CloudEval: A Simulation Environment for Evaluating the Dynamic Cloud VM consolidation

Virtual machine(VM) scheduling algorithms optimize the energy consumption without performance degradation, where cloud providers profited greatly. However, not any algorithms performs the same. To evaluate the power efficiency of such algorithms, various scale of cloud clusters have to be maintained, which is costly and difficult. In this paper, we present a emulation software CloudEval to evalute the performance of migration algorithms of VMs. Comparing to previous cloud simulation environment, we addresses the evaluation of dynamic scheduling of the VMs in the cloud, by a simpler and a non-intrusive manner. Most previous cloud simulation environments encapsulate the simulation process in a close form(eg. the coupling of simulation and evaluation), which is not easy for researchers to design a VM scheduling algorithm and perform the evaluation. Besides, it suggests the clear test cases of workload and energy model, which is also important in VM scheduling algorithm evaluation. Specifically, we address 3 aspects in the evaluation environment: the open architecture of simulation framework for algorithm/strategy design, the evaluation metrics, the simulation cases of workload and mapping of realistic cloud environment. The framework is intended to provide references for algorithm researches.

[1]  Jingde Cheng,et al.  A Comprehensive Evaluation of Scheduling Methods of Virtual Machine Migration for Energy Conservation , 2017, IEEE Systems Journal.

[2]  Ming Zhao,et al.  Performance Modeling of Virtual Machine Live Migration , 2011, 2011 IEEE 4th International Conference on Cloud Computing.

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

[5]  Dzmitry Kliazovich,et al.  Simulation and Performance Analysis of Data Intensive and Workload Intensive Cloud Computing Data Centers , 2013 .

[6]  Hermann de Meer,et al.  Performance tradeoffs of energy-aware virtual machine consolidation , 2013, Cluster Computing.

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

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

[10]  Dario Bruneo,et al.  A Stochastic Model to Investigate Data Center Performance and QoS in IaaS Cloud Computing Systems , 2014, IEEE Transactions on Parallel and Distributed Systems.

[11]  Waltenegus Dargie,et al.  Estimation of the cost of VM migration , 2014, 2014 23rd International Conference on Computer Communication and Networks (ICCCN).

[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]  Rajkumar Buyya,et al.  NetworkCloudSim: Modelling Parallel Applications in Cloud Simulations , 2011, 2011 Fourth IEEE International Conference on Utility and Cloud Computing.

[14]  Wei Wang,et al.  Study of a Virtual Machine Migration Method , 2013, 2013 International Conference on Advanced Cloud and Big Data.

[15]  James J. Filliben,et al.  Comparing VM-Placement Algorithms for On-Demand Clouds , 2011, CloudCom.

[16]  Alexander Schill,et al.  Power Consumption Estimation Models for Processors, Virtual Machines, and Servers , 2014, IEEE Transactions on Parallel and Distributed Systems.

[17]  Dror G. Feitelson,et al.  Workload Modeling for Performance Evaluation , 2002, Performance.