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Group Based Resource Management and Pricing Model in Cloud Computing


Affiliations
1 Institute of Information Technology, Jahangirnagar University, Dhaka, Bangladesh
 

Cloud computing utilizes large scale computing infrastructure that has been radically changing the IT landscape enabling remote access to computing resources with low service cost, high scalability , availability and accessibility. Serving tasks from multiple users where the tasks are of different characteristics with variation in the requirement of computing power may cause under or over utilization of resources. Therefore maintaining such mega-scale datacenter requires efficient resource management procedure to increase resource utilization. However, while maintaining efficiency in service provisioning it is necessary to ensure the maximization of profit for the cloud providers. Most of the current research works aims at how providers can offer efficient service provisioning to the user and improving system performance. There are comparatively fewer specific works regarding resource management which also deals with the economic section that considers profit maximization for the provider. In this paper we represent a model that deals with both efficient resource utilization and pricing of the resources. The joint resource management model combines the work of user assignment, task scheduling and load balancing on the fact of CPU power endorsement. We propose four algorithms respectively for user assignment, task scheduling, load balancing and pricing that works on group based resources offering reduction in task execution time(56.3%), activated physical machines(41.44%),provisioning cost(23%). The cost is calculated over a time interval involving the number of served customer at this time and the amount of resources used within this time.

Keywords

Resource Management, Resource Pricing, Task Execution, Load Balancing, Task Scheduling.
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  • Group Based Resource Management and Pricing Model in Cloud Computing

Abstract Views: 389  |  PDF Views: 159

Authors

Shelia Rahman
Institute of Information Technology, Jahangirnagar University, Dhaka, Bangladesh
Afroza Sultana
Institute of Information Technology, Jahangirnagar University, Dhaka, Bangladesh
Afsana Islam
Institute of Information Technology, Jahangirnagar University, Dhaka, Bangladesh
Md. Whaiduzzaman
Institute of Information Technology, Jahangirnagar University, Dhaka, Bangladesh

Abstract


Cloud computing utilizes large scale computing infrastructure that has been radically changing the IT landscape enabling remote access to computing resources with low service cost, high scalability , availability and accessibility. Serving tasks from multiple users where the tasks are of different characteristics with variation in the requirement of computing power may cause under or over utilization of resources. Therefore maintaining such mega-scale datacenter requires efficient resource management procedure to increase resource utilization. However, while maintaining efficiency in service provisioning it is necessary to ensure the maximization of profit for the cloud providers. Most of the current research works aims at how providers can offer efficient service provisioning to the user and improving system performance. There are comparatively fewer specific works regarding resource management which also deals with the economic section that considers profit maximization for the provider. In this paper we represent a model that deals with both efficient resource utilization and pricing of the resources. The joint resource management model combines the work of user assignment, task scheduling and load balancing on the fact of CPU power endorsement. We propose four algorithms respectively for user assignment, task scheduling, load balancing and pricing that works on group based resources offering reduction in task execution time(56.3%), activated physical machines(41.44%),provisioning cost(23%). The cost is calculated over a time interval involving the number of served customer at this time and the amount of resources used within this time.

Keywords


Resource Management, Resource Pricing, Task Execution, Load Balancing, Task Scheduling.

References