Open Access Open Access  Restricted Access Subscription or Fee Access

Grid Self-Load-Balancing: the Agent Process Paradigm


(*) Corresponding author


Authors' affiliations


DOI: https://doi.org/10.15866/irecos.v12i2.12718

Abstract


Load balancing aims to exploit networked resources equitably in such a way that no resources are overloaded while others are under-loaded or idle. Many approaches have been proposed and implemented, but as new infrastructures emerge like grids and Global Computing (GC), new challenges are raised with regard to network latency. The location policy, as one of the main fundamentals of load balancing solutions, aims to locate overloaded and under-loaded nodes in a network. To do so, multiple communication messages are sent across the network. This technique wastes network resources and causes remarkable network delays in environments like GC, which makes it impractical. In this paper, we propose a new paradigm for adaptive distributed load balancing inspired by swarm intelligence and multi-agent systems. In such a paradigm, no load balancing service would be required. In fact, work tasks are self-aware and capable of self-load-balancing over an unknown-load network. By its nature and based on stigmergy mechanisms, communication frequency of the proposed paradigm is significantly reduced compared to existing solutions. The present work explains the fundamentals of this paradigm, coined the Agent Process Paradigm (APP), as well as its underlying algorithms. Results of performance evaluation are presented and discussed at the end of this paper.
Copyright © 2017 Praise Worthy Prize - All rights reserved.

Keywords


Self Load Balancing; Swarm Intelligence; Multi-Agent System; Task Migration; Grid Computing And Distributed Applications

Full Text:

PDF


References


Y. Jiang, A Survey of Task Allocation and Load Balancing in Distributed Systems, IEEE Transactions on Parallel and Distributed Systems. Vol. 27, n. 2, pp. 585-595, 2016.
http://dx.doi.org/10.1109/tpds.2015.2407900

A. Adnane, H. Medromi, Adaptive HPC Cluster Load Balancing Approaches and Implementations, Proceedings of the International Conference on Intelligent Information and Network Technology IC2INT (Page: 15 Year of Publication: 2013).
http://dx.doi.org/10.1109/eict.2014.6777900

A. Adnane, H. Medromi, Equilibrage de Charge et Intelligence Collective au Sein d'un Cluster de Calcul, Proceedings of First Workshop on Data-mining and Optimization FDO (Page: 109 Year of Publication: 2012).
http://dx.doi.org/10.1016/b978-2-294-71023-0.00009-2

A. S. Milani, N. J. Navimipour, Load Balancing Mechanisms and Techniques in the Cloud Environments: Systematic Literature Review and Future Trends, Journal of Network and Computer Applications,Vol. 71, pp. 86-98, 2016.
http://dx.doi.org/10.1016/j.jnca.2016.06.003

N. Pandey, S. K. Verma, V. K. Tamta, Load Balancing Approaches in Grid Computing Environment, International Journal of Computer Applications. Vol. 72, n. 12, pp. 42-49, 2013.
http://dx.doi.org/10.5120/12549-9185

J. Kolodziej, S. U. Khan, Multi-Level Hierarchic Genetic-Based Scheduling of Independent Jobs in Dynamic Heterogeneous Grid Environment, Information Sciences. Vol. 214, pp. 01-19, 2012.
http://dx.doi.org/10.1016/j.ins.2012.05.016

J. Zhao, K. Yang, X. Wei, Y. Ding, L. Hu, G. Xu, A Heuristic Clustering-Based Task Deployment Approach for Load Balancing Using Bayes Theorem in Cloud Environment, IEEE Transactions on Parallel and Distributed Systems, Vol. 27, n. 2, pp. 305-316, 2016.
http://dx.doi.org/10.1109/tpds.2015.2402655

G. Jackson, P. Keleher, A. Sussman, Decentralized Scheduling and Load Balancing for Parallel Programs, Proceedings of the 14th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (Page: 324 Year of Publication: 2014).
http://dx.doi.org/10.1109/ccgrid.2014.44

S. Khan, B. Nazir, I. A. Khan, S. Shamshirband, A. T. Chronopoulos, Load Balancing in Grid Computing: Taxonomy, Trends and Opportunities, Journal of Network and Computer Applications, Vol. 88, pp. 99-111, 2017.
http://dx.doi.org/10.1016/j.jnca.2017.02.013

K. Al Nuaimi, N. Mohamed, M. Al Nuaimi, J. Al-Jaroodi, A Survey of Load Balancing in Cloud Computing: Challenges and Algorithm, Proceedings of the Second Symposium on Network Cloud Computing and Applications (Page: 137 Year of Publication: 2012).
http://dx.doi.org/10.1109/ncca.2012.29

J. Cao, D. P. Spooner, S. A. Jarvis, G. R. Nudd, Grid Load Balancing Using Intelligent Agents. Future Generation Computer Systems, Vol. 21, n. 1, pp. 135-149, 2005.
http://dx.doi.org/10.1016/j.future.2004.09.032

H. Siar, K. Kiani, A. T. Chronopoulos, A Combination of Game Theory and Genetic Algorithm for Load Balancing in Distributed Computer Systems, International Journal of Advanced Intelligence Paradigms. Vol. 9, n. 1, pp. 82-95, 2017.
http://dx.doi.org/10.1504/ijaip.2017.10002028

H. Desai, R. Oza, A Study of Dynamic Load Balancing in Grid Environment, Proceedings of the International Conference on Wireless Communications, Signal Processing and Networking. (Page: 128 Year of Publication: 2016).
http://dx.doi.org/10.1109/wispnet.2016.7566105

J. Balasangameshwaraa, N. Rajub, A Hybrid Policy for Fault Tolerant Load Balancing in Grid Computing Environments, Journal of Network and Computer Applications, Vol. 35, n. 1, pp. 412-422, 2012.
http://dx.doi.org/10.1016/j.jnca.2011.09.005

Q. Long, J. Lin, Z. Sun, Agent Scheduling Model for Adaptive Dynamic Load Balancing in Agent Based Distributed Simulations, Simulation Modelling Practice and Theory. Vol. 19, n. 4, pp. 1021-1034, 2011.
http://dx.doi.org/10.1016/j.simpat.2011.01.002

S. A. Ludwig, A. Moallem, Swarm Intelligence Approaches for Grid Load Balancing. Journal of Grid Computing. Vol. 9, n. 3, pp. 279-301, 2011.
http://dx.doi.org/10.1007/s10723-011-9180-5

A. M. Tripathi, S. Singh, A Literature Review on Algorithms for the Load Balancing in Cloud Computing Environments and their Future Trends, Computer Modelling and New Technologies, Vol. 21, n. 1, pp. 64-73, 2017.
http://dx.doi.org/10.23883/ijrter.2017.3076.uimcu

D. Corne, A. Reynolds, E. Bonabeau, Handbook of Natural Computing (Springer, Berlin, Heidelberg, 2012).
http://dx.doi.org/10.1007/978-3-540-92910-9_48

M. Lagwal , N. Bhardwaj, A Survey On Load Balancing Methods and Algorithms in Cloud Computing, International Journal of Computer Sciences and Engineering. Vol. 5, n. 4, pp. 46-51, 2017.
http://dx.doi.org/10.21884/ijmter.2017.4234.2ipfq

Adnane, A., Medromi, H., Built-in Stigmergy-Based Load Balancing Model for HPC Clusters, (2014) International Review on Computers and Software (IRECOS), 9 (5), pp. 883-891.

Ch. Li, Ch. Ding, K. Shen, Quantifying the Cost of Context Switch, Proceedings of the 2007 Workshop on Experimental Computer Science (Year of Publication: 2007 ISBN: 978-1-59593-751-3).
http://dx.doi.org/10.1145/1281700.1281702

M. J. North, N. T. Collier, J. Ozik, E. R. Tatara, C. M. Macal, M. Bragen, P. Sydelko, Complex Adaptive Systems Modeling with Repast Simphony, Complex Adaptive Systems Modeling, 2013.
http://dx.doi.org/10.1186/2194-3206-1-3

C. M. Macal, M. J. North, Tutorial on Agent-Based Modeling and Simulation. Journal of Simulation. Vol. 4, n. 3, pp. 151-162, 2010.
http://dx.doi.org/10.1057/9781137453648.0004

S. Abar, G. K. Theodoropoulos, P. Lemarinier, G. M. P. O’Hare, Agent Based Modelling and Simulation tools: A review of the State-of-Art Software, Computer Science Review. Vol. 24, pp. 13-33, 2017.
http://dx.doi.org/10.1016/j.cosrev.2017.03.001


Refbacks

  • There are currently no refbacks.



Please send any question about this web site to info@praiseworthyprize.com
Copyright © 2005-2024 Praise Worthy Prize