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strategies evaluated by heuristic algorithm on basis of smallest number of servers
required to meet the negotiated SLA. Probability dependent priority found. [6]
In 2011 M.Noureddine and R.Bashroush demonstrate modality cost analysis based
methodology for cost effective datacenter capacity planning in the cloud. Provisioning
appropriate resources to each tenant /application such that services level agreements
(SLA) met. The objectives of this paper to use methodology to guide resource
provisioning. The systematic methodology to estimate the performance of each
modality. The quantitative methodology explained for planning the capacity of cloud
datacenter. Set the applications in modalities and measure the cost of hardware
resources. Discussed three experiments,MCA-S, MCA-M and MCA-L that
represented user profiles and measure the resource overhead. A validation tool is used
to simulate load and validate assumptions. Office Lync Server Stress (LSS) generate
simulated load.[7]
In January 31, 2011, Sivadon Chaisiri, Bu-Sung Lee, and Dusit Niyato discuss
about the Optimization of Resource Provisioning Cost. Under the resource
provisioning optimal cloud provisioning algorithm illustrates virtual machine
management that consider multiple provisioning stages with demand price
uncertainty. In this task system model of cloud computing environment has been
thoroughly explained using various techniques such as cloud consumer, virtual
machine and cloud broker in details. [8]
The agent-based adaptive resource allocation is discussed in 2011 by the Gihun
Jung, Kwang Mong Sim. In this paper the provider needs to allocate each consumer
request to an appropriate data center among the distributed data centers because these
consumers can satisfy with the service in terms of fast allocation time and execution
response time. Service provider offers their resources under the infrastructure as a
service model. For IaaS the service provider delivers its resources at the request of
consumers in the form of VMs. To find an appropriate data center for the consumer
request, propose an adaptive resource allocation model considers both the
geographical distance between the location of consumer and data centers and the
workload of data center. With experiment the adaptive resource allocation model
shows higher performance. An agent based test bed designed and implemented to
demonstrate the proposed adaptive resource allocation model. The test bed
implemented using JAVA with JADE (Java Agent Development framework). [9]
Dongwan Shin and Haken Akkan discussed about the “Domain based Virtual
Resource Management in Cloud Computing”. Cloud Computing enable convenient,
on-demand access to computing resource.
For satisfying various needs from user of different groups, cloud computing
provides benefits from computer security. The critical challenge in this
computing demanding advnced mechanism for protecting data and applications in
private, public and hybrid cloud, so for instance they include cloud data security
in clouds.
In this paper motivated to investigate a flexible, decentralized, and policy-driven
approach to protecting virtualized resources.
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