The application of Ants' society algorithm for Management of resources in continuous bilateral auction

Background: The main purpose of this paper is to improve the efficiency of grid computing by means of Ants' society algorithm. Application of this algorithm in various problem led to an improvement in efficiency and reduction in processing time. This enables us to use this algorithm in grid computing. Economic solutions in the field of management of heterogeneous resources for grid computing showed significant performance. The main idea was economic solutions for product exchange in market. This paper aims to introduce a new method for bilateral auction scenario by means of genetic algorithm (GA). In this method, by making resources intelligent, we move the packages of call for proposal so that it can reduce response time as well as being able to supply resources with lower prices. For simplicity in controlling packages, we used the network structure in implementation. Applied structure includes routers and communication of users and auctioners and auctioners and resources owners. The method was implemented using GridSim simulator. This is an open source software written in Java programming language. Results reveal that the method of bilateral auction using GA reduces sale stages and consequently leads to faster responding to requests and also resources are supplied with a lower cost

Dual-phase just-in-time workflow scheduling in P2P grid systems

This paper presents a fully decentralized justin-time workflow scheduling method in a P2P Grid system. The proposed solution allows each peer node to autonomously dispatch inter-dependent tasks of workflows to run on geographically distributed computers. To reduce the workflow completion time and enhance the overall execution efficiency, not only does each node perform as a scheduler to distribute its tasks to execution nodes (or resource nodes), but the resource nodes will also set the execution priorities for the received tasks. By taking into account the unpredictability of tasks' finish time, we devise an efficient task scheduling heuristic, namely dynamic shortest makespan first (DSMF), which could be applied at both scheduling phases for determining the priority of the workflow tasks. We compare the performance of the proposed algorithm against seven other heuristics by simulation. Our algorithm achieves 20%~60% reduction on the average completion time and 37.5%~90% improvement on the average workflow execution efficiency over other decentralized algorithms. © 2010 IEEE.published_or_final_versionProcessing (ICPP 2010), San Diego, CA., 13-16 September 2010. In Proceedings of the 39th ICCP, 2010, p. 238-24

Decentralized dynamic workflow scheduling for grid computing using reinforcement learning

10.1109/ICON.2006.302614Proceedings - 2006 IEEE International Conference on Networks, ICON 2006 - Networking-Challenges and Frontiers190-9