Cloud Adaptive Resource Allocation Mechanism for Efficient Parallel Processing

Cloud Adaptive Resource Allocation Mechanism for Efficient Parallel Processing

Manisha Malhotra (Chandigarh University, Gharuan, Mohali, India) and Rahul Malhotra (Chandigarh University, Gharuan, Mohali, India)
Copyright: © 2014 |Pages: 6
DOI: 10.4018/ijcac.2014100101
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Abstract

As cloud based services becomes more assorted, resource provisioning becomes more challenges. This is an important issue that how resource may be allocated. The cloud environment offered distinct types of virtual machines and cloud provider distribute those services. This is necessary to adjust the allocation of services with the demand of user. This paper presents an adaptive resource allocation mechanism for efficient parallel processing based on cloud. Using this mechanism the provider's job becomes easier and having the least chance for the wastage of resources and time.
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The section throws light on the work which had been already done by some researchers and organisations.

Compiere is directing ERP software and CRM system (Danny, 2011). 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. Popovici et al. (2005) looked into the profit impelled service request scheduling for workflow. Zou, et al (2010) impart on minimizing the resource consumption for serving requests. If the available resource capability is not able to complete the request before its deadline then its scheduling policies discard the service of a request. So need to improve on the scheduling policy. 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. Zheng et al (2009) proposed a binary integer programming method to solve the independent optimization and an evolutionary mechanism which change multiplex strategies of initial optimal solution with minimizing their loss. Fu Y. et al proposed an SLA-based dynamic scheduling algorithm of distributed resources for streaming. The researcher 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. Gaber et al (2009) adapt the data mining algorithm output on streaming applications according to resource availability and data arrival rate.

From the literature survey this has been observed that there is need to take much more attention on the resource scheduling algorithms. This work focuses on resource allocation mechanism which is applicable on VMs in Cloud computing environments.

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