Colony of cooperating agents based independent job scheduling in a computation grid

Abstract

PurposeGrid computing is gaining more significance in the high‐performance computing world. This concept leads to the discovery of solutions for complicated problems regarding the diversity of available resources among different jobs in the grid. However, the major problem is the optimal job scheduling for heterogeneous resources, in which each job needs to be allocated to a proper grid's node with the appropriate resources. An important challenge is to solve optimally the scheduling problem, because the capability and availability of resources vary dynamically and the complexity of scheduling increases with the size of the grid. The purpose of this paper is to present a framework which combines the fuzzy C‐mean (FCM) clustering with an ant colony optimization (ACO) algorithm to improve the scheduling decision when the grid is heterogeneous.Design/methodology/approachIn the proposed model, the FCM algorithm classifies the jobs into appropriate classes, and the ACO algorithm maps the jobs to the appropriate resources. The ACO is characterized by ant‐like mobile agents that cooperate and stochastically explore a network, iteratively building solutions based on their own memory and on the traces (pheromone levels) left by other agents.FindingsThe simulation is done by using historical information on jobs in a grid. The experimental results show that the proposed algorithm can allocate jobs more efficiently and more effectively than the traditional algorithms for scheduling policies.Originality/valueThe paper provides a scheduling model based on FCM clustering and ACO algorithm for grid scheduling. The authors compared the performance of the proposed algorithm with the performance of various job‐scheduling algorithms in the grid computing environment. The comparison results show that the proposed algorithm outperforms other algorithms and gives optimal results.

Integration of Kalman Filter with Ant Colony Optimization for Solving the Job Shop Scheduling Problem

“…Huang (2013) improves ant colony optimization for the Flexible Job Shop Scheduling Problem, whereas El Khoukhi (2016) tackles it with machine unavailability restrictions [32,33]. By combining fuzzy clustering and ant colony optimization, Helmy (2011) presents a framework for heterogeneous grid scheduling, while Banharnsakun (2011) recommends using the Best-so-far Artificial Bee Colony for the Job Shop Scheduling Problem [34,35]. Taken as a whole, this research helps to advance cutting-edge techniques and algorithms for handling different scheduling and planning problems in a variety of sectors.…”