Hybrid ant colony system algorithm for static and dynamic job scheduling in grid computing

Grid computing is a distributed system with heterogeneous infrastructures. Resource management system (RMS) is one of the most important components which has great influence on the grid computing performance. The main part of RMS is the scheduler algorithm which has the responsibility to map submitted tasks to available resources. The complexity of scheduling problem is considered as a nondeterministic polynomial complete (NP-complete) problem and therefore, an intelligent algorithm is required to achieve better scheduling solution. One of the prominent intelligent algorithms is ant colony system (ACS) which is implemented widely to solve various types of scheduling problems. However, ACS suffers from stagnation problem in medium and large size grid computing system. ACS is based on exploitation and exploration mechanisms where the exploitation is sufficient but the exploration has a deficiency. The exploration in ACS is based on a random approach without any strategy. This study proposed four hybrid algorithms between ACS, Genetic Algorithm (GA), and Tabu Search (TS) algorithms to enhance the ACS performance. The algorithms are ACS(GA), ACS+GA, ACS(TS), and ACS+TS. These proposed hybrid algorithms will enhance ACS in terms of exploration mechanism and solution refinement by implementing low and high levels hybridization of ACS, GA, and TS algorithms. The proposed algorithms were evaluated against twelve metaheuristic algorithms in static (expected time to compute model) and dynamic (distribution pattern) grid computing environments. A simulator called ExSim was developed to mimic the static and dynamic nature of the grid computing. Experimental results show that the proposed algorithms outperform ACS in terms of best makespan values. Performance of ACS(GA), ACS+GA, ACS(TS), and ACS+TS are better than ACS by 0.35%, 2.03%, 4.65% and 6.99% respectively for static environment. For dynamic environment, performance of ACS(GA), ACS+GA, ACS+TS, and ACS(TS) are better than ACS by 0.01%, 0.56%, 1.16%, and 1.26% respectively. The proposed algorithms can be used to schedule tasks in grid computing with better performance in terms of makespan

Immediate mode scheduling of independent jobs in computational grids

With the emerging paradigm of grid computing and the development of grid infrastructures, grid-based applications are becoming a common approach for solving many complex, large-scale problems in science and engineering. In order to benefit from the large computing power of grid systems, efficient allocation of jobs to resources is necessary. In this work, we consider the allocation problem in immediate mode, in which jobs are allocated as soon as they arrive in the system. We implemented several methods and measured four parameters of the system: makespan, flow- time, resource utilization and matching proximity. The immediate methods are especially interesting when good quality allocations are necessary in very short time. The considered methods have been tested using the most difficult benchmark in the literature for the problem. The computational results allowed us to identify which of considered methods perform better for makespan, flowtime, resource utilization and matching proximity. Also, we were able to evaluate the usefulness of such methods if we knew in advance certain grid characteristics such as degree of consistency of computing, heterogeneity of jobs and resources.Peer Reviewe

Immediate mode scheduling of independent jobs in computational grids

With the emerging paradigm of grid computing and the development of grid infrastructures, grid-based applications are becoming a common approach for solving many complex, large-scale problems in science and engineering. In order to benefit from the large computing power of grid systems, efficient allocation of jobs to resources is necessary. In this work, we consider the allocation problem in immediate mode, in which jobs are allocated as soon as they arrive in the system. We implemented several methods and measured four parameters of the system: makespan, flow- time, resource utilization and matching proximity. The immediate methods are especially interesting when good quality allocations are necessary in very short time. The considered methods have been tested using the most difficult benchmark in the literature for the problem. The computational results allowed us to identify which of considered methods perform better for makespan, flowtime, resource utilization and matching proximity. Also, we were able to evaluate the usefulness of such methods if we knew in advance certain grid characteristics such as degree of consistency of computing, heterogeneity of jobs and resources.Peer Reviewe