and correlations between
workloads, and attempt to build performance models that can help explore trade-
offs between QoS targets.
Mar ket and Policy Engine: The SLA module stores the service terms and
conditions that are being supported by the Cloud to each respective Cloud Broker
on a per user basis. Based on these terms and conditions, the Pricing module can
determine how service requests are charged based on the available supply and re-
quired demand of computing resources within the Cloud. The Accounting module
stores the actual usage information of resources by requests so that the total usage
cost of each user can be calculated. The Billing module then charges the usage
costs to users accordingly.
Cloud customers can normally associate two or more conflicting QoS targets
with their application services. In such cases, it is necessary to trade off one or
more QoS targets to find a superior solution. Due to such diverse QoS targets and
varying optimization objectives, we end up with a Multi-dimensional Optimiza-
tion Problem (MOP). For solving the MOP, one can explore multiple heterogene-
ous optimization algorithms, such as dynamic programming, hill climbing, parallel
swarm optimization, and multi-objective genetic algorithm.
Application Composition engine: This component of the Cloud Coordinator
encompasses a set of features intended to help application developers create and
deploy [18] applications, including the ability for on demand interaction with a da-
tabase backend such as SQL Data services provided by Microsoft Azure, an appli-
cation server such as Internet Information Server (IIS) enabled with secure
ASP.Net scripting engine to host web applications, and a SOAP driven Web ser-
vices API for programmatic access along with combination and integration with
other applications and data.
Vi rtualization: VMs support flexible and utility driven configurations that
control the share of processing power they can consume based on the time critical-
ity of the underlying application. However, the current approaches to VM-based
Cloud computing are limited to rather inflexible configurations within a Cloud.
This limitation can be solved by developing mechanisms for transparent migration
of VMs across service boundaries with the aim of minimizing cost of service deli-
very (e.g., by migrating to a Cloud located in a region where the energy cost is
low) and while still meeting the SLAs. The Mobility Manager is responsible for
dynamic migration of VMs based on the real-time feedback given by the Sensor
service. Currently, hypervisors such as VMware [8] and Xen [9] have a limitation
that VMs can only be migrated between hypervisors that are within the same sub-
net and share common storage. Clearly, this is a serious bottleneck to achieve
adaptive migration of VMs in federated Cloud environments. This limitation has
to be addressed in order to support utility driven, power-aware migration of VMs
across service domains.
Sensor: Sensor infrastructure will monitor the power consumption, heat dissi-
pation, and utilization of computing nodes in a virtualized Cloud environment. To
this end, we will extend our Service Oriented Sensor Web [14] software system.
Sensor Web provides a middleware infrastructure and programming model for
creating, accessing, and utilizing tiny sensor devices that are deployed within a
Cloud. The Cloud Coordinator service makes use of Sensor Web services for dy-
namic sensing of Cloud nodes and surrounding temperature. The output data re-
ported by sensors are Ieedback to the Coordinator`s Virtualization and Scheduling
components, to optimize the placement, migration, and allocation of VMs in the
Cloud. Such sensor-based real time monitoring of the Cloud operating environ-
ment aids in avoiding server breakdown and achieving optimal throughput out of
the available computing and storage nodes.
Discovery and Monitoring: In order to dynamically perform scheduling, re-
source allocation, and VM migration to meet SLAs in a federated network, it is
mandatory that up-to-date inIormation related to Cloud`s availability, pricing and
SLA rules are made available to the outside domains via the Cloud Exchange.
This component of Cloud Coordinator is solely responsible for interacting with the
Cloud Exchange through remote messaging. The Discovery and Monitoring com-
ponent undertakes the following activities: (i) updates the resource status metrics
including utilization, heat dissipation, power consumption based on feedback giv-
en by the Sensor component; (ii) facilitates the Market and Policy Engine in pe-
riodically publishing the pricing policies, SLA rules to the Cloud Exchange; (iii)
aids the Scheduling and Allocation component in dynamically discovering the
Clouds that offer better optimization for SLA constraints such as deadline and
budget limits; and (iv) helps the Virtualization component in determining load and
power consumption; such information aids the Virtualization component in per-
forming load-balancing through dynamic VM migration.
Further, system components will need to share scalable methods for collecting
and representing monitored data. This leads us to believe that we should intercon-
nect and monitor system components based on decentralized messaging and in-
formation indexing infrastructure, called Distributed Hash Tables (DHTs) [26].
However, implementing scalable techniques that monitor the dynamic behaviors
related to services and resources is non-trivial. In order to support a scalable ser-
vice monitoring algorithm over a DHT infrastructure, additional data distribution
indexing techniques such as logical multi-dimensional or spatial indices [27]
(MX-CIF Quad tree, Hilbert Curves, Z Curves) should be implemented.
3.2 Cloud Broker (CB)
The Cloud Broker acting on behalf of users identifies suitable Cloud service pro-
viders through the Cloud Exchange and negotiates with Cloud Coordinators for an
allocation of resources that meets QoS needs of users. The architecture of Cloud
Broker is shown in Figure 3 and its components are discussed below:
13
User I nterface: This provides the access linkage between a user application in-
terface and the broker. The Application Interpreter translates the execution re-
quirements of a user application which include what is to be executed, the descrip-
tion of task inputs including remote data files (if required), the information about
task outputs (if present), and the desired QoS. The Service Interpreter understands
the service requirements needed for the execution which comprise service loca-
tion, service type, and specific details such as remote batch job submission sys-
tems for computational services. The Credential Interpreter reads the credentials
for accessing necessary services.


Job Monitor Job Dispatcher
Persistence
Database
Execution Interface
User Interface
Core Services
Cloud Coordinator 1
Application
Interpreter
Service
Interpreter
Credential
Interpreter
Global
Cloud Market
Service
Monitor
Service
Negotiator
Scheduler
Cloud Coordinator n . . . . . . . .

Figure 3: High level architecture of Cloud Broker service.


Core Services: They enable the main functionality of the broker. The Service
Negotiator bargains for Cloud services from the Cloud Exchange. The Scheduler
determines the most appropriate Cloud services for the user application based on
its application and service requirements. The Service Monitor maintains the status
of Cloud services by periodically checking the availability of known Cloud ser-
vices and discovering new services that are available. If the local Cloud is unable
to satisfy application requirements, a Cloud Broker lookup request that encapsu-
lates the user`s QoS parameter is submitted to the Cloud Exchange, which
matches the lookup request against the available offers. The matching procedure
considers two main system performance metrics: first, the user specified QoS tar-
gets must be satisfied within acceptable bounds and, second, the allocation should
not lead to overloading (in terms of utilization, power consumption) of the nodes.
In case the match occurs the quote is forwarded to the requester (Scheduler). Fol-
lowing that, the Scheduling and Allocation component deploys the application
with the Cloud that was suggested by Cloud market.
Execution I nterface: This provides execution support for the user application.
The Job Dispatcher creates the necessary broker agent and requests data files (if
any) to be dispatched with the user application to the remote Cloud resources for
execution. The Job Monitor observes the execution status of the job so that the re-
sults of the job are returned to the user upon job completion.
Persistence: This maintains the state of the User Interface, Core Services, and
Execution Interface in a database. This facilitates recovery when the broker fails
and assists in user-level accounting.
3.3 Cloud Exchange (CEx)
As a market maker, the CEx acts as an information registry that stores the Cloud`s
current usage costs and demand patterns. Cloud Coordinators periodically update
their availability, pricing, and SLA policies with the CEx. Cloud Brokers query
the registry to learn information about existing SLA offers and resource availabili-
ty of member Clouds in the federation. Furthermore, it provides match-making
services that map user requests to suitable service providers. Mapping functions
will be implemented by leveraging various economic models such as Continuous
Double Auction (CDA) as proposed in earlier works [6].
As a market maker, the Cloud Exchange provides directory, dynamic bidding
based service clearance, and payment management services as discussed below.
x Di rectory: The market directory allows the global CEx participants to locate
providers or consumers with the appropriate bids/offers. Cloud providers can
publish the available supply of resources and their offered prices. Cloud con-
sumers can then search for suitable providers and submit their bids for re-
quired resources. Standard interfaces need to be provided so that both provid-
ers and consumers can access resource information from one another readily
and seamlessly.
x Auctioneer : Auctioneers periodically clear bids and asks received from the
global CEx participants. Auctioneers are third party controllers that do not
represent any providers or consumers. Since the auctioneers are in total con-
trol of the entire trading process, they need to be trusted by participants.
x Bank: The banking system enforces the financial transactions pertaining to
agreements between the global CEx participants. The banks are also indepen-
dent and not controlled by any providers and consumers; thus facilitating im-
partiality and trust among all Cloud market participants that the financial
transactions are conducted correctly without any bias. This should be realized
by integrating with online payment management services, such as PayPal,
with Clouds providing accounting services.
4. Early Experiments and Preli minary Results
Although we have been working towards the implementation of a software system
for federation of cloud computing environments, it is still a work-in-progress.
Hence, in this section, we present our experiments and evaluation that we under-
took using CloudSim [29] framework for studying the feasibility of the proposed
research vision. The experiments were conducted on a Celeron machine having
the following configuration: 1.86GHz with 1MB of L2 cache and 1 GB of RAM
running a standard Ubuntu Linux version 8.04 and JDK 1.6.
15
4.1. Evaluating Performance of Federated Cloud Computing Envi ronments
The first experiment aims at proving that federated infrastructure of clouds has po-
tential to deliver better performance and service quality as compared to existing
non-federated approaches. To this end, a simulation environment that models fed-
eration of three Cloud providers and a user (Cloud Broker) is modeled. Every pro-
vider instantiates a Sensor component, which is responsible for dynamically sens-
ing the availability information related to the local hosts. Next, the sensed
statistics are reported to the Cloud Coordinator that utilizes the information in un-
dertaking load-migration decisions. We evaluate a straightforward load-migration
policy that performs online migration of VMs among federated Cloud providers
only if the origin provider does not have the requested number of free VM slots
available. The migration process involves the following steps: (i) creating a virtual
machine instance that has the same configuration, which is supported at the desti-
nation provider; and (ii) migrating the applications assigned to the original virtual
machine to the newly instantiated virtual machine at the destination provider. The
federated network of Cloud providers is created based on the topology shown in
Figure 4.

Public Cloud Provider 1
Public Cloud Provider 2
Public Cloud Provider 0
Cloud
Coordinator
Cloud
Coordinator
Cloud
Coordinator
Load
Status
Application
Cloud
Broker
Monitors:
Resource Utilization
Network Traffic
Disk Reads/Writes...
T1
T3
T4
T2
T
T T
T

Figure 4: A network topology of federated Data Centers.

Every Public Cloud provider in the system is modeled to have 50 computing
hosts, 10GB of memory, 2TB of storage, 1 processor with 1000 MIPS of capacity,
and a time-shared VM scheduler. Cloud Broker on behalf of the user requests
instantiation of a VM that requires 256MB of memory, 1GB of storage, 1 CPU,
and time-shared Cloudlet scheduler. The broker requests instantiation of 25 VMs
and associates one Cloudlet (Cloud application abstraction) to each VM to be
executed. These requests are originally submitted with the Cloud Provider 0. Each
Cloudlet is modeled to have 1800000 MIs (Million Instrictions). The simulation
experiments were run under the following system configurations: (i) a federated
network of clouds is available, hence data centers are able to cope with peak
demands by migrating the excess of load to the least loaded ones; and (ii) the data
centers are modeled as independent entities (without federation). All the workload
submitted to a Cloud provider must be processed and executed locally.
Table 2 shows the average turn-around time for each Cloudlet and the overall
makespan of the user application for both cases. A user application consists of one
or more Cloudlets with sequential dependencies. The simulation results reveal that
the availability of federated infrastructure of clouds reduces the average turn-
around time by more than 50%, while improving the makespan by 20%. It shows
that, even for a very simple load-migration policy, availability of federation brings
signiIicant beneIits to user`s application perIormance.

Table 2: Performance Results.
Performance Metrics With
Federation
Without
Federation
%
I mprovement
Average Turn Around
Time (Secs)
2221.13 4700.1 > 50%
Makespan (Secs) 6613.1 8405 20%
4.2 Evaluating a Cloud provisioning st rategy in a federated envi ronment
In previous subsection, we focused on evaluation of the federated service and
resource provisioning scenarios. In this section, a more complete experiment that
also models the inter-connection network between federated clouds, is presented.
This example shows how the adoption of federated clouds can improve
productivity of a company with expansion of private cloud capacity by
dynamically leasing resources from public clouds at a reasonably low cost.
The simulation scenario is based on federating a private cloud with the Amazon
EC2 cloud. The public and the private clouds are represented as two data centers
in the simulation. A Cloud Coordinator in the private data center receives the
user`s applications and processes them in a FCFS basis, queuing the tasks when
there is available capacity for them in the infrastructure. To evaluate the
effectiveness of a hybrid cloud in speeding up tasks execution, two scenarios are
simulated. In the first scenario, tasks are kept in the waiting queue until active
tasks finish (currently executing) in the private cloud. All the workload is
processed locally within the private cloud. In the second scenario, the waiting
tasks are directly sent to available public cloud. In other words, second scenario
simulates a Cloud Bursts case for integrating local private cloud with public cloud
form handing peak in service demands. Before submitting tasks to the Amazon
cloud, the VM images (AMI) are loaded and instantiated. The number of images
instantiated in the Cloud is varied in the experiment, from 10% to 100% of the
number of machines available in the private cloud. Once images are created, tasks
in the waiting queues are submitted to them, in such a way that only one task run
on each VM at a given instance of time. Every time a task finishes, the next task in
17
the waiting queue is submitted to the available VM host. When there were no
tasks to be submitted to the VM, it is destroyed in the cloud.
The local private data center hosted 100 machines. Each machine has 2GB of
RAM, 10TB of storage and one CPU run 1000 MIPS. The virtual machines
created in the public cloud were based in an Amazon's small instance (1.7 GB of
memory, 1 virtual core, and 160 GB of instance storage). We consider in this
example that the virtual core of a small instance has the same processing power as
the local machine.
The workload sent to the private cloud is composed of 10000 tasks. Each task
takes between 20 and 22 minutes to run in one CPU. The exact amount of time
was randomly generated based on the normal distribution. Each of the 10000 tasks
is submitted at the same time to the scheduler queue.
Table 3 shows the makespan of the tasks running only in the private cloud and
with extra allocation of resources from the public cloud. In the third column, we
quantify the overall cost of the services. The pricing policy was designed based on
the Amazon`s small instances (U$ 0.10 per instance per hour) pricing model. It
means that the cost per instance is charged hourly in the beginning of execution.
And, if an instance runs during 1 hour and 1 minute, the amount for 2 hours (U$
0.20) will be charged.

Table 3: Cost and performance of several public/private cloud strategies
Makespan (s) Cloud Cost (U$)
Private only 127155.77 0.00
Public 10% 115902.34 32.60
Public 20% 106222.71 60.00
Public 30% 98195.57 83.30
Public 40% 91088.37 103.30
Public 50% 85136.78 120.00
Public 60% 79776.93 134.60
Public 70% 75195.84 147.00
Public 80% 70967.24 160.00
Public 90% 67238.07 171.00
Public 100% 64192.89 180.00
Increasing the number of resources by a rate reduces the job makespan at the
same rate, which is an expected observation or outcome. However, the cost
associated with the processing increases significantly at higher rates. Nevertheless,
the cost is still acceptable, considering that peak demands happen only
occasionally and that most part of time this extra public cloud capacity is not
required. So, leasing public cloud resources is cheapest than buying and
maintaining extra resources that will spend most part of time idle.
5. Conclusions and Future Di rections
Development of fundamental techniques and software systems that integrate
distributed clouds in a federated fashion is critical to enabling composition and
deployment of elastic application services. We believe that outcomes of this
research vision will make significant scientific advancement in understanding the
theoretical and practical problems of engineering services for federated
environments. The resulting framework facilitates the federated management of
system components and protects customers with guaranteed quality of services in
large, federated and highly dynamic environments. The different components of
the proposed framework offer powerful capabilities to address both services and
resources management, but their end-to-end combination aims to dramatically
improve the effective usage, management, and administration of Cloud systems.
This will provide enhanced degrees of scalability, flexibility, and simplicity for
management and delivery of services in federation of clouds.
In our future work, we will focus on developing comprehensive model driven
approach to provisioning and delivering services in federated environments. These
models will be important because they allow adaptive system management by es-
tablishing useful relationships between high-level performance targets (specified
by operators) and low-level control parameters and observables that system com-
ponents can control or monitor. We will model the behaviour and performance of
different types of services and resources to adaptively transform service requests.
We will use a broad range of analytical models and statistical curve-fitting tech-
niques such as multi-class queuing models and linear regression time series. These
models will drive and possibly transform the input to a service provisioner, which
improves the efficiency of the system. Such improvements will better ensure the
achievement of performance targets, while reducing costs due to improved utiliza-
tion of resources. It will be a major advancement in the field to develop a robust
and scalable system monitoring infrastructure to collect real-time data and re-
adjust these models dynamically with a minimum of data and training time. We
believe that these models and techniques are critical for the design of stochastic
provisioning algorithms across large federated Cloud systems where resource
availability is uncertain.
Lowering the energy usage of data centers is a challenging and complex issue
because computing applications and data are growing so quickly that increasingly
larger servers and disks are needed to process them fast enough within the
required time period. Green Cloud computing is envisioned to achieve not only
efficient processing and utilization of computing infrastructure, but also
minimization of energy consumption. This is essential for ensuring that the future
growth of Cloud Computing is sustainable. Otherwise, Cloud computing with
increasingly pervasive front-end client devices interacting with back-end data
centers will cause an enormous escalation of energy usage. To address this
problem, data center resources need to be managed in an energy-efficient manner
to drive Green Cloud computing. In particular, Cloud resources need to be
allocated not only to satisfy QoS targets specified by users via Service Level
19
Agreements (SLAs), but also to reduce energy usage. This can be achieved by
applying market-based utility models to accept requests that can be fulfilled out of
the many competing requests so that revenue can be optimized along with energy-
efficient utilization of Cloud infrastructure.
Acknowledgements: We acknowledge all members of Melbourne CLOUDS Lab
(especially William Voorsluys and Suraj Pandey) for their contributions to
InterCloud investigation.
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