Abstract
We examine the problem of managing a server farm in a cost-efficient way that reduces the cost caused by server failures, according to an Infrastructure-as-a-Service model in cloud. Specifically, failures in cloud systems are so frequent that severely affect the normal operation of job requests and incurring high penalty cost. It is possible to increase the net revenue through reducing the energy cost and penalty by leveraging failure predictiors. First, we incorporate the malfunction and recovery states into the server management process, and improve the cost-efficiency of each server using failure predictor-based proactive recovery. Second, we present a revenue-driven cloud scheduling algorithm, which further increases net revenue in collaboration with server management algorithm. The formal and experimental analysis manifests our expected net revenue improvement.
This work is based on ”On Revenue Driven Server Management in Cloud”, by L. Zhao and K. Sakurai, which appeared in Proc. of 2nd International Conference on Cloud Computing and Service Science, Portugal, April 2012.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Bobroff, N., Kochut, A., Beaty, K.: Dynamic Placement of Virtual Machines for Managing SLA Violations. In: 10th IFIP/IEEE International Symposium on Integrated Network Management, pp. 119–128 (2007)
Schroeder, B., Gibson, G.A.: A large-scale study of failures in high-performance computing systems. In: DSN 2006, pp. 249–258 (2006)
Hoelzle, U., Barroso, L.A.: The Datacenter as a Computer: An Introduction to the Design of Warehouse-Scale Machines, 1st edn. Morgan and Claypool Publishers (2009)
Dean, J.: Experiences with mapreduce, an abstraction for large-scale computation. In: PACT 2006, pp. 1–6. ACM (2006)
Vishwanath, K.V., Nagappan, N.: Characterizing cloud computing hardware reliability. In: SoCC 2010, pp. 193–204 (2010)
Nightingale, E.B., Douceur, J.R., Orgovan, V.: Cycles, cells and platters: an empirical analysisof hardware failures on a million consumer pcs. In: EuroSys 2011, pp. 343–356. ACM (2011)
Javadi, B., Kondo, D., Vincent, J.M., Anderson, D.P.: Discovering statistical models of availability in large distributed systems: An empirical study of seti@home. IEEE Transactions on Parallel and Distributed Systems 22, 1896–1903 (2011)
Fu, S., Xu, C.Z.: Exploring event correlation for failure prediction in coalitions of clusters. In: SC 2007, pp. 41:1–41:12. ACM (2007)
Pinheiro, E., Weber, W.D., Barroso, L.A.: Failure trends in a large disk drive population. In: FAST 2007, pp. 17–28 (2007)
Salfner, F., Lenk, M., Malek, M.: A survey of online failure prediction methods. ACM Comput. Surv. 42, 10:1–10:42 (2010)
Koomey, J., Brill, K., Turner, P., et al.: A simple model for determining true total cost of ownership for data centers. Uptime institute white paper (2007)
Patel, C.D., Shah, A.J.: A simple model for determining true total cost of ownership for data centers. Hewlett-Packard Development Company report HPL-2005-107 (2005)
Fitó, J.O., Presa, I.G., Guitart, J.: Sla-driven elastic cloud hosting provider. In: PDP 2010, pp. 111–118 (2010)
Macías, M., Rana, O., Smith, G., Guitart, J., Torres, J.: Maximizing revenue in grid markets using an economically enhanced resource manager. Concurrency and Computation Practice and Experience 22, 1990–2011 (2010)
Mazzucco, M., Dyachuk, D., Deters, R.: Maximizing cloud providers’ revenues via energy aware allocation policies. In: IEEE CLOUD 2010, pp. 131–138 (2010)
Mazzucco, M., Dyachuk, D., Dikaiakos, M.: Profit-aware server allocation for green internet services. In: MASCOTS 2010, pp. 277–284 (2010)
Abraham, A., Grosan, C.: Genetic programming approach for fault modeling of electronic hardware. In: The 2005 IEEE Congress on Evolutionary Computation, vol. 2, pp. 1563–1569 (2005)
Marbukh, V., Mills, K.: Demand pricing & resource allocation in market-based compute grids: A model and initial results. In: ICN 2008, pp. 752–757 (2008)
Zheng, Q., Veeravalli, B.: Utilization-based pricing for power management and profit optimization in data centers. JPDC 72, 27–34 (2012)
Macías, M., Guitart, J.: A genetic model for pricing in cloud computing markets. In: SAC 2011, pp. 113–118. ACM, New York (2011)
Mastroianni, C., Meo, M., Papuzzo, G.: Self-economy in cloud data centers: statistical assignment and migration of vms. In: Jeannot, E., Namyst, R., Roman, J. (eds.) Euro-Par 2011, Part I. LNCS, vol. 6852, pp. 407–418. Springer, Heidelberg (2011)
Rackspace (2012), http://www.rackspace.com (Online; accessed January 31, 2012)
Lewis, P.A.: A branching poisson process model for the analysis of computer failure patterns. Journal of the Royal Statistical Society, Series B 26, 398–456 (1964)
IBM: Ibm system x 71451ru entry-level server (2012), http://www.amazon.com/System-71451RU-Entry-level-Server-E7520/dp/B003U772W4
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer International Publishing Switzerland
About this paper
Cite this paper
Zhao, L., Sakurai, K. (2013). Improving Cost-Efficiency through Failure-Aware Server Management and Scheduling in Cloud. In: Ivanov, I.I., van Sinderen, M., Leymann, F., Shan, T. (eds) Cloud Computing and Services Science. CLOSER 2012. Communications in Computer and Information Science, vol 367. Springer, Cham. https://doi.org/10.1007/978-3-319-04519-1_2
Download citation
DOI: https://doi.org/10.1007/978-3-319-04519-1_2
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-04518-4
Online ISBN: 978-3-319-04519-1
eBook Packages: Computer ScienceComputer Science (R0)