Performance Analysis of Available Bandwidth Estimation Tools for Grid Networks

Abstract Modern large-scale grid computing systems for processing advanced science and engineering applications rely on geographically distributed clusters. In such highly distributed environments, estimating the available bandwidth between clusters is a key issue for efficient task scheduling. We analyze the performance of two well known available bandwidth estimation tools, pathload and abget, with the aim of using them in grid environments. Our experiments consider the accuracy of the estimation, the convergence time, their level of intrusion in the grid links, and the ability to handle multiple simultaneous estimations. Overall, pathload represents a good solution to estimate available bandwidth in grid environments

Performance Analysis of a DEKF for Available Bandwidth Measurement

The paper presents a characterisation analysis of a measurement algorithm based on a Discrete-time Extended Kalman Filter (DEKF), which has recently been proposed for the estimation and tracking of end-to-end available bandwidth. The analysis is carried out by means of simulations for different rates of variations of the available bandwidth and permits assessing the performance of the measurement algorithm for different values of the filter parameters, that is, the covariance matrixes of the measurement and process noise

Performance analysis of available bandwidth estimation tools for grid networks

Modern large-scale grid computing systems for processing advanced science and engineering applications rely on geographically distributed clusters. In such highly distributed environments, estimating the available bandwidth between clusters is a key issue for efficient task scheduling. We analyze the performance of two well known available bandwidth estimation tools, pathload and abget, with the aim of using them in grid environments. Differently than previous investigations (Jain et al., http://www.caida.org/workshops/isma/0312/slides/rprasad-best.pdf ; Shriram et al., in Passive and active network measurement: 6th international workshop, PAM 2005. Springer, Berlin, 2005), our experiments consider a series of relevant metrics such as accuracy of the estimation, convergence time, degree of intrusion in the grid links, and ability to handle multiple simultaneous estimations. No previous work has analyzed the use of available bandwidth tools for the derivation of efficient grid scheduling.531SI10312

Refining The Estimation Of The Available Bandwidth In Inter-cloud Links For Task Scheduling

In hybrid clouds, the available bandwidth in inter-cloud links is quite variable. Overestimating the available bandwidth on theses channels at scheduling time can enlarge the makespan and cause deadline misses. In this paper, we propose a procedure for deflating the estimated available bandwidth used as input to cloud schedulers since schedulers are not usually designed to cope with inaccurate information on available bandwidth. The procedure is based on a multiple linear regression procedure which utilizes historical information of previous executions of workflows. Results showed that the proposed procedure can increase the number of valid schedules without increasing the makespan and cost estimations, regardless the variability in the available bandwidth during the execution of an application workflow.11271132Zhang, Q., Cheng, L., Boutaba, R., Cloud computing: State-of-the-art and research challenges (2010) Journal of Internet Services and Applications, 1 (1), pp. 7-18Bittencourt, L.F., Madeira, E.R.M., Da Fonseca, N.L.S., Scheduling in hybrid clouds (2012) IEEE Communications Magazine, 50 (9), pp. 42-47Taylor, I.J., Deelman, E., Gannon, D.B., Shields, M., Workflows for e-Science (2007) Scientific Workflows for Grids, , SpringerBittencourt, L.F., Madeira, E.R.M., Da Fonseca, N.L.S., Impact of communication uncertainties on workflow scheduling in hybrid clouds (2012) IEEE Global Communications Conference (IEEE GLOBECOM), , Anaheim, USA, decemberBatista, D.M., Chaves, L.J., Fonseca, N.L., Ziviani, A., Performance analysis of available bandwidth estimation tools for grid networks (2010) Journal of Supercomputing, 53 (1), pp. 103-121. , JulRahman, M., Li, X., Palit, H., Hybrid heuristic for scheduling data analytics workflow applications in hybrid cloud environment (2011) IEEE International Symposium on Parallel and Distributed Processing Workshops and Phd Forum (IPDPSW), pp. 966-974Bittencourt, 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. , DecVecchiola, C., Calheiros, R.N., Karunamoorthy, D., Buyya, R., Deadline-driven provisioning of resources for scientific applications in hybrid clouds with aneka (2012) Future Gener. Comput. Syst., 28 (1), pp. 58-65. , janAllen, 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) 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. , 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. 3-19Wang, G., Ng, T.S.E., The impact of virtualization on network performance of amazon EC2 data center (2010) 29th Conference on Information Communications, pp. 1163-1171Deelman, E., Singh, G., Su, M.-H., Blythe, J., Gil, Y., Kesselman, C., Mehta, G., Katz, D.S., Pegasus: A framework for mapping complex scientific workflows onto distributed systems (2005) Scientific Programming Journal, 13 (3), pp. 219-237Ramakrishnan, A., Singh, G., Zhao, H., Deelman, E., Sakellariou, R., Vahi, K., Blackburn, K., Samidi, M., Scheduling dataintensive workflows onto storage-constrained distributed resources (2007) IEEE International Symposium on Cluster Computing and the Grid, pp. 401-409Sanghrajka, S., Mahajan, N., Sion, R., Cloud performance benchmark series-network performance: Amazon EC2 (2011) Stony Brook University, Tech. Rep.Sanghrajka, S., Sion, R., Cloud performance benchmark series-network performance: Rackspace com (2011) Stony Brook University, Tech. Rep.Casanova, H., Legrand, A., Zagorodnov, D., Berman, F., Heuristics for scheduling parameter sweep applications in grid environments (2000) Heterogeneous Computing Workshop, pp. 349-36