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New Roadmap for Elastic Grid Resource Matchmaking


Affiliations
1 Department of Information Technology, Sathyabama University, Chennai, Tamil Nadu, India
2 Department of Computer Applications, St. Joseph’s College of Engineering, Chennai, Tamil Nadu, India
 

Dynamic grid computing possesses the power of several resources in one to solve the problems optimally at run time. Main focus of grid computing is on matchmaking and scheduling. The proposed system finds the resources which are suitable for a given job, from the available grid resources. A Elastic Grid design system is proposed to design the topology for a grid network, more number of the quality of service attributes used, optimal fuzzy scheduling, profitable resource allocation increase perfection of grid balancing, coordinated and optimized resource sharing, decrease the monitoring and migration work and detect the fault tolerance grid resource from the available resources based on Optimal Resource management Techniques Operations (ORMTO).

Keywords

Dynamic Grid Computing, Grid Resource Matchmaking, ORMTO, Fuzzy Scheduling, Grid Balancing
User

  • Joseph J, and Fellenstein C (2003). Grid Computing Book, PHI, Chapter 1, Prentice Hall PTR Publisher, 1–7.
  • Barzegar B, Esmaeelzadeh H et al. (2011). A new method on resource management in grid computing systems based on QoS and semantics, Indian Journal of Science and Technology, vol 4(11), 1416–1419.
  • Xiaoshan H, Sun X et al. (2003). Qos guided min-min heuristic for grid task scheduling, Journal of Computer Science and Technology - Grid Computing, vol 18(4), 442–451.
  • Naik V K (2007). Prediction based resource matching for grid environments, US 2008/0172673A1.
  • Wu M, and Sun X (2003). A general self-adaptive task scheduling system for non-dedicated heterogeneous computing, 2003 IEEE International Conference on Cluster Computing, 2003 Proceedings, 354–361.
  • Agarwal A, and Kuma P (2011). Multi dimensional QOS oriented task scheduling in grid environment, International Journal of Grid Computing & Applications, vol 2(1), 28–37.
  • Qiang G, Heng-wei Z et al. (2011). A grid resource matching algorithm, Computer Science and Network Technology, International Conference, 142–145.
  • F Pan, Wang J et al. (2011). Toward optimal deployment of communication-intensive cloud applications, 2011 IEEE International Conference on Cloud Computing (CLOUD), 460–467.
  • Chtepen M, Claeys F H A et al. (2009). Adaptive task check pointing and replication: toward efficient fault-tolerant grids, IEEE Transactions on Parallel and Distributed systems, vol 20(2), 180–190.
  • Nordin M I B, Abdullah A B et al. (2011), Goal-based cloud broker for medical informatics application: a proposed goal-based request and selection strategy, International Confe-rence on Telecommunication Technology and Applications, 35–40.
  • Blythe J, Jain S et al. (2005). Task scheduling strategies for workflow-based applications in grids, Cluster Computing and the Grid, 2005, CCGrid 2005, IEEE International Symposium, vol 2, 759–767.
  • Ning X, and Shaohua Y (2013). A load-balanced crosspoint-queued switch fabric, China Communications, vol 10(2), 134–142.
  • Sadasivam G S, Rajendran V V (2009). An efficient approach to task scheduling in computational grids, International Journal of Computer Science and Applications, Techno Mathematics Research Foundation, vol 6(1), 53–69.
  • Batista D M, Drummond A C et al. (2008). Scheduling grid tasks under uncertain demands, SAC ‘08 Proceedings of the 2008 ACM symposium on Applied computing, 2041–2045.
  • Nazir B, Hassan M F et al. (2009). Adaptive task scheduling strategy for economy based grid, International Symposium on Computing, Communication, and Control, 164–169.
  • Wolski R, Brevik J et al. (2004). Grid resource allocation and control using computational economies, 1–27, Available from: http://www.cs.ucsb.edu/~rich/publications/gc-book.pdf
  • Brooke J, Fellows D et al. (2004). Semantic matching of grid resource descriptions, Lecture notes in computer science, vol 3165, 240–249.
  • Hu Z, Hu Z et al. (2010). A service-clustering-based dynamic scheduling algorithm for grid tasks, International Journal of Grid and Distributed Computing, vol 3(3), 53–66.
  • Rao K R M, Ramachandram S et al. (2011). A reliable distributed grid scheduler for independent tasks, International Journal of Computer Science, vol 8(2), 213–223.

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  • New Roadmap for Elastic Grid Resource Matchmaking

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Authors

R. Surendran
Department of Information Technology, Sathyabama University, Chennai, Tamil Nadu, India
B. Parvatha Varthini
Department of Computer Applications, St. Joseph’s College of Engineering, Chennai, Tamil Nadu, India

Abstract


Dynamic grid computing possesses the power of several resources in one to solve the problems optimally at run time. Main focus of grid computing is on matchmaking and scheduling. The proposed system finds the resources which are suitable for a given job, from the available grid resources. A Elastic Grid design system is proposed to design the topology for a grid network, more number of the quality of service attributes used, optimal fuzzy scheduling, profitable resource allocation increase perfection of grid balancing, coordinated and optimized resource sharing, decrease the monitoring and migration work and detect the fault tolerance grid resource from the available resources based on Optimal Resource management Techniques Operations (ORMTO).

Keywords


Dynamic Grid Computing, Grid Resource Matchmaking, ORMTO, Fuzzy Scheduling, Grid Balancing

References





DOI: https://doi.org/10.17485/ijst%2F2013%2Fv6i8%2F36351