International Journal of Advanced Computer Research (IJACR) ISSN (P): 2249-7277 ISSN (O): 2277-7970 Vol - 8, Issue - 35, March 2018
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Communication complexity in high-speed distributed computer network in an agent based architecture for grids service

Serrano, Juan Francisco and Surós Rina

Abstract

Grid is a technology that implements the process of sharing resources in a flexible, secure and coordinated manner. Task management in computational grids involves planning, implementation and monitoring. The main contribution of this work consists in the development of a model with an agent-based architecture for managing computer resources each with defined operations so that the user can perform tasks efficiently and effectively and thus improve substantially the management by a gLite Grid middleware. The solution proposed provides a platform based on a collection of agents in a virtual organization. The model considers the heterogeneity of resources so that it is completely independent of any physical network architecture. This paper focus on the model, simulation and evaluation of an agent-based management of computational resources in grid environment architecture. Experimental results showed significantly the effectiveness of algorithms and planning policies to achieve load balancing, fault monitoring, and service quality. The computational complexity of the proposed model is studied and the experimental results are analyzed with respect to the use of the computing resources.

Keyword

Grids, Agent architecture, Load balancing, Fault monitoring, Computational complexity.

Cite this article

Refference

[1][1]Czajkowski K. GT resource management and scheduling: the globus perspective. GlobusWorld 2003:13-7.

[2][2]Pathak M, Bhartee AK, Tandon V. An efficient scheduling policy for load balancing model for computational grid system. Computer Engineering and Intelligent Systems. 2012; 3(7):51-61.

[3][3]Czajkowski K, Fitzgerald S, Foster I, Kesselman C. Grid information services for distributed resource sharing. In international symposium on high performance distributed computing 2001 (pp. 181-94). IEEE.

[4][4]Xiao P, Liu D, Qu X. Three-side gaming model for resource co-allocation in grid computing. Journal of Software. 2012; 7(9):2125-32.

[5][5]Foster I, Kesselman C. The grid 2: blueprint for a new computing infrastructure. Elsevier; 2003.

[6][6]Bajo J, Corchado JM. Neural systems in distributed computing and artificial intelligence. Neurocomputing. 2017; (231):1-2.

[7][7]Kiourt C, Kalles D. A distributed multi agents based platform for high performance computing infrastructures. Workshop on parallel and distributed computing for knowledge discovery in databases 2016.

[8][8]Kiourt C, Kalles D. A platform for large-scale game-playing multi-agent systems on a high performance computing infrastructure. Multiagent and Grid Systems. 2016; 12(1):35-54.

[9][9]Hu J, Gao J. IMCAG: infrastructure for managing and controlling agent grid. In international conference on grid and cooperative computing 2003 (pp. 161-5). Springer, Berlin, Heidelberg.

[10][10]Yousif A, Abdullah AH, Latiff MS, Bashir MB. A taxonomy of grid resource selection mechanisms. International Journal of Grid and Distributed Computing. 2011; 4(3):107-17.

[11][11]Yousif A, Nor SM, Abdualla AH, Bashir MB. Job scheduling algorithms on grid computing: state-of-the art. International Journal of Grid and Distributed Computing. 2015; 8(6):125-40.

[12][12]Utkarsh K, Trivedi A, Srinivasan D, Reindl T. A consensus-based distributed computational intelligence technique for real-time optimal control in smart distribution grids. IEEE Transactions on Emerging Topics in Computational Intelligence. 2017; 1(1):51-60.

[13][13]Mariotti M, Gervasi O, Vella F, Costantini A, Cuzzocrea A. A DBMS-based system for integrating grids and clouds: anatomy, models, functionalities. In proceedings of the international conference on internet of things and cloud computing 2016. ACM.

[14][14]Foster I. Globus toolkit version 4: software for service-oriented systems. In IFIP international conference on network and parallel computing 2005 (pp. 2-13). Springer, Berlin, Heidelberg.

[15][15]Kaur N, Kaur H, Ahuja SP. Analysis of advanced grid resource management models. Research Cell: An International Journal of Engineering Sciences. 2011.

[16][16]Foster I, Kesselman C. Globus: a metacomputing infrastructure toolkit. The International Journal of Supercomputer Applications and High Performance Computing. 1997; 11(2):115-28.

[17][17]Open Grid Forum. An Open Global Forum for Advanced Distributed Computing. https://www.ogf.org/ogf/doku.php. Accessed 21 August 2017.

[18][18]Foster I, Kesselman C, Tuecke S. The anatomy of the grid: enabling scalable virtual organizations. The International Journal of High Performance Computing Applications. 2001; 15(3):200-22.

[19][19]Luan C, Song G, Zheng Y. An infrastructure for grid job monitoring. In international conference on grid and cooperative computing 2005 (pp. 443-8). Springer, Berlin, Heidelberg.

[20][20]Yousif A, Nor SM, Abdullah AH, Bashir MB. A discrete firefly algorithm for scheduling jobs on computational grid. In: Yang XS. (eds) cuckoo search and firefly algorithm. Studies in Computational Intelligence 2014, p. 271-90. Springer International Publishing.

[21][21]Li J, Zhang GY, Gu GC. A multi-agent based architecture for network attack resistant system. In international conference on grid and cooperative computing 2003 (pp. 980-3). Springer, Berlin, Heidelberg.

[22][22]Foster I, Jennings NR, Kesselman C. Brain meets brawn: why grid and agents need each other. In proceedings of the third international joint conference on autonomous agents and multiagent systems (pp. 8-15). IEEE Computer Society.

[23][23]FIPA ACL Message Structure Specification. http://www.fipa.org/specs/fipa00061/SC00061G.html. Accessed 10 June 2017.

[24][24]Kim JS, Quang B, Rho S, Kim S, Kim S, Breton V, et al. Towards effective scheduling policies for many‐task applications: practice and experience based on HTCaaS. Concurrency and Computation: Practice and Experience. 2017; 29(21):1-15.

[25][25]Serrano JF, Suros RE. Design of a multiagent system for the management of resources in high speed networks. International symposium on communication of knowledge and conferences 2008 (pp. 71-6). International Institute of Informatics and Systemics.

[26][26]Foster I, Kesselman C, Nick JM, Tuecke S. Grid services for distributed system integration. Computer. 2002; 35(6):37-46.

[27][27]Shestak V, Chong EK, Maciejewski AA, Siegel HJ. Probabilistic resource allocation in heterogeneous distributed systems with random failures. Journal of Parallel and Distributed Computing. 2012; 72(10):1186-94.

[28][28]Jiang Y, Huang J, Ding J, Liu Y. Method of fault detection in cloud computing systems. International Journal of Grid and Distributed Computing. 2014; 7(3):205-12.

[29][29]Sugavanam P, Siegel HJ, Maciejewski AA, Oltikar M, Mehta A, Pichel R, et al. Robust static allocation of resources for independent tasks under makespan and dollar cost constraints. Journal of Parallel and Distributed Computing. 2007; 67(4):400-16.

[30][30]Yagoubi B, Meddeber M. Distributed load balancing model for grid computing. African Journal of Research in Computer Science and Applied Mathematics. 2010; 12:43-60.

[31][31]EGEE Middleware Design Team. EGEE Deliverable 1.4: EGEE Middleware Architecture. https://edms.cern.ch/document/594698/. Accessed 11 July 2017.

[32][32]http://www.upv.es/sma/teoria/agentes/tesiscif.pdf. Accessed 11 July 2017.

[33][33]Xiong Z, Zhang X, Liu L. Resource management model and resource discovery algorithm for P2PGrid. Journal of Networks. 2011; 6(8):1187-94.

[34][34]Wilkinson B, Allen M. Parallel programming: techniques and applications using networked workstations and parallel computers. Pearson Education; 2005.

[35][35]Foster I, Kesselman C, Nick JM, Tuecke S. The physiology of the grid. Grid computing: making the global infrastructure a reality. 2003, p.217-49.

[36][36]https://edms.cern.ch/document/674643/1. Accessed 11 July 2017.