Energy aware cloud application management in private cloud data center

Cloud services decouple cloud applications from IT infrastructure in cloud environment. On demand resources provisioning pattern makes high efficient resource utility and application dynamic scaling possible. Hence Cloud data center could provide infrastructure capabilities in a more energy efficient way. However, there exists a tradeoff between energy efficiency and application service level objectives. It is challenging to realize effective energy consumption and energy efficiency aware autonomic cloud application service level agreement assurance mechanism. In this paper, we propose energy aware cloud application management architecture for private cloud data center. Furthermore we present a cloud application and power management model. In order to metering server energy utility efficiency and cloud applications' total energy consumption of running on a specific group of servers, we define related measurement metrics. The objective of our approach is to reduce data center's total energy efficiency by controlling cloud applications' overall energy consumption while ensuring cloud applications' service level agreement. The experiment results show that the cloud application energy consumption and energy efficiency is being improved effectively.

[1]  Shuang Wu,et al.  Virtual Machine Based Energy-Efficient Data Center Architecture for Cloud Computing: A Performance Perspective , 2010, 2010 IEEE/ACM Int'l Conference on Green Computing and Communications & Int'l Conference on Cyber, Physical and Social Computing.

[2]  Waheed Iqbal,et al.  SLA-Driven Dynamic Resource Management for Multi-tier Web Applications in a Cloud , 2010, 2010 10th IEEE/ACM International Conference on Cluster, Cloud and Grid Computing.

[3]  Gregor von Laszewski,et al.  Efficient resource management for Cloud computing environments , 2010, International Conference on Green Computing.

[4]  Jean-Marc Menaud,et al.  SLA-Aware Virtual Resource Management for Cloud Infrastructures , 2009, 2009 Ninth IEEE International Conference on Computer and Information Technology.

[5]  Azizah Abdul Rahman,et al.  Server Consolidation: An Approach to make Data Centers Energy Efficient and Green , 2010, ArXiv.

[6]  René W. Schmidt,et al.  vApp: a standards-based container for cloud providers , 2010, OPSR.

[7]  Zhoujun Li,et al.  Adaptive Management of Virtualized Resources in Cloud Computing Using Feedback Control , 2009, 2009 First International Conference on Information Science and Engineering.

[8]  Massoud Pedram,et al.  Dynamic voltage and frequency scaling based on workload decomposition , 2004, Proceedings of the 2004 International Symposium on Low Power Electronics and Design (IEEE Cat. No.04TH8758).

[9]  Xi He,et al.  Power-aware scheduling of virtual machines in DVFS-enabled clusters , 2009, 2009 IEEE International Conference on Cluster Computing and Workshops.

[10]  Rajkumar Buyya,et al.  Energy Efficient Allocation of Virtual Machines in Cloud Data Centers , 2010, 2010 10th IEEE/ACM International Conference on Cluster, Cloud and Grid Computing.