Penalty‐aware and cost‐efficient resource management in cloud data centers

Summary Today, data centers are the main source of providing cloud services through a service level agreement (SLA). Most research papers for cloud resource management concentrate on how to reduce host energy consumption and SLA violation (SLAV) to minimize operational cost. However, they do not consider the amount of penalty that cloud provider should pay to users because of SLAV. In this paper, we propose a new penalty-aware and cost-efficient method that considers cloud resource management as a cost problem. In this method parameters such as user budget, penalty, and host energy consumption cost play an important role in minimizing operational cost which leads to higher profit for cloud provider. The simulation results with CloudSim show that our proposed method minimizes operational cost compared to the prior resource managements. Copyright © 2016 John Wiley & Sons, Ltd.

[1]  Abbas Horri,et al.  Novel resource allocation algorithms to performance and energy efficiency in cloud computing , 2014, The Journal of Supercomputing.

[2]  Gargi Dasgupta,et al.  Server Workload Analysis for Power Minimization using Consolidation , 2009, USENIX Annual Technical Conference.

[3]  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..

[4]  Li Yi,et al.  A secure cost-effective migration of enterprise applications to the cloud , 2014, Int. J. Commun. Syst..

[5]  Saeed Sharifian,et al.  Novel energy and SLA efficient resource management heuristics for consolidation of virtual machines in cloud data centers , 2015, Comput. Electr. Eng..

[6]  Rajkumar Buyya,et al.  Managing Overloaded Hosts for Dynamic Consolidation of Virtual Machines in Cloud Data Centers under Quality of Service Constraints , 2013, IEEE Transactions on Parallel and Distributed Systems.

[7]  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..

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

[9]  Jordi Guitart,et al.  Assessing and forecasting energy efficiency on Cloud computing platforms , 2015, Future Gener. Comput. Syst..

[10]  W. Cleveland Robust Locally Weighted Regression and Smoothing Scatterplots , 1979 .

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

[12]  Rajkumar Buyya,et al.  SLA-oriented resource provisioning for cloud computing: Challenges, architecture, and solutions , 2011, 2011 International Conference on Cloud and Service Computing.

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

[14]  Ren-Hung Hwang,et al.  An energy-saving algorithm for cloud resource management using a Kalman filter , 2014, Int. J. Commun. Syst..

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

[16]  Wei Wang,et al.  Stochastic modeling of dynamic power management policies in server farms with setup times and server failures , 2014, Int. J. Commun. Syst..

[17]  Albert Y. Zomaya,et al.  A Taxonomy and Survey of Energy-Efficient Data Centers and Cloud Computing Systems , 2010, Adv. Comput..