Abstract
Opportunistic peer-to-peer (P2P) Grids are distributed computing infrastructures that harvest the idle computing cycles of computing resources geographically distributed. In these Grids, the demand for resources is typically bursty. During bursts of resource demand, many Grid resources are required, but on other occasions they may remain idle for long periods of time. If the resources are kept powered on even when they are neither processing their owners’ workload nor Grid jobs, their exploitation is not efficient in terms of energy consumption. One way to reduce the energy consumed in these idleness periods is to place the computers that form the Grid in a “sleeping” state which consumes less energy. In Grid computing, this strategy introduces a tradeoff between the benefit of energy saving and the associated costs in terms of increasing the job response time, also known as makespan, and reducing the hard disks’ lifetime. To mitigate these costs, it is usually introduced a timeout policy together with the sleeping state, which tries to avoid useless state transitions. In this work, we use simulations to analyze the potential of using sleeping states to save energy in each site of a P2P Grid. Our results show that sleeping states can save energy with low associated impact on jobs’ makespan and hard disks’ lifetime. Furthermore, the best sleeping strategy to be used depends on the characteristics of each individual site, thus, each site should be configured to use the sleeping strategy that best fits its characteristics. Finally, differently from other kinds of Grid infrastructures, P2P Grids can place a machine in sleeping mode as soon as it becomes idle, i.e. it is not necessary to use an aggressive timeout policy. This allows increases on the Grid’s energy saving without impacting significantly the jobs’ makespan and the disks’ lifetime.
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Advanced Micro Devices (AMD): Processor tech. support.amd.com/us/Processor_TechDocs/43375.pdf, online July 2011
Advanced Micro Devices, Inc.: Magic packet technology (1995). http://support.amd.com/us/Embedded_TechDocs/20213.pdf, online July 2011
Andrade, N., Brasileiro, F., Cirne, W., Mowbray, M.: Automatic Grid assembly by promoting collaboration in peer-to-peer Grids. J. Parallel Distrib. Comput. 67(8), 957–966 (2007)
Augustine, J., Irani, S., Swamy, C.: Optimal power-down strategies. In: Proceedings of the 45th Annual IEEE Symposium on Foundations of Computer Science, pp. 530–539. IEEE Computer Society, Washington, DC (2004)
Baliga, B.J., Ayre, R.W.A., Hinton, K., Tucker, R.S.: Green cloud computing: balancing energy in processing, storage, and transport. Proc. IEEE 99(1), 149–167 (2011)
Benini, L., Bogliolo, A., De Micheli, G.: A survey of design techniques for system-level dynamic power management. IEEE T VLSI Syst. 8(3), 299–316 (2000)
Berl, A., Race, N., Ishmael, J., de Meer, H.: Network virtualization in energy-efficient office environments. Comput. Netw. 54, 2856–2868 (2010)
Bertis, V., Bolze, R., Desprez, F., Reed, K.: From dedicated Grid to volunteer Grid: large scale execution of a bioinformatics application. J. Grid Computing 7, 463–478 (2009). doi:10.1007/s10723-009-9130-7
Bolla, R., Bruschi, R., Davoli, F., Cucchietti, F.: Energy efficiency in the future internet: a survey of existing approaches and trends in energy-aware fixed network infrastructures. IEEE Commun. Surv. Tutor. 13(2), 223–244 (2011)
Brasileiro, F., Andrade, N., Lopes, R., Sampaio, L.: Democratizing resource-intensive e-science through peer-to-peer Grid computing. In: Yang, X., Wang, L., Jie, W. (eds.) Guide to e-Science, Computer Communications and Networks, pp. 53–80. Springer, London (2011)
Cirne, W., Brasileiro, F., Andrade, N., Costa, L., Andrade, A., Novaes, R., Mowbray, M.: Labs of the world, unite!!! J. Grid Computing 4(3), 225–246 (2006)
Cirne, W., Brasileiro, F., Paranhos, D., Góes, L.F.W., Voorsluys, W.: On the efficacy, efficiency and emergent behavior of task replication in large distributed systems. Parallel Comput. 33(3), 213–234 (2007)
Garg, S.K., Buyya, R.: Exploiting heterogeneity in Grid computing for energy-efficient resource allocation. In: Proceedings of the 17th International Conference on Advanced Computing and Communications (2009)
Hewlett-Packard Corporation, Intel Corporation, Microsoft Corporation, Phoenix Technologies Ltd. and Toshiba Corporation: Advanced configuration and power interface specification. http://www.acpi.info/spec.htm, online July 2011
Intel and U.S. Environmental Protection Agency: Energy star* system implementation whitepaper. www.intel.com/cd/channel/reseller/asmo-na/eng/339085.htm, online July 2011
Iosup, A., Dumitrescu, C., Epema, D., Li, H., Wolters, L.: How are real Grids used? The analysis of four Grid traces and its implications. In: Proceedings of the 7th IEEE/ACM International Conference on Grid Computing, pp. 262–269. IEEE Computer Society, Washington, DC (2006)
Iosup, A., Sonmez, O., Anoep, S., Epema, D.: The performance of bags-of-tasks in large-scale distributed systems. In: Proceedings of the 17th international symposium on high performance distributed computing, pp. 97–108. ACM, New York (2008)
Kondo, D., Chien, A.A., Casanova, H.: Resource management for rapid application turnaround on enterprise desktop Grids. In: Proceedings of the 2004 ACM/IEEE conference on Supercomputing, SC ’04, pp. 1–14. IEEE Computer Society, Washington, DC (2004)
Kondo, D., Taufer, M., Brooks III, C.L., Casanova, H., Chien, A.A.: Characterizing and evaluating desktop Grids: an empirical study. In: Proceedings of the 18th International Parallel and Distributed Processing Symposium (2004)
Lammie, M., Thain, D., Brenner, P.: Scheduling Grid workloads on multicore clusters to minimize energy and maximize performance. In: 10th IEEE/ACM International Conference on IEEE Grid Computing, pp. 145–152 (2009)
Lu, Y.H., de Micheli, G.: Adaptive hard disk power management on personal computers. In: Proceedings of the Ninth Great Lakes Symposium on VLSI, GLS’99, pp. 1–4. IEEE Computer Society, Washington, DC (1999)
Lublin, U., Feitelson, D.G.: The workload on parallel supercomputers: modeling the characteristics of rigid jobs. J. Parallel Distrib. Comput. 63, 1105–1122 (2003)
Microsoft Corporation: Windows power management. http://www.microsoft.com/whdc/archive/winpowmgmt.mspx, online July 2011
Opitz, A., König, H., Szamlewska, S.: What does Grid computing cost? J. Grid Computing 6, 385–397 (2008). doi:10.1007/s10723-008-9098-8
Orgerie, A.C., Lefevre, L., Gelas, J.P.: Chasing gaps between bursts: towards energy efficient large scale experimental Grids. In: International Conference on Parallel and Distributed Computing Applications and Technologies, pp. 381–389. IEEE Computer Society, Los Alamitos (2008)
Pallipadi, V., Starikovskiy, A.: The ondemand governor: past, present and future. In: Proceedings of Linux Symposium, vol. 2, pp. 223–238 (2006)
Pinheiro, E., Bianchini, R., Carrera, E.V., Heath, T.: Dynamic cluster reconfiguration for power and performance, pp. 75–93. Kluwer Academic, Norwell (2003)
Ponciano, L., Brasileiro, F.: On the impact of energy-saving strategies in opportunistic Grids. In: Energy Efficient Grids, Clouds and Clusters Workshop, Proceedings. 11th ACM-IEEE International Conference on Grid Computing (Grid 2010), pp. 282–289. IEEE Computer Society, Brussels (2010)
Ren, X., Lee, S., Eigenmann, R., Bagchi, S.: Prediction of resource availability in fine-grained cycle sharing systems empirical evaluation. J. Grid Computing 5, 173–195 (2007). doi:10.1007/s10723-007-9077-5
Rood, B., Lewis, M.J.: Multi-state Grid resource availability characterization. In: 8th Grid Computing Conference (2007)
Schott, B., Emmen, A.: Green desktop-Grids: scientific impact, carbon footprint, power usage efficiency. Int. J. Scalable Comput. Practice Exp. 12(2), 257–264 (2011)
Seagate Technology LLC: Product manual: St31000524as, st3750525as, st3500413as, st3320413as, st3250312as, st3160316as. http://www.seagate.com/www/en-us/support/document_library, online August 2011
Sharma, K., Aggarwal, S.: Energy aware scheduling on desktop Grid environment with static performance prediction. In: Proceedings of the 2009 Spring Simulation Multiconference, pp. 1–8. Society for Computer Simulation International, San Diego (2009)
Trivedi, K.S.: Probability and Statistics with Reliability, Queuing and Computer Science Applications, 2nd edn. Wiley, Chichester (2002)
Xie, T., Sun, Y.: Understanding the relationship between energy conservation and reliability in parallel disk arrays. J. Parallel Distrib. Comput. 71, 198–210 (2011)
Yigitbasi, N., Gallet, M., Kondo, D., Iosup, A., Epema, D.: Analysis and modeling of time-correlated failures in large-scale distributed systems. In: 8th IEEE/ACM International Conference on Grid Computing (2010)
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This work has been funded by CNPq/Brazil (grants 560262/2010-8 and 305858/2010-6) and the European Commission through the DEGISCO project (grant agreement nr RI-261561).
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Ponciano, L., Brasileiro, F. Assessing Green Strategies in Peer-to-Peer Opportunistic Grids. J Grid Computing 11, 129–148 (2013). https://doi.org/10.1007/s10723-012-9218-3
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DOI: https://doi.org/10.1007/s10723-012-9218-3