Abstract
We consider allocation problems that arise in the context of service allocation in Clouds. More specifically, on the one part we assume that each Physical Machine (denoted as PM) is offering resources (memory, CPU, disk, network). On the other part, we assume that each application in the IaaS Cloud comes as a set of services running as Virtual Machines (VMs) on top of the set of PMs. In turn, each service requires a given quantity of each resource on each machine where it runs (memory footprint, CPU, disk, network). Moreover, there exists a Service Level Agreement (SLA) between the Cloud provider and the client that can be expressed as follows: the client requires a minimal number of service instances which must be alive at the end of a time period, with a given reliability (that can be converted into penalties paid by the provider). In this context, the goal for the Cloud provider is to find an allocation of VMs onto PMs so as to satisfy, at minimal cost, both capacity and reliability constraints for each service. In this paper, we propose a simple model for reliability constraints and we prove that it is possible to derive efficient heuristics.
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Armbrust, M., Fox, A., Griffith, R., Joseph, A., Katz, R., Konwinski, A., Lee, G., Patterson, D., Rabkin, A., Stoica, I., et al.: Above the clouds: A berkeley view of cloud computing. EECS Department, University of California, Berkeley, Tech. Rep. UCB/EECS-2009-28 (2009)
Beaumont, O., Eyraud-Dubois, L., Larchevêque, H.: Reliable Service Allocation in Clouds. In: IPDPS 2013 - 27th IEEE International Parallel & Distributed Processing Symposium, Boston, États-Unis (2013), http://hal.inria.fr/hal-00743524
Beaumont, O., Eyraud-Dubois, L., Rejeb, H., Thraves, C.: Heterogeneous Resource Allocation under Degree Constraints. IEEE Transactions on Parallel and Distributed Systems (2012)
Beloglazov, A., Buyya, R.: Energy efficient allocation of virtual machines in cloud data centers. In: 2010 10th IEEE/ACM International Conference on Cluster, Cloud and Grid Computing, pp. 577–578. IEEE (2010)
Berl, A., Gelenbe, E., Di Girolamo, M., Giuliani, G., De Meer, H., Dang, M., Pentikousis, K.: Energy-efficient cloud computing. The Computer Journal 53(7), 1045 (2010)
Bougeret, M., Casanova, H., Rabie, M., Robert, Y., Vivien, F.: Checkpointing strategies for parallel jobs. In: 2011 International Conference for High Performance Computing, Networking, Storage and Analysis (SC), pp. 1–11. IEEE (2011)
Box, G.E., Hunter, W.G., Hunter, J.S.: Statistics for experimenters: an introduction to design, data analysis, and model building. Wiley Series in Probability and Mathematical Statistics (1978)
Cappello, F.: Fault tolerance in petascale/exascale systems: Current knowledge, challenges and research opportunities. International Journal of High Performance Computing Applications 23(3), 212–226 (2009)
Cirne, W., Frachtenberg, E.: Web-scale job scheduling. In: Cirne, W., Desai, N., Frachtenberg, E., Schwiegelshohn, U. (eds.) JSSPP 2012. LNCS, vol. 7698, pp. 1–15. Springer, Heidelberg (2013)
Dongarra, J., Beckman, P., Aerts, P., Cappello, F., Lippert, T., Matsuoka, S., Messina, P., Moore, T., Stevens, R., Trefethen, A., et al.: The international exascale software project: a call to cooperative action by the global high-performance community. International Journal of High Performance Computing Applications 23(4), 309–322 (2009)
Ferreira, K., Stearley, J., Laros III, J., Oldfield, R., Pedretti, K., Brightwell, R., Riesen, R., Bridges, P.G., Arnold, D.: Evaluating the viability of process replication reliability for exascale systems. In: Proceedings of 2011 International Conference for High Performance Computing, Networking, Storage and Analysis, p. 44. ACM (2011)
Garey, M.R., Johnson, D.S.: Computers and Intractability, a Guide to the Theory of NP-Completeness. W.H. Freeman and Company (1979)
Hochbaum, D.: Approximation Algorithms for NP-hard Problems. PWS Publishing Company (1997)
Data centers waste vast amounts of energy belying industry image, http://www.nytimes.com/2012/09/23/technology/data-centers-waste-vast-amounts-of-energy-belying-industry-image.html
Ranganathan, K., Iamnitchi, A., Foster, I.: Improving data availability through dynamic model-driven replication in large peer-to-peer communities. In: 2nd IEEE/ACM International Symposium on Cluster Computing and the Grid, p. 376 (May 2002)
Santos-Neto, E., Cirne, W., Brasileiro, F., Lima, A.: Exploiting replication and data reuse to efficiently schedule data-intensive applications on grids. In: Feitelson, D., Rudolph, L., Schwiegelshohn, U. (eds.) JSSPP 2004. LNCS, vol. 3277, pp. 210–232. Springer, Heidelberg (2005)
da Silva, D.P., Cirne, W., Brasileiro, F.: Trading cycles for information: Using replication to schedule bag-of-tasks applications on computational grids. In: Kosch, H., Böszörményi, L., Hellwagner, H. (eds.) Euro-Par 2003. LNCS, vol. 2790, pp. 169–180. Springer, Heidelberg (2003)
Van, H., Tran, F., Menaud, J.: SLA-aware virtual resource management for cloud infrastructures. In: IEEE Ninth International Conference on Computer and Information Technology, pp. 357–362. IEEE (2009)
Zhang, Q., Cheng, L., Boutaba, R.: Cloud computing: state-of-the-art and research challenges. Journal of Internet Services and Applications 1(1), 7–18 (2010)
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Beaumont, O., Eyraud-Dubois, L., Pesneau, P., Renaud-Goud, P. (2014). Reliable Service Allocation in Clouds with Memory and Capacity Constraints. In: an Mey, D., et al. Euro-Par 2013: Parallel Processing Workshops. Euro-Par 2013. Lecture Notes in Computer Science, vol 8374. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-54420-0_68
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DOI: https://doi.org/10.1007/978-3-642-54420-0_68
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