Adaptive Power Panel of Cloud Computing Controlling Cloud Power Consumption

Cloud computing had created a new era of network design, where end-users can get their required services without having to purchase expensive infrastructure or even to care about troubleshooting. Power consumption is a challenge facing the Cloud Providers to operate their Datacenters. One solution to overcome this is the Virtual Machine (VM) migration, which is a technique used to switch under-utilized hosts to sleep mode in order to save power, and to avoid over-utilized hosts from Service Level Agreement (SLA) violation. But still the problem is that the Cloud Service Provider apply a single policy on all nodes. Our proposed solution is an adaptive power panel where different policies can be applied based on both of the nature of the tasks running on hosts, and the Cloud Provider decision.

[1]  Ziming Zhang,et al.  An adaptive power management framework for autonomic resource configuration in cloud computing infrastructures , 2012, 2012 IEEE 31st International Performance Computing and Communications Conference (IPCCC).

[2]  A. Anasuya Threse Innocent Cloud Infrastructure Service Management - A Review , 2012, ArXiv.

[3]  J. Enrique Muñoz Expósito,et al.  Fuzzy Rule-Based Systems for Optimizing Power Consumption in Data Centers , 2013, IP&C.

[4]  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..

[5]  Rajkumar Buyya,et al.  CloudSim: A Novel Framework for Modeling and Simulation of Cloud Computing Infrastructures and Services , 2009, ArXiv.

[6]  Rajkumar Buyya,et al.  Modeling and simulation of scalable Cloud computing environments and the CloudSim toolkit: Challenges and opportunities , 2009, 2009 International Conference on High Performance Computing & Simulation.

[7]  Hai Li,et al.  VSV: L2-miss-driven variable supply-voltage scaling for low power , 2003, Proceedings. 36th Annual IEEE/ACM International Symposium on Microarchitecture, 2003. MICRO-36..

[8]  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..

[9]  Shrisha Rao,et al.  Power-Aware Cloud Metering , 2014, IEEE Transactions on Services Computing.

[10]  Yousra Alkabani,et al.  Green cloud computing: Datacenters power management policies and algorithms , 2014, 2014 9th International Conference on Computer Engineering & Systems (ICCES).

[11]  Rajkumar Buyya,et al.  Article in Press Future Generation Computer Systems ( ) – Future Generation Computer Systems Cloud Computing and Emerging It Platforms: Vision, Hype, and Reality for Delivering Computing as the 5th Utility , 2022 .

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

[13]  Enrique V. Carrera,et al.  Load balancing and unbalancing for power and performance in cluster-based systems , 2001 .

[14]  Raouf Boutaba,et al.  Virtualization in the Cloud , 2015 .

[15]  Ahmad Ghafarian Foreniscs analysis of cloud computing services , 2015, 2015 Science and Information Conference (SAI).

[16]  E. N. Elnozahy,et al.  Energy-Efficient Server Clusters , 2002, PACS.

[17]  Kaushik Roy,et al.  VSV: L2-Miss-Driven Variable Supply-Voltage Scaling for Low Power , 2003, MICRO.

[18]  Poulami Dalapati,et al.  Green Solution for Cloud Computing with Load Balancing and Power Consumption Management , 2013 .

[19]  Sherif Sakr,et al.  SLA-Based and Consumer-centric Dynamic Provisioning for Cloud Databases , 2012, 2012 IEEE Fifth International Conference on Cloud Computing.

[20]  Yousra Alkabani,et al.  Energy efficient resource management for Cloud Computing Environment , 2014, 2014 9th International Conference on Computer Engineering & Systems (ICCES).