Total views : 352

A Constraint-based Decentralized Task Routing Approach to Large-Scale Scheduling in Cloud Environment


  • School of Computing, SASTRA University, Thanjavur - 613401, Tamil Nadu, India
  • Information and Communication Technology, SASTRA University, Thanjavur - 613401, Tamil Nadu, India


This article mainly focused on cloud scheduling with constraint based decentralized task routing. The job of scheduling tasks across various nodes in a hierarchical network scenario is an exigent problem. The concept of decentralized distribution scheme proposed in base paper is time-consuming since it has to compute the availability function for each and every node. In this paper we proposed a CBDA (Constraint Based Decentralized Algorithm) which offers the expediency of being quick and “Make-span minimization policy” is implemented to reduce the completion time of the currently executing nodes. In our presumption, the submission nodes are semi centralized and it can store the availability information of the nodes or routers within its area. This paper considers the allotment of the tasks to the execution nodes which are unoccupied by other tasks. The dynamic allotment of the tasks to the nodes in the tree based approach is the major criteria for selecting the desired node. This paper proposes a trade-off between fully centralized model and the decentralized model by implementing a new constraint based decentralization scheme which saves time consumption and enhances efficiency of task scheduling.


Cloud Computing, Constraint based Method Scheduling, Decentralized Router, Dynamic Task

Full Text:

 |  (PDF views: 162)


  • Tannenbaum T, Wright D, Miller K, Livny M. Condor: A distributed job scheduler. Beowulf Cluster Computing with Linux; 2002. p. 307–50.
  • Anderson D. A system for public-resource computing and storage. Proceedings of the 5th IEEE/ACM International Workshop on Grid Computing, IEEE Computer Society;2004. p. 4–10.
  • Celaya J, Arronategui U. A task routing approach to largescale scheduling. Future Generation Computer Systems.2013; 29(5):1097–111.
  • Abramson D, Bethwaite B, Enticott C, Garic S, Peachey T. Parameter exploration in science and engineering using many-task computing. IEEE Transactions on Parallel and Distributed Systems. 2011; 22(6):960–73.
  • Dean J, Ghemawat S. Map reduce: Simplified data processing on large clusters. Communications of the ACM. 2008;51(1):1–13.
  • Rodero I, Guim F, Corbalan J, Fong L, Sadjadi SM. Grid broker selection strategies using aggregated resource information. Future Generation Computer Systems. 2010; 26(1):72–86.
  • Cai M, Hwang K. Distributed aggregation algorithms with load-balancing for scalable grid resource monitoring.IEEE International Parallel and Distributed Processing Symposium, IPDPS’07; 2007. p. 1–10.
  • Stoica I, Morris R, Liben-Nowell D. A scalable peer-topeer lookup protocol for Internet applications. IEEE/ACM Transactions on Networking. 2003; 11(1):17–32.
  • Kim J-S, Nam B, Keleher P, Marsh M, Bhattacharjee B, Sussman A. Tradeoffs in matching jobs and balancing load for distributed desktop grids. Future Generation Computer Systems. 2008; 24(5):415–24.
  • Ratnasamy S, Francis P, Handley M, Karp R, Schenker S. A scalable content addressable network. Proceedings of the 2001 Conference on Applications, Technologies, Architectures, and Protocols for Computer Communications, SIGCOMM’01; 2006. p. 161–72.
  • Bagheri R, Jahanshahi M. Scheduling workflow applications on the heterogeneous cloud resources. Indian Journal of Science and Technology. 2015; 8(12):1–8.
  • Shyamala K, Rani ST. An analysis on efficient resource allocation mechanisms in cloud computing. Indian Journal of Science and Technology. 2015; 8(9):814–21.
  • Priyadarsini RJ, Arockiam L. PBCOPSO: A parallel optimization algorithm for task scheduling in cloud environment. Indian Journal of Science and Technology. 2015;8(16):1–5.


  • There are currently no refbacks.

Creative Commons License
This work is licensed under a Creative Commons Attribution 3.0 License.