Power aware resource allocation policy for hybrid cloud

Cloud computing is now trending and more popular in these days for the computation and adopted by many companies like google, amazon, Microsoft etc., As the cloud size increases with increase in number of data center power consumption over a data center increases. As number of request over the data center increase with increase in load and power consumption of the data center. So the requests need to be balanced in such manner which having more effective strategy for utilization of resources and reduction in power consumption. Hybrid cloud computing made it more complicated with respective to requests type that may increase or decrease power consumption. A recent survey on cloud computation shows that the power consumption of a server, increasing in a linear way due to utilization of resource (processors). Request balancing in such manner without having knowledge of load over server maximize resource utilization but also increasing power consumption at server. So to overcome these issues in cloud Infrastructure as a service (IaaS), we have proposing a load balancing algorithm to minimize the power consumption and cost over a data center. Proposed algorithm has proven to have better performance in term of load and power efficiency as compared to previously proposed load balancing algorithm for cloud IaaS.

[1]  Jing Zhao,et al.  An Improved Combined Model for the Electricity Demand Forecasting , 2010, 2010 International Conference on Computational and Information Sciences.

[2]  Jianhua Gu,et al.  A Scheduling Strategy on Load Balancing of Virtual Machine Resources in Cloud Computing Environment , 2010, 2010 3rd International Symposium on Parallel Architectures, Algorithms and Programming.

[3]  Xiaohong Jiang,et al.  Live Migration of Multiple Virtual Machines with Resource Reservation in Cloud Computing Environments , 2011, 2011 IEEE 4th International Conference on Cloud Computing.

[4]  Dan Wang,et al.  Cloud Task Scheduling Based on Load Balancing Ant Colony Optimization , 2011, 2011 Sixth Annual Chinagrid Conference.

[5]  Chen Jing,et al.  A dynamic and integrated load-balancing scheduling algorithm for Cloud datacenters , 2011, 2011 IEEE International Conference on Cloud Computing and Intelligence Systems.

[6]  Manohar Chandwani,et al.  On trust management and reliability of distributed scheduling algorithms , 2010, ICoAC 2010.

[7]  Xiaorui Wang,et al.  Server-Level Power Control , 2007, Fourth International Conference on Autonomic Computing (ICAC'07).

[8]  Wolf-Dietrich Weber,et al.  Power provisioning for a warehouse-sized computer , 2007, ISCA '07.

[9]  Rajkumar Buyya,et al.  Power-aware provisioning of Cloud resources for real-time services , 2009, MGC '09.

[10]  Richard E. Brown,et al.  Report to Congress on Server and Data Center Energy Efficiency: Public Law 109-431 , 2008 .

[11]  Luiz André Barroso,et al.  The Case for Energy-Proportional Computing , 2007, Computer.

[12]  Michael Kistler,et al.  The case for power management in web servers , 2002 .

[13]  Kuo-Qin Yan,et al.  Towards a Load Balancing in a three-level cloud computing network , 2010, 2010 3rd International Conference on Computer Science and Information Technology.