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. 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.
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References
Jain M, Prasad RS, Dovrolis C (2003) Evaluating pathrate and pathload with realistic cross-traffic. Talk at bandwidth estimation workshop 2003. http://www.caida.org/workshops/isma/0312/slides/rprasad-best.pdf. Retrieved August 17, 2009
Shriram A, Murray M, Hyun Y, Brownlee N, Broido A (2005) Comparison of public end-to-end bandwidth estimation tools on high-speed links. In: Passive and active network measurement: 6th international workshop, PAM 2005. Springer, Berlin
Foster I (2002) What is the grid? A three point checklist. GRID Today 1(6). http://www-fp.mcs.anl.gov/~foster/Articles/WhatIsTheGrid.pdf. Retrieved August 17, 2009
El Khatib Y, Edwards C (2007) A survey-based study of grid traffic. In: GridNets ’07: Proceedings of the first international conference on networks for grid applications. ICST, Brussels, pp 1–8
Newman HB (2004) Networking for high energy and nuclear physics as global e-science. In: Proceedings of computing in high energy and nuclear physics, Sep 2004. http://ultralight.caltech.edu/web-site/common/publications/article/2004/09chep04/Network_for_HEP.pdf. Retrieved August 17, 2009
Ferrari T, Giacomini F (2004) Network monitoring for GRID performance optimization. Comput Commun 27(14):1357–1363. Special Issue on Network Support for Grid Computing
Silvester JA (2005) CalREN: Advanced network(s) for education in California, Oct 2005. http://isd.usc.edu/~jsilvest/talks-dir/20051021-cudi-merida.pdf. Retrieved August 17, 2009
WLCG Worldwide LHC Computing Grid (2008) http://lcg.web.cern.ch/LCG/public/. Retrieved August 17, 2009
Jain M, Dovrolis C (2003) End-to-end available bandwidth: Measurement methodology, dynamics, and relation with TCP throughput. IEEE/ACM Trans Netw (TON) 11(4):537–549
Batista DM, Drummond AC, Fonseca NLS (2009) Robust scheduler for grid networks. In: SAC ’09: Proceedings of the 2009 ACM symposium on applied computing, New York, NY, USA, Mar 2009. ACM Press, New York, pp 35–39
Ito T, Ohsaki H, Imase M (2005) On parameter tuning of data transfer protocol GridFTP for wide-area grid computing. In: Proceedings of the BroadNets 2005, vol 2, pp 1338–1344
Batista DM, da Fonseca NLS, Miyazawa FK, Granelli F (2008) Self-adjustment of resource allocation for grid applications. Comput Netw 52(9):1762–1781
Huedo E, Montero RS, Llrorent IM (2002) An experimental framework for executing applications in dynamic grid environments. Technical Report 2002-43. NASA Langley Research Center
Montero RS, Huedo E, Llorente IM (2003) Grid resource selection for opportunistic job migration. In: Proceedings of the 9th international Euro-Par conference. Springer, Berlin, pp 366–373
Allen G, Angulo D, Foster I, Lanfermann G, Liu C, Radke T, Seidel E, Shalf J (2001) The cactus worm: Experiments with dynamic resource discovery and allocation in a grid environment. Int J High Perform Comput Appl 15(4):345–358
Chun G, Dail H, Casanova H, Snavely A (2004) Benchmark probes for grid assessment. In: Proceedings of the 18th international parallel and distributed processing symposium, Apr 2004, pp 276–283
Iosup A, Li H, Jan M, Anoep S, Dumitrescu C, Wolters L, Epema DHJ (2008) The grid workloads archive. Future Gener Comput Syst 24(7):672–686
Antoniades D, Athanatos M, Papadogiannakis A, Markatos EP, Dovrolis C (2006) Available bandwidth measurement as simple as running wget. In: Proceedings of the passive and active measurement workshop 2006
Cooperative Association for Internet Data Analysis (CAIDA) (2008) CAIDA : tools : taxonomy. http://www.caida.org/tools/taxonomy/performance.xml#bw. Retrieved August 17, 2009
Prasad R, Dovrolis C, Murray M, Claffy K (2003) Bandwidth estimation: Metrics, measurement techniques, and tools. IEEE Netw 17(6):27–35
NLANR (2008) Iperf. http://sourceforge.net/projects/iperf/?abmode=1. Retrieved August 17, 2009
Tirumala A, Cottrell L, Dunigan T (2003) Measuring end-to-end bandwidth with Iperf using Web100. Technical Report SLAC-PUB-9733, Stanford Linear Accelerator Center
Ozturk Y, Kulkarni M (2008) DIChirp: Direct injection bandwidth estimation. Int J Netw Manag 18(5):377–394
Pásztor A, Veitch D (2002) PC-based precision timing without GPS. In: ACM, SIGMETRICS, Los Angeles, CA, USA, June 2002
Wang SY, Choua CL, Lin CC (2007) The design and implementation of the NCTUns network simulation engine. Simul Model Pract Theory 15(1):57–81
Prasad R, Jain M, Dovrolis C (2004) Effects of interrupt coalescence on network measurements. In: Proceedings of the passive and active network measurement workshop 2004, pp 247–256
PlanetLab (2009) An open platform for developing, deploying, and accessing planetary-scale services. http://www.planet-lab.org/. Retrieved August 17, 2009
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Batista, D.M., Chaves, L.J., da Fonseca, N.L.S. et al. Performance analysis of available bandwidth estimation tools for grid networks. J Supercomput 53, 103–121 (2010). https://doi.org/10.1007/s11227-009-0344-z
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DOI: https://doi.org/10.1007/s11227-009-0344-z