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Issue No.02 - February (2012 vol.23)
pp: 296-303
Jianfeng Zhan , Chinese Academy of Sciences, Beijing
Lei Wang , Chinese Academy of Sciences, Beijing
Yi Liang , Beijing University of Technology, Beijing
The basic idea behind cloud computing is that resource providers offer elastic resources to end users. In this paper, we intend to answer one key question to the success of cloud computing: in cloud, can small-to-medium scale scientific communities benefit from the economies of scale? Our research contributions are threefold: first, we propose an innovative public cloud usage model for small-to-medium scale scientific communities to utilize elastic resources on a public cloud site while maintaining their flexible system controls, i.e., create, activate, suspend, resume, deactivate, and destroy their high-level management entities—service management layers without knowing the details of management. Second, we design and implement an innovative system—DawningCloud, at the core of which are lightweight service management layers running on top of a common management service framework. The common management service framework of DawningCloud not only facilitates building lightweight service management layers for heterogeneous workloads, but also makes their management tasks simple. Third, we evaluate the systems comprehensively using both emulation and real experiments. We found that for four traces of two typical scientific workloads: High-Throughput Computing (HTC) and Many-Task Computing (MTC), DawningCloud saves the resource consumption maximally by 59.5 and 72.6 percent for HTC and MTC service providers, respectively, and saves the total resource consumption maximally by 54 percent for the resource provider with respect to the previous two public cloud solutions. To this end, we conclude that small-to-medium scale scientific communities indeed can benefit from the economies of scale of public clouds with the support of the enabling system.
Cloud, scientific communities, economies of scale, many-task computing, and high-throughput computing.
Jianfeng Zhan, Lei Wang, Yi Liang, "In Cloud, Can Scientific Communities Benefit from the Economies of Scale?", IEEE Transactions on Parallel & Distributed Systems, vol.23, no. 2, pp. 296-303, February 2012, doi:10.1109/TPDS.2011.144
[1] I. Raicu et al., "Many-Task Computing for Grids and Supercomputers," Proc. Workshop Many-Task Computing for Grids and Supercomputers (MTAGS), 2008.
[2] M. Armbrust et al., "Above the Clouds: A Berkeley View of Cloud Computing," Technical Report UCB/EECS-2009-28, UC Berkeley, 2009.
[3] C. Evangelinos et al., "Cloud Computing for Parallel Scientific HPC Applications: Feasibility of Running Coupled Atmosphere-Ocean Climate Models on Amazons EC2," Proc. Workshop Cloud Computing and Its Application (CCA '08), 2008.
[4] S.L. Garfinkel et al., "Commodity Grid Computing with Amazon's S3 and EC2," Login: The USENIX Magazine, vol. 32, pp. 7-13, 2007.
[5] E. Deelman et al., "The Cost of Doing Science on the Cloud: The Montage Example," Proc. ACM/IEEE Conf. Supercomputing (SC), 2008.
[6] K. Gaj et al., "Performance Evaluation of Selected Job Management Systems," Proc. 16th Int'l Parallel and Distributed Processing Symp. (IPDPS), 2002.
[7] M. Livny et al., "Mechanisms for High Throughput Computing," SPEEDUP J., vol. 11, 1997.
[8] J. Zhan et al., "Phoenix Cloud: Consolidating Different Computing Loads on Shared Cluster System for Large Organization," Proc. Workshop Cloud Computing and Its Application (CCA '08), 2008.
[9] B. Rochwerger et al., "The Reservoir Model and Architecture for Open Federated Cloud Computing," IBM J. Research and Development, vol. 53, no. 4, pp. 535-545, 2009.
[10] R.S. Montero et al., "Dynamic Deployment of Custom Execution Environments in Grids," Proc. Second Int'l Conf. Advanced Eng. Computing and Applications in Sciences (ADVCOMP '08), pp. 33-38, 2008.
[11] B. Sotomayor et al., "Combining Batch Execution and Leasing Using Virtual Machines," Proc. 17th Int'l Symp. High Performance Distributed Computing (HPDC), pp. 87-96, 2008.
[12] R. Buyya et al., "Cloud Computing and Emerging IT Platforms: Vision, Hype, and Reality for Delivering Computing as the Fifth Utility," Future Generation Computer Systems, vol. 25, no. 6, pp. 599-616, 2009.
[13] L. Wang et al., "In Cloud, Do MTC or HTC Service Providers Benefit from the Economies of Scale?," Proc. Workshop Many-Task Computing on Grids and Supercomputers (MTAGS '09), 2009.
[14] R. Moreno-Vozmediano et al., "Elastic Management of Cluster-Based Services in the Cloud," Proc. Workshop Automated Control for Datacenters and Clouds (ACDC '09), pp. 19-24, 2009.
[15] P. Marshall et al., "Using Clouds to Elastically Extend Site Resources," Proc. IEEE/ACM Int'l Conf. Cluster Cloud and Grid Computing (CCGrid '10), 2010.
[16] M.D. de Assunção et al., "Evaluating the Cost-Benefit of Using Cloud Computing to Extend the Capacity of Clusters," Proc. 18th ACM Int'l Symp. High Performance Distributed Computing (HPDC), pp. 141-150, 2009.
[17] B. Sotomayor et al., "Virtual Infrastructure Management in Private and Hybrid Clouds," IEEE Internet Computing, vol. 13, no. 5, pp. 14-22, Sept. 2009.
[18] M.R. Palankar et al., "Amazon S3 for Science Grids: A Viable Solution?," Proc. Int'l Workshop Data-Aware Distributed Computing (DADC '08), pp. 55-64, 2008.
[19] J. Zhan et al., "Fire Phoenix Cluster Operating System Kernel and Its Evaluation," Proc. IEEE Int'l Cluster Computing, 2005.
[20] J. Zhan et al., "The Design Methodology of Phoenix Cluster System Software Stack," Proc. Third Workshop High-Performance Computing in China (CHINA HPC '07), pp. 174-182, 2005.
[21] Z. Zhan et al., "Easy and Reliable Cluster Management: The Self-Management Experience of Fire Phoenix," Proc. Int'l Parallel and Distributed Processing Symp. (IPDPS), 2006.
[22] C. Evangelinos et al., "Many Task Computing for Multidisciplinary Ocean Sciences: Real-Time Uncertainty Prediction and Data Assimilation," Proc. Workshop Many-Task Computing on Grids and Supercomputers (MTAGS '09), 2009.
[23] B. Sotomayor et al., "F. Capacity Leasing in Cloud Systems Using the Opennebula Engine," Proc. Cloud Computing and Applications (CCA '08), 2008.
[24] P. Wang et al., "Transformer: A New Paradigm for Building Data-Parallel Programming Models," IEEE Micro, vol. 30, no. 4, pp. 55-64, July/Aug. 2010.
[25] E. Walker et al., "The Real Cost of a CPU Hour," Computer, vol. 42, no. 4, pp. 35-41, Apr. 2009.
[26] F. Galn et al., "Service Specification in Cloud Environments Based on Extensions to Open Standards," Proc. Int'l ICST Conf. Comm. System Software and Middleware (COMSWARE '09), pp. 1-12, 2009.
[27] L. Rodero-Merino et al., "From Infrastructure Delivery to Service Management in Clouds," Future Generation Computer Systems, vol. 26, no. 8, pp. 1226-1240, Oct. 2010.
[28] A. Sullivan et al., Economics: Principles in Action, p. 157. Pearson Prentice Hall, 2003.
[29] B. Hindman et al., "Mesos: A Platform for Fine-Grained Resource Sharing in the Data Center," Technical Report UCB/EECS-2010-87, UC Berkeley, 2010.
[30] R. Calheiros et al., "CloudSim: A Novel Framework for Modeling and Simulation of Cloud Computing Infrastructures and Services," Proc. ICPP' 09, 2009.
[31] A. Verma et al., "Power-Aware Dynamic Placement of HPC Applications," Proc. Ann. Int'l Conf. Supercomputing (ICS '08), 2008.
[32] W. Huang et al., "A Case for High Performance Computing with Virtual Machines," Proc. Ann. Int'l Conf. Supercomputing (ICS '06), 2006.
[33] W. Zhou et al., "Scalable Group Management in Large-Scale Virtualized Clusters," To appear in the J. High Technology Letters,, 2011.
[34] J. Dean et al., "MapReduce: Simplified Data Processing on Large Clusters," Proc. Symp. Operating Systems Principles (SOSP '04), pp. 137-150, 2004.
[35] M. Isard et al., "Dryad: Distributed Data-Parallel Programs from Sequential Building Blocks," ACM SIGOPS Operating Systems Rev., vol. 41, no. 3, pp. 59-72, 2007.
[36] C. Moretti et al., "All-Pairs: An Abstraction for Data-Intensive Cloud Computing," Proc. IEEE Int'l Symp. Parallel and Distributed Processing (IPDPS '08), pp. 1-11, 2008.
[37] D. Wentzlaff et al., "An Operating System for Multicore and Clouds: Mechanisms and Implementation," Proc. ACM Symp. Cloud Computing (SoCC '10), pp. 3-14, 2010.
[38] L. Wang et al., "In Cloud, Can Scientific Communities Benefit from the Economies of Scale?," technical report,, 2010.
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