Heuristic based Energy-aware Resource Allocation by Dynamic Consolidation of Virtual Machines in Cloud Data Center

Rapid growth of the IT industry has led to significant energy consumption in the last decade. Data centers swallow an enormous amount of electrical energy and have high operating costs and carbon dioxide excretions. In response to this, the dynamic consolidation of virtual machines (VMs) allows for efficient resource management and reduces power consumption through the live migration of VMs in the hosts. Moreover, each client typically has a service level agreement (SLA), this leads to stipulations in dealing with energy-performance trade-offs, as aggressive consolidation may lead to performance degradation beyond the negotiation. In this paper we propose a heuristic based resource allocation of VM selection and a VM allocation approach that aims to minimize the total energy consumption and operating costs while meeting the client-level SLA. Our experiment results demonstrate significant enhancements in cloud providers’ profit and energy savings while improving the SLA at a certain level.

[1]  Karsten Schwan,et al.  VirtualPower: coordinated power management in virtualized enterprise systems , 2007, SOSP.

[2]  George Markowsky,et al.  Multidimensional Bin Packing Algorithms , 1977, IBM J. Res. Dev..

[3]  Jeffrey M. Galloway,et al.  Power Aware Load Balancing for Cloud Computing , 2011 .

[4]  Jerome A. Rolia,et al.  Resource pool management: Reactive versus proactive or let's be friends , 2009, Comput. Networks.

[5]  Massoud Pedram,et al.  SLA-based Optimization of Power and Migration Cost in Cloud Computing , 2012, 2012 12th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (ccgrid 2012).

[6]  KyoungSoo Park,et al.  CoMon: a mostly-scalable monitoring system for PlanetLab , 2006, OPSR.

[7]  César A. F. De Rose,et al.  Server consolidation with migration control for virtualized data centers , 2011, Future Gener. Comput. Syst..

[8]  Borja Sotomayor,et al.  Virtual Infrastructure Management in Private and Hybrid Clouds , 2009, IEEE Internet Computing.

[9]  Andrzej Kochut,et al.  Dynamic Placement of Virtual Machines for Managing SLA Violations , 2007, 2007 10th IFIP/IEEE International Symposium on Integrated Network Management.

[10]  Akshat Verma,et al.  pMapper: Power and Migration Cost Aware Application Placement in Virtualized Systems , 2008, Middleware.

[11]  Arun Venkataramani,et al.  Sandpiper: Black-box and gray-box resource management for virtual machines , 2009, Comput. Networks.

[12]  P. Rousseeuw,et al.  Alternatives to the Median Absolute Deviation , 1993 .

[13]  Jose Renato Santos,et al.  JustRunIt: Experiment-Based Management of Virtualized Data Centers , 2009, USENIX Annual Technical Conference.

[14]  Vanish Talwar,et al.  No "power" struggles: coordinated multi-level power management for the data center , 2008, ASPLOS.

[15]  J. Koomey.  Worldwide electricity used in data centers , 2008 .

[16]  Andrew Warfield,et al.  Live migration of virtual machines , 2005, NSDI.

[17]  Jordi Torres,et al.  Energy-efficient and multifaceted resource management for profit-driven virtualized data centers , 2012, Future Gener. Comput. Syst..

[18]  Nagarajan Kandasamy,et al.  Power and performance management of virtualized computing environments via lookahead control , 2008, 2008 International Conference on Autonomic Computing.

[19]  Tao Chen,et al.  Robust Backup Path Selection in Overlay Routing with Bloom Filters , 2013, KSII Trans. Internet Inf. Syst..

[20]  David M Erceg-Hurn,et al.  Modern robust statistical methods: an easy way to maximize the accuracy and power of your research. , 2008, The American psychologist.

[21]  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.

[22]  Aameek Singh,et al.  Shares and utilities based power consolidation in virtualized server environments , 2009, 2009 IFIP/IEEE International Symposium on Integrated Network Management.

[23]  Yong Dou,et al.  From WiFi to WiMAX: Efficient GPU-based Parameterized Transceiver across Different OFDM Protocols , 2013, KSII Trans. Internet Inf. Syst..

[24]  Henri Casanova,et al.  Resource allocation algorithms for virtualized service hosting platforms , 2010, J. Parallel Distributed Comput..

[25]  Rajkumar Buyya,et al.  CloudSim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms , 2011, Softw. Pract. Exp..

[26]  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.

[27]  Rajkumar Buyya,et al.  Energy-aware resource allocation heuristics for efficient management of data centers for Cloud computing , 2012, Future Gener. Comput. Syst..

[28]  Xiaoyun Zhu,et al.  1000 Islands: Integrated Capacity and Workload Management for the Next Generation Data Center , 2008, 2008 International Conference on Autonomic Computing.

[29]  Navendu Jain,et al.  Managing cost, performance, and reliability tradeoffs for energy-aware server provisioning , 2011, 2011 Proceedings IEEE INFOCOM.

[30]  Rajkumar Buyya,et al.  Optimal online deterministic algorithms and adaptive heuristics for energy and performance efficient dynamic consolidation of virtual machines in Cloud data centers , 2012, Concurr. Comput. Pract. Exp..