Context Aware Data Center Allocation in Cloud Computing Environment 
Context Aware Data Center Allocation for Mapper/Reducer Architecture
Abstract:
Both consumers and data centers of a Mapper/Reducer Provider service provider can be distributed geographically; the Mapper/Reducer Provider service provider needs to allocate each consumer request to an appropriate data center among the distributed data centers. In this project, we propose a Context aware data center allocation model which allocates the consumer’s request to an appropriate data center. The method to find a proper data center is based on six contextual parameters: 1) Geographical distance (network delay) between a consumer and data centers, 2) Workload of each data center 3)Power usage effectiveness 4)Network monitoring 5)Facility monitoring 6)Allocation delay time. The proposed model can successfully allocate the datacenter based on above mentioned parameters.
Objective or Aim:
To allocate each consumer request to an appropriate data center among the distributed data centers based on the location of consumer and the location of data centers.
Scope:
Using the proposed model can expect fast allocation time, but also future response time. The proposed model can successfully allocate the data center with minimum distance between user and data center, workload, power usage effectiveness, network traffic, allocation delay, good facility.
Problem Statement:
Mapper/Reducer Provider computing service providers deliver their resources to consumers as a service, for example, software, platform and Infrastructure. Those services are based on the demands of the consumers, and the provider offers the services to consumers through virtualized resources. The resources of the providers are usually hosted by a data center. Since the location of consumer is different in geographically, a service provider should have distributed data centers throughout the world to deliver its services. Therefore, to find an appropriate data center for a consumer request is very much necessary.
Abstract:
Both consumers and data centers of a Mapper/Reducer Provider service provider can be distributed geographically; the Mapper/Reducer Provider service provider needs to allocate each consumer request to an appropriate data center among the distributed data centers. In this project, we propose a Context aware data center allocation model which allocates the consumer’s request to an appropriate data center. The method to find a proper data center is based on six contextual parameters: 1) Geographical distance (network delay) between a consumer and data centers, 2) Workload of each data center 3)Power usage effectiveness 4)Network monitoring 5)Facility monitoring 6)Allocation delay time. The proposed model can successfully allocate the datacenter based on above mentioned parameters.
Objective or Aim:
To allocate each consumer request to an appropriate data center among the distributed data centers based on the location of consumer and the location of data centers.
Scope:
Using the proposed model can expect fast allocation time, but also future response time. The proposed model can successfully allocate the data center with minimum distance between user and data center, workload, power usage effectiveness, network traffic, allocation delay, good facility.
Problem Statement:
Mapper/Reducer Provider computing service providers deliver their resources to consumers as a service, for example, software, platform and Infrastructure. Those services are based on the demands of the consumers, and the provider offers the services to consumers through virtualized resources. The resources of the providers are usually hosted by a data center. Since the location of consumer is different in geographically, a service provider should have distributed data centers throughout the world to deliver its services. Therefore, to find an appropriate data center for a consumer request is very much necessary.
References: 3. I. Foster, Z. Yong,I. Raicu, S. Lu, "Mapper/Reducer Provider Computing and GridComputing 360-Degree Compared," Grid Computing Environments Workshop, 2008. GCE '08 , vol., no., pp.1-10, 12-16 Nov. 2008 4 5. A Survey on Resource Allocation Strategies in Mapper/Reducer Provider Computing (IJACSA) International Journal of Advanced Computer Science and Applications, Vol. 3, No.6, 2012 97 | P a g e www.ijacsa.thesai.org 6 10. Chandrashekhar S. Pawar and R.B. Wagh, A review of resource allocation policies in Mapper/Reducer Provider computing, World Journal of Science and Technology, 2(3):165-167, 2012. 11. Jiayin Li, Meikang Qiu, Jian-Wei Niu, Yu Chen, Zhong Ming,“Adaptive Resource Allocation for Pre-empt able Jobs in Mapper/Reducer Provider Systems,” in 10th International Conference on Intelligent System Design and Application, Jan. 2011, pp. 31-36. 14. C. Belady, A. Rawson, J. Pfleuger, and T. Cader, "Green grid data center power efficiency metrics: PUE and DCiE," the green grid, pp. 1-9, 2008