Abstract
The currently emerging large-scale complex networks and networks of networks are becoming apparent in the pervasive supply of seamless and transparent access to heterogeneous resources and services such as network domains, applications, services and storage owned by multiple organizations. The dynamics and heterogeneous environments involved, however, pose many challenges for controlling and balancing resource access, composition and deployment across complex grid and network infrastructures. In this paper, a scheme is proposed that gives a distributed load-balancing scheme by generating almost regular resource allocation networks. This network system is self-organized and depends only on local information for load distribution and resource discovery. The in-degree of each node refers to its free resources, and the job assignment and resource discovery processes required for load-balancing are accomplished by using fitted random sampling. Simulation results show that the generated network system provides an effective, scalable, and reliable load-balancing scheme for the distributed resources in grids and networks. The proposed solution is tested with real world data and the performance is tested against a recently reported distributed algorithm for load balancing.
Similar content being viewed by others
References
Ruggaber R (2007) Internet of services SAP research vision. In: Proceedings of the 16th IEEE international workshops on enabling technologies: infrastructure for collaborative enterprises, 2007 (WETICE 2007), p 3
Lüling R, Monien B, Ramme F (1991) A study of dynamic load balancing algorithms. In: Proceedings of the third IEEE SPDP, pp 686–689
Mitzenmacher M (2001) The power of two choices in randomized load balancing. In: IEEE Trans Parallel Distrib Syst, 12(10)
Casavant T, Kuhl J (1988) A taxonomy of scheduling in general-purpose distributed computing systems. IEEE Trans Softw Eng 14(2):141–154
Hendrickson B, Devine K (2000) Dynamic load balancing in computational mechanics. Comput Methods Appl Mech Eng 184:485–500
Aversa R, Di Martino B, Mazzocca N, Venticinque S (2006) MAGDA: A Mobile Agent based Grid Architecture. J Grid Comput 4(4):395–412
Shen H, Xu C-Z (2007) Locality-aware and churn-resilient load-balancing algorithms in structured peer-to-peer networks. IEEE Trans Parallel Distrib Syst 18(6):849–862
Kashyap A, Basar T, Srikant R (2007) Quantized consensus. Automatica 43(7):1192–1203
Berenbrink P, Friedetzky T, Hu Z (2009) A new analytical method for parallel, diffusion-type load balancing. J Parallel Distrib Comput 69(1):54–61
Kremien O, Kramer J (1992) Methodical analysis of adaptive load sharing algorithms. IEEE Trans Parallel Distrib Syst 3(6):747–760
Erdös P, Rényi A (1960) On the evolution of random graphs. Publ Math Inst Hung Acad Sci 5:17–61
PingER (2005) PingER: The Internet End-to-end Performance Measurement (IEPM) project. URL: http://www-iepm.slac.stanford.edu/pinger/
Abramowitz M, Stegun IA (1972) Handbook of mathematical functions with formulas, graphs, and mathematical tables, 9th edn. Dover Publications, New York
Newman MEJ, Park J (2003) Phys Rev 68
Albert R, Jeong H, Barabási AL (1999) Diameter of the world wide web. Nature 401(6749):130–131
Lehmann S, Jackson AD, Lautrup B (2005) Life, death and preferential attachment. Europhys Lett
Sarshar N, Boykin PO, Roychowdhury V (2004) In: Proceedings of the fourth international conference on peer-to-peer computing, pp 2–9
Lov’asz L, Winkler P (1995) Mixing of random walks and other diffusions on a graph. In: Surveys in combinatorics. London mathematical society lecture note series, vol 218. Cambridge University Press, Cambridge, pp 119–154
Distributed management task force, CIM Policy Model, v2.8
Kleinrock L (1975) Queuing systems. Volume I. Theory. Wiley, New York
Adabala S, Chadha V, Chawla P, Figueiredo R, Fortes J (2005) From virtualized resources to virtual computing grids: the in-vigo system. Future Gener Comput Syst 21(6):896–909
Schantz RE, Schmidt DC (2001) Middleware for distributed systems: evolving the common structure for network-centric applications. In: Encyclopaedia of software engineering. Wiley, New York
Di Nitto E, Dubois DJ, Mirandola R, Saffre F, Tateson R (2008) Applying self-aggregation to load balancing: experimental results. In: Proceedings of the 3rd international conference on bio-inspired models of network, information and computing systems (Bionetics 2008), Article 14, 25–28 November, 2008
Tian Hi, Shen H, Matsuzawa T (2005) Random walk routing for wireless sensor networks. In: Proceedings of the sixth international conference on parallel and distributed computing applications and technologies, USA
Servetto S, Barrenechea G (2002) Constrained random walks on random graphs; Routing algorithms for large scale wireless sensor networks. In: Proc. of 1st ACM international workshop on wireless sensor networks and applications
Kunz T (1991) The influence of different workload descriptions on a heuristic load balancing scheme. IEEE Trans Softw Eng, pp 1327–1341
Shivaratri N, Krueger P, Singhal M (1992) Load distributing for locally distributed systems. IEEE Comput 33–44
Ferrari D, Zhou S (1987) An empirical investigation of load indices for load balancing applications. Technical report. University of California, Berkeley
Becker W, Waldmann G (1994) Exploiting inter-task dependencies for dynamic load balancing. In: IEEE 3rd int. symposium on high performance distributed computing. San Francisco, California
Monien B (1996) Load balancing driven process migration. In: EURO-PAR’96, Lyon, France
Xu C, Lüling R, Monien B, Lau FCM (1995) An analytical comparison of nearest neighbours algorithms for load balancing in parallel computers. In: The 9th international parallel processing symposium, Paderborn, Germany
Zhou S (1988) A trace-driven simulation study of dynamic load balancing. IEEE Trans Softw Eng 14(9):1327–1341
Lin H, Raghavendra C (1992) A dynamic load-balancing policy with a central job dispatcher (LBC). IEEE Trans Softw Eng 18(2):148–158
Li Feng DY, Wu H, Zhang Y (2000) A dynamic load balancing algorithm based on distributed database system. In: Proc. 4th intl. conf. on high performance computing in the Asia-Pacific region, Beijing, China, pp 949–952
Braun TD (2001) A comparison of eleven static heuristics for mapping a class of independent tasks onto heterogeneous distributed computing systems. J Parallel Distrib Comput 61(6):810–837
Jackson D, Snell Q, Clement M (2001) Core algorithms of the Maui scheduler. In: Proceedings of 7th workshop on job scheduling strategies for parallel processing (Cambridge, MA, USA, 2001). Lecture notes computer science, vol 2221, pp 87–102
Litzkow M, Livny M, Mutka M (1988) Condor—a hunter of idle workstations. In: Proceedings of 8th international conference on distributed computing systems (ICDCS’88), San Jose, CA, USA, 1988, pp 104–111
Henderson RL (1995) Job scheduling under the portable batch system. In: Proceeding of 1st workshop on job scheduling strategies for parallel processing, Santa Barbara, CA, USA, 1995. Lecture notes in computer science, vol 949, pp 279–294
Foster I, Kesselman C (1997) Globus: a metacomputing infrastructure toolkit. Int J High Perform Comput Appl 2:115–128
Frey J, Tannenbaum T, Livny M, Foster I, Tuecke S (2002) CondorG: a computation management agent for multi-institutional grids. Cluster Comput 5(3):237–246
Zhuge H (2004) Semantics, resource and grid. Future Gen Comput Syst 20(1):1–5
Abramson D, Giddy J, Kotler L (2000) High performance parametric modeling with Nimrod/G: killer application for the global grid. In: Proceedings of the IPDPS‘00, Cancun, Mexico, 2000
Grimshaw WA (1997) The legion vision of a worldwide virtual computer. Commun ACM 40(1):39–45
Grimshaw WA (2004) The legion vision of a worldwide virtual computer. Intell Syst 19(1):13–17
Cao J, Spooner DP, Jarvis SA, Nudd GR (2005) Grid load balancing using intelligent agents. Future Gen Comput Syst 21(1):135–149. Special issue on intelligent grid environments: principles and applications
Nudd GR, Kerbyson DJ, Papaefstathiou E, Perry SC, Harper JS, Wilcox DV (2000) PACE—A toolset for the performance prediction of parallel and distributed systems. Int J High Perform Comput Appl 14(3):228–251
Albert R, Barabási AL (2002) Statistical mechanics of complex networks. Rev Mod Phys 74(1):47–97
Cybenko G (1989) Dynamic load balancing for distributed memory multiprocessors. J Parallel Distrib Comput 7(2):279–301
Saffre F, Tateson R, Halloy J, Shackleton M, Deneubourg JL (2008) Aggregation dynamics in overlay networks and their implications for self-organized distributed applications. Comput J, March 31st, 2008
Di Nitto E, Dubois DJ, Mirandola R (2007) Self-aggregation algorithms for autonomic systems. In: Proceedings of the 2nd international conference on bio-inspired models of network, information and computing systems (Bionetics 2007), 10–12 Dec. 2007, pp 120–128
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Randles, M., Abu-Rahmeh, O., Johnson, P. et al. Biased random walks on resource network graphs for load balancing. J Supercomput 53, 138–162 (2010). https://doi.org/10.1007/s11227-009-0366-6
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11227-009-0366-6