Agent based Resource Allocation Mechanism Focusing Cost Optimization in Cloud Computing

Agent based Resource Allocation Mechanism Focusing Cost Optimization in Cloud Computing

Aarti Singh (Maharishi Markendeshwar Institute of Computer Technology and Business Management, Maharishi Markandeshwar University, Haryana, India) and Manisha Malhotra (Maharishi Markendeshwar Institute of Computer Technology and Business Management, Maharishi Markandeshwar University, Haryana, India)
Copyright: © 2015 |Pages: 9
DOI: 10.4018/IJCAC.2015070104
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Abstract

A cloud computing environment offers a simplified, centralized platform or resources for use when needed on low cost. One of the key functionality of this type of computing is to allocate the resources on an individual demand. However, with the expanding requirements of cloud user, the need of efficient resource computing is also emerging. The main role of service provider is to effectively distribute and share the resources which otherwise would result into resource wastage. In addition to the user getting the appropriate service according to request, the cost of respective resource is also optimized. In order to surmount the mentioned shortcomings, this paper proposes a new agent based optimized resource assignment algorithm which is not only responsible for searching comprehensive services but also considers reducing the cost of virtual machines which are consumed by on-demand services only.
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The section throws light on the work of some renowned researchers who had been pillars and founders of the current research work.

Compiere is directing ERP software and CRM system. This organization provides an individual VM for each customer to maintain service level agreement (SLA) requirements such as response time and capacity. This induces the wastage of hardware resources which results in high infrastructure cost. However customers may not use complete VM capacity which is reserved to serve their requests. Schneider et al. Schneider et al. (2009) proposed an adaptive algorithm to adjust the level of parallelism at runtime so that the system can handle collapse data which is based on the current workload on the node. Fu et al. (2002) proposed an SLA-based dynamic scheduling algorithm of distributed resources for streaming. Gaber et al. (2009) adapted the data mining algorithm output on streaming applications according to resource availability and data arrival rate. Moreover, Yarmolenko et al. (2006) evaluated various SLA-based scheduling heuristics on parallel computing resources using resource utilization and income as evaluation metrics. Lee et al. (2010) looked into the profit impelled service request scheduling for workflow. Zheng et al. (2009) proposed binary integer programming method to solve independent optimization problems while changing multiplex strategies of initial optimal solution by minimizing their loss. The proposed algorithms are useful for linear problems not for dynamic and complex problems. From the literature survey this has been observed that there is need to pay more attention on the resource scheduling policy. The main purposes of scheduling algorithms are to minimize the resource starvation and to ensure for providing the effective and fairness resources. Scheduling deals with the problem of decision making to find out the outstanding request and which request is to be allocated resources. Yet none of algorithm exists which is effective and fair for resource scheduling in cloud computing. This work focuses on scheduling enterprise applications on VMs in cloud computing environments.

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