Impact Of Communication Uncertainties On Workflow Scheduling In Hybrid Clouds

The so-called hybrid cloud is the composition of an infrastructure that comprises private resources as well as public resources leased from public clouds. Hybrid clouds can be utilized for the execution of applications composed of dependent jobs, usually modeled as workflows. In this scenario, a scheduler must distribute the components of the workflow onto available resources considering the communication demands and the available bandwidth in network links. However, such information can be imprecise, and consequently decisions on resource allocation can be ineffective. In this paper, we evaluate scheduling algorithms in the face of imprecise information on the availability of communication channels. Results showed that schedules are negatively affected by the unforeseen variations in bandwidth during the execution of the application. © 2012 IEEE.16231628Zhang, Q., Cheng, L., Boutaba, R., Cloud computing: State-of-the-art and research challenges (2010) Journal of Internet Services and Applications, 1 (1), pp. 7-18Casanova, H., Legrand, A., Zagorodnov, D., Berman, F., Heuristics for scheduling parameter sweep applications in grid environments (2000) Heterogeneous Computing Workshop, 2000. (HCW 2000) Proceedings. 9th, pp. 349-363Armbrust, M., Fox, A., Griffith, R., Joseph, A.D., Katz, R., Konwinski, A., Lee, G., Zaharia, M., A view of cloud computing (2010) Communications of the ACM, 53, pp. 50-58. , AprDong, F., Akl, S.G., Scheduling algorithms for grid computing: State of the art and open problems (2006) Queen's University School of Computing, Kingston, Canada, Tech. Rep., , janYu, J., Buyya, R., Ramamohanarao, K., Work flow scheduling algorithms for grid computing (2008) Studies in Computational Intelligence, 146, pp. 173-214Pandey, S., Wu, L., Guru, S., Buyya, R., A particle swarm optimization-based heuristic for scheduling workflow applications in cloud computing environments (2010) 24th IEEE International Conference on Advanced Information Networking and Applications (AINA), pp. 400-407. , aprilYu, J., Buyya, R., Tham, C.K., Cost-based scheduling of scientific workflow applications on utility grids (2005) E-Science and Grid Computing, p. 8. , july, pp. -147Bittencourt, L.F., Madeira, E.R.M., HCOC: A cost optimization algorithm for workflow scheduling in hybrid clouds (2011) Journal of Internet Services and Applications, 2 (3), pp. 207-227. , AugBittencourt, L.F., Madeira, E.R.M., A performance-oriented adaptive scheduler for dependent tasks on grids (2008) Concurrency and Computation: Practice and Experience, 20 (9), pp. 1029-1049Abrishami, S., Naghibzadeh, M., Epema, D., Cost-driven scheduling of grid workflows using partial critical paths (2010) 11th IEEE/ACM International Conference on Grid Computing (GRID 2010), pp. 81-88. , octKalantari, M., Akbari, M.K., Grid performance prediction using state-space model (2009) Concurrency and Computation : Practice and Experience, 21 (9), pp. 1109-1130. , JunGautama, H., Van Gemund, A., Low-cost static performance prediction of parallel stochastic task compositions (2006) Parallel and Distributed Systems, IEEE Transactions on, 17 (1), pp. 78-91. , janKhan, A., Yan, X., Tao, S., Anerousis, N., Workload characterization and prediction in the cloud: A multiple time series approach (2012) Workshop on Cloud Management (CloudMan 2012), NOMS Workshop Proceedings, , USA, AprilAllen, G., Angulo, D., Foster, I., Lanfermann, G., Liu, C., Radke, T., Seidel, E., Shalf, J., The cactus worm: Experiments with dynamic resource discovery and allocation in a grid environment (2001) International Journal of High Performance Computing Applications, 15, p. 2001Sakellariou, R., Zhao, H., A low-cost rescheduling policy for efficient mapping of workflows on grid systems (2004) Scientific Programming, 12 (4), pp. 253-262. , http://dl.acm.org/citation.cfm?id=1240160.1240165, DecBatista, D.M., Da Fonseca, N.L.S., Miyazawa, F.K., Granelli, F., Self-adjustment of resource allocation for grid applications (2008) Computer Networks, 52 (9), pp. 1762-1781. , JunBatista, D.M., Da Fonseca, N.L.S., Robust scheduler for grid networks under uncertainties of both application demands and resource availability (2011) Computer Networks, 55 (1), pp. 3-19Zhao, Y., Dobson, J., Foster, I., Moreau, L., Wilde, M., A notation and system for expressing and executing cleanly typed workflows on messy scientific data (2005) SIGMOD Records, 34 (3), pp. 37-43Deelman, E., (2008) Clouds: An opportunity for scientific applications? (keynote in the 2008 Cracow Grid Workshops)Gil, Y., Deelman, E., Ellisman, M., Fahringer, T., Fox, G., Gannon, D., Goble, C., Myers, J., Examining the challenges of scientific workflows (2007) IEEE Computer, 40 (12), pp. 24-32. , de