Research on CPU Workload Prediction and Balancing in Cloud Environment

Servers workload in the cloud environment should be balanced in order to achieve high efficiency and reduce resources consuming. One of the solutions is based on workload prediction, and design a proper load migration and balancing strategy. For the ease of discussion, we focus on CPU workload only in this paper. Specifically, considering the characteristics of workload, such as the strong correlation with time, we employ a time-series based two-step method to predict the CPU workload for both individual physical server and the cluster. Then, with the knowledge of the cluster workload, we design a strategy for workload migration and load balancing. Besides, we conduct extensive experiments to evaluate our method.

[1]  Bruno Schulze,et al.  Proceedings of the 7th International Workshop on Middleware for Grids, Clouds and e-Science , 2009, Middleware 2009.

[2]  Meng Wang,et al.  Consolidating virtual machines with dynamic bandwidth demand in data centers , 2011, 2011 Proceedings IEEE INFOCOM.

[3]  João Saboia.  Autoregressive Integrated Moving Average (ARIMA) Models for Birth Forecasting , 1977 .

[4]  Jerome A. Rolia,et al.  Workload Analysis and Demand Prediction of Enterprise Data Center Applications , 2007, 2007 IEEE 10th International Symposium on Workload Characterization.

[5]  Peter A. Dinda,et al.  The statistical properties of host load , 1999, Sci. Program..

[6]  Yi Zhao,et al.  Adaptive Distributed Load Balancing Algorithm Based on Live Migration of Virtual Machines in Cloud , 2009, 2009 Fifth International Joint Conference on INC, IMS and IDC.

[7]  Xifeng Yan,et al.  Workload characterization and prediction in the cloud: A multiple time series approach , 2012, 2012 IEEE Network Operations and Management Symposium.

[8]  P. Mell,et al.  The NIST Definition of Cloud Computing , 2011 .

[9]  Jin-Soo Kim,et al.  Resource Co-Allocation : A Complementary Technique that Enhances Performance in Grid Computing Environment , 2005, 11th International Conference on Parallel and Distributed Systems (ICPADS'05).

[10]  Jing Xu,et al.  Multi-Objective Virtual Machine Placement in Virtualized Data Center Environments , 2010, 2010 IEEE/ACM Int'l Conference on Green Computing and Communications & Int'l Conference on Cyber, Physical and Social Computing.

[11]  Sami Ekici,et al.  Energy and entropy-based feature extraction for locating fault on transmission lines by using neural network and wavelet packet decomposition , 2008, Expert Syst. Appl..

[12]  Johan A. K. Suykens,et al.  Least Squares Support Vector Machines , 2002 .

[13]  Foreword and Editorial International Journal of Hybrid Information Technology , 2022 .

[14]  Tram Truong Huu,et al.  An Auction-Based Resource Allocation Model for Green Cloud Computing , 2013, 2013 IEEE International Conference on Cloud Engineering (IC2E).

[15]  Weng Chuliang.  Load Balance Approach to Save Power on Cloud Datacenter , 2012 .

[16]  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..

[17]  Yongzhao Zhan,et al.  Virtualization and Cloud Computing , 2019, CompTIA® A+® Complete Practice Tests.

[18]  Rajkumar Buyya,et al.  Adaptive threshold-based approach for energy-efficient consolidation of virtual machines in cloud data centers , 2010, MGC '10.

[19]  Kai Hwang,et al.  Adaptive Workload Prediction of Grid Performance in Confidence Windows , 2010, IEEE Transactions on Parallel and Distributed Systems.

[20]  Randy H. Katz,et al.  A view of cloud computing , 2010, CACM.

[21]  Xuejie Zhang,et al.  A load balancing mechanism based on ant colony and complex network theory in open cloud computing federation , 2010, 2010 The 2nd International Conference on Industrial Mechatronics and Automation.

[22]  Chih-Chen Chang,et al.  Structural Damage Assessment Based on Wavelet Packet Transform , 2002 .

[23]  Johan A. K. Suykens,et al.  Least Squares Support Vector Machines , 2002 .

[24]  Archana Ganapathi,et al.  Statistics-driven workload modeling for the Cloud , 2010, 2010 IEEE 26th International Conference on Data Engineering Workshops (ICDEW 2010).

[25]  Byrav Ramamurthy,et al.  Scalable Web server clustering technologies , 2000, IEEE Netw..

[26]  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.