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Issue No.01 - First Quarter (2013 vol.6)
pp: 116-129
Ying Song , Chinese Academy of Sciences, Beijing
Yuzhong Sun , Chinese Academy of Sciences, Beijing
Weisong Shi , Wayne State University, Detroit
ABSTRACT
In a shared virtual computing environment, dynamic load changes as well as different quality requirements of applications in their lifetime give rise to dynamic and various capacity demands, which results in lower resource utilization and application quality using the existing static resource allocation. Furthermore, the total required capacities of all the hosted applications in current enterprise data centers, for example, Google, may surpass the capacities of the platform. In this paper, we argue that the existing techniques by turning on or off servers with the help of virtual machine (VM) migration is not enough. Instead, finding an optimized dynamic resource allocation method to solve the problem of on-demand resource provision for VMs is the key to improve the efficiency of data centers. However, the existing dynamic resource allocation methods only focus on either the local optimization within a server or central global optimization, limiting the efficiency of data centers. We propose a two-tiered on-demand resource allocation mechanism consisting of the local and global resource allocation with feedback to provide on-demand capacities to the concurrent applications. We model the on-demand resource allocation using optimization theory. Based on the proposed dynamic resource allocation mechanism and model, we propose a set of on-demand resource allocation algorithms. Our algorithms preferentially ensure performance of critical applications named by the data center manager when resource competition arises according to the time-varying capacity demands and the quality of applications. Using Rainbow, a Xen-based prototype we implemented, we evaluate the VM-based shared platform as well as the two-tiered on-demand resource allocation mechanism and algorithms. The experimental results show that Rainbow without dynamic resource allocation (Rainbow-NDA) provides 26 to 324 percent improvements in the application performance, as well as 26 percent higher average CPU utilization than traditional service computing framework, in which applications use exclusive servers. The two-tiered on-demand resource allocation further improves performance by 9 to 16 percent for those critical applications, 75 percent of the maximum performance improvement, introducing up to 5 percent performance degradations to others, with 1 to 5 percent improvements in the resource utilization in comparison with Rainbow-NDA.
INDEX TERMS
Resource management, Servers, Dynamic scheduling, Optimization, Heuristic algorithms, Algorithm design and analysis, Data models, model, Data centers, virtual machines, on-demand resource allocation, optimization, algorithm
CITATION
Ying Song, Yuzhong Sun, Weisong Shi, "A Two-Tiered On-Demand Resource Allocation Mechanism for VM-Based Data Centers", IEEE Transactions on Services Computing, vol.6, no. 1, pp. 116-129, First Quarter 2013, doi:10.1109/TSC.2011.41
REFERENCES
[1] P. Apparao et al., "Characterization & Analysis of a Server Consolidation Benchmark," Proc. Fourth ACM SIGPLAN/SIGOPS Int'l Conf. Virtual Execution Environments (VEE '08), pp. 21-29, Mar. 2008.
[2] K. Appleby et al., "Oceano-SLA Based Management of a Computing Utility," Proc. IFIP/IEEE Int'l Symp. Integrated Network Management, pp. 855-868, 2001.
[3] P. Barham et al., "Xen and the Art of Virtualization," Proc. 19th ACM Symp. Operating Systems Principles (SOSP), pp. 164-177, 2003.
[4] H.W. Cain et al., "An Architectural Evaluation of Java TPC-W," Proc. Seventh Int'l Symp. High-Performance Computer Architecture (HPCA), pp. 229-240, 2001.
[5] G. Chen et al., "Energy-Aware Server Provisioning and Load Dispatching for Connection-Intensive Internet Services," Proc. Fifth USENIX Symp. Networked Systems Design and Implementation (NSDI '08), pp. 337-350, 2008.
[6] L. Cherkasova, D. Gupta, and A. Vahdat, "Comparison of the Three CPU Schedulers in Xen," ACM SIGMETRICS Performance Evaluation Rev., vol. 35, no. 2, pp. 42-51, 2007.
[7] I. Cunha et al., "Self-Adaptive Capacity Management for Multi-Tier Virtualized Environments," Proc. IFIP/IEEE Int'l Symp. Integrated Network Management (IM '07), pp. 129-138, 2007.
[8] P. Dubois, "MySQL," NewRiders, Dec. 1999.
[9] R.T. Fielding and G. Kaiser, "The Apache HTTP Server Project," IEEE Internet Computing, vol. 1, no. 4, pp. 88-90, July 1997.
[10] S. Govindan, A.R. Nath, and A. Das, "Xen and Co.: Communication-Aware CPU Scheduling for Consolidated Xen-Based Hosting Platforms," Proc. ACM Third Int'l Conf. Virtual Execution Environments (VEE), pp. 126-136, 2007.
[11] Helix, http:/www.realnetworks.com, 2012.
[12] F. Hermenier et al., "Entropy: A Consolidation Manager for Clusters," Proc. ACM Int'l Conf. Virtual Execution Environments (VEE '09), pp. 41-50, 2009.
[13] M. Hines and K. Gopalan, "Post-Copy Based Live Virtual Machine Migration Using Adaptive Pre-Paging and Dynamic Self-Ballooning," Proc. ACM Int'l Conf. Virtual Execution Environments (VEE '09), pp. 51-60, 2009.
[14] "HP Utility Data Center - Technical White Paper," white paper, Hewlett-Packard, 2001.
[15] Httperf, http://www.hpl.hp.com/research/linuxhttperf /, 2012.
[16] IBM Redbook, "Advanced POWER Virtualization on IBM System p5: Introduction and Configuration," Jan. 2007.
[17] G. Jung et al., "Generating Adaptation Policies for Multi-Tier Applications in Consolidated Server Environments," Proc. Int'l Conf. Autonomic Computing (ICAC '08), pp. 23-32, 2008.
[18] M. Kallahalla et al., "SoftUDC: A Software-Based Data Center for Utility Computing," Computer, vol. 37, no. 11, pp. 38-46, Nov. 2004.
[19] N. Kandasamy et al., "A Hierarchical Optimization Framework for Autonomic Performance Management of Distributed Computing Systems," Proc. IEEE 26th Int'l Conf. Distributed Computing Systems (ICDCS), p. 9, 2006.
[20] S. Kumar and K. Schwan, "Netchannel: A VMM-Level Mechanism for Continuous, Transparent Device Access during VM Migration," Proc. ACM Fourth Int'l Conf. Virtual Execution Environments (VEE '08), 2008.
[21] D. Kusic et al., "Power and Performance Management of Virtualized Computing Environments via Lookahead Control," Proc. Int'l Conf. Autonomic Computing (ICAC), pp. 3-12, 2008.
[22] H.A. Lagar-Cavilla et al., "SnowFlock: Rapid Virtual Machine Cloning for Cloud Computing," Proc. Fourth ACM European Conf. Computer Systems (Eurosys), pp. 1-12, 2009.
[23] L.S. Lasdon, Optimization Theory for Large Systems. Courier Dover, 2002.
[24] Linpack, http://www.netlib.org/benchmarkhpl/, 2012.
[25] LVS, http:/www.linuxvirtualserver.org/, 2012.
[26] D.A. Menasc and M.N. Bennani, "Autonomic Virtualized Envi- ronments," Proc. Int'l Conf. Autonomic and Autonomous Systems (ICAS), p. 28, 2006.
[27] Microsoft, http://www.microsoft.commanagement/, 2012.
[28] M. Nelson et al., "Fast Transparent Migration for Virutal Machines," Proc. Ann. Conf. USENIX Ann. Technical Conf. (ATC), pp. 391-394, 2005.
[29] P. Padala et al., "Automated Control of Multiple Virtual Resources," Proc. Fourth ACM European Conf. Computer Systems (Eurosys), pp. 13-26, 2009.
[30] P. Padala et al., "Adaptive Control of Virtualized Resources in Utility Computing Environments," Proc. Second ACM European Conf. Computer Systems (Eurosys '07), pp. 289-302, 2007.
[31] M. Rosenblum, "VMware's Virtual Platform: A Virtual Machine Monitor for Commodity PCs," Proc. 11th Hot Chips Conf. (Hot Chips '11), 1999.
[32] H. Sandklef, "Testing Applications with Xnee," Linux J., vol. 2004, no. 117, p. 5, Jan. 2004.
[33] A. Shoykhet et al., "Virtuoso: A System for Virtual Machine Marketplaces," Technical Report NWU-CS-04-39, Dept. of Computer Science, Northwestern Univ., July 2004.
[34] Y. Song et al., "A Service-Oriented Priority-Based Resource Scheduling Scheme for Virtualized Utility Computing," Proc. Int'l Conf. High Performance Computing (HiPC), pp. 220-231, 2008.
[35] Y. Song, Y. Zhang, Y. Sun, and W. Shi, "Utility Analysis for Internet-Oriented Server Consolidation in VM-Based Data Centers," Proc. IEEE Int'l Conf. Cluster Computing and Workshops, pp. 1-10, 2009.
[36] Y. Song, H. Wang, Y. Li, B. Feng, and Y. Sun, "Multi-Tiered On-Demand Resource Scheduling for VM-Based Data Center," Proc. IEEE Ninth Int'l Symp. Cluster Computing and the Grid (CCGrid 09), pp. 148-155, May 2009.
[37] SPECweb2005, http://www.spec.orgweb2005/, 2012.
[38] T. Tannenbaum et al., "Condor - A Distributed Job Scheduler," Beowulf Cluster Computing with Linux, Thomas Sterling, ed., pp. 307-350, MIT Press, 2002.
[39] Tomcat, http:/tomcat.apache.org/, 2012.
[40] VMware, "Resource Management with VMware DRS," technical paper, 2006.
[41] C.A. Waldspurger, "Memory Resource Management in VMware ESX Server," Proc. Fifth Symp. Operating Systems Design and Implementation (OSDI '02), pp. 181-194, 2002.
[42] J. Wang, Y. Sun, and J. Fan, "Analysis on Resource Utilization Patterns of Office Computer," Proc. IASTED Int'l Conf. Parallel and Distributed Computing and Systems, pp. 626-631, 2005.
[43] Q. Wang and D. Makaroff, "Workload Characterization for an E-Commerce Web Site," Proc. Conf. Centre for Advanced Studies Conf. Collaborative Research (CASCON '03), pp. 313-327, 2003.
[44] X. Wang et al., "Appliance-Based Autonomic Provisioning Framework for Virtualized Outsourcing Data Center," Proc. IEEE Fourth Int'l Conf. Autonomic Computing (ICAC '07), p. 29, 2007.
[45] X. Wang et al., "A Resource Management Framework for Multi-Tier Service Delivery in Autonomic Virtualized Environments," Proc. IEEE Network Operations and Management Symp., pp. 310-316, 2008.
[46] Z. Wang et al., "Capacity and Performance Overhead in Dynamic Resource Allocation to Virtual Containers," Proc. IFIP/IEEE Int'l Symp. Integrated Network Management (IM '07), pp. 149-158, 2007.
[47] C. Weng, Z. Wang, M. Li, and X. Lu, "The Hybrid Scheduling Framework for Virtual Machine Systems," Proc. ACM SIGPLAN/SIGOPS Int'l Conf. Virtual Execution Environments (VEE '09), pp. 111-120, 2009.
[48] T. Wood, "Black-Box and Gray-Box Strategies for Virtual Machine Migration," Proc. Fourth USENIX Conf. Networked Systems Design and Implementation (NSDI), 2007.
[49] J. Xu et al., "On the Use of Fuzzy Modeling in Virtualized Data Center Management," Proc. Int'l Conf. Autonomic Computing (ICAC '07), p. 25, 2007.
[50] H. Yu et al., "Understanding User Behavior in Large-Scale Video-on-Demand Systems," Proc. First ACM SIGOPS/EuroSys European Conf. Computer Systems (EuroSys '06), pp. 333-344, 2006.
[51] W. Zhao and Z. Wang, "Dynamic Memory Balancing for Virtual Machines," Proc. Int'l Conf. Virtual Execution Environments (VEE '09), pp. 21-30, 2009.
[52] M. Aron, P. Druschel, and W. Zwaenepoel, "Cluster Reserves: A Mechanism for Resource Management in Cluster-Based Network Servers," Proc. ACM Joint Int'l Conf. Measurement and Modeling of Computing Systems (SIGMETRICS '00), pp. 90-101, June 2000.
[53] K. Shen, H. Tang, T. Yang, and L. Chu, "Integrated Resource Management for Cluster-Based Internet Servcies," Proc. Fifth Symp. Operating Systems Design and Implementation (OSDI '02), Dec. 2002.
[54] Y. Song et al., "An Adaptive Resource Flowing Scheme amongst VMs in a VM-Based Utility Computing," Proc. IEEE Seventh Int'l Conf. Computer and Information Technology (CIT '07), pp. 1053-1058, 2007.
[55] X. Fan, W.D. Weber, and L.A. Barroso, "Power Provisioning for a Warehouse-Sized Computer," Proc. 34th Ann. Int'l Symp. Computer Architecture (ISCA '07), pp. 13-23, 2007.
[56] R.P. Doyle, J.S. Chase, and W. Jin, "Model-Based Resource Provisioning in a Web Service Utility," Proc. Fourth Conf. USENIX Symp. Internet Technologies and Systems (USITS '03), 2003.
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