Job Scheduling using Ant Colony Optimization in Grid ... - IEEE Xplore

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Department of computer engineering & technology ... scheduling sequential jobs in grid systems. ... Keywords: GRID COMPUTING; JOB SCHEDULING; ACO.
International Conference on Electrical, Electronics, and Optimization Techniques (ICEEOT) - 2016

Job Scheduling using Ant Colony Optimization in Grid environment Oshin Department of computer engineering & technology GNDU Amritsar [email protected]

Abstract: Grid computing proposes a dynamic and earthly distributed organization of resources that harvest ideal CPU cycle to drift advance computing demands and accommodate user's prerequisites. Heterogeneous gridsdemand efficient allocation and scheduling strategies to cope up with the expanding grid automations.In order to obtain optimal scheduling solutions, primary focus of research has shifted towards metaheuristic techniques. The paper uses different parameters to provide analytical study of variants of Ant Colony Optimization for scheduling sequential jobs in grid systems. Based on the literature analysis, one can summarize that ACO is the most convincing technique for schedulingproblems. However, incapacitation of ACO to fix up a systematized startup and poor scattering capability cast down its efficiency.To overpower these constraints researchers have proposed different hybridizations of ACO that manages to sustain more effective results than standalone ACO. Keywords: GRID COMPUTING; JOB SCHEDULING; ACO

I.

INTRODUCTION

Globus toolkit leads toward acknowledgment of grid system in the early nineties.To distribute services of virtual system over the network of resources grid computer has provided a huge platform [1]. Main aim of this system is abbreviating turn-around time in addition to accomplish considerable amount of throughput [2]. Grid computing is authoritative and globally distributed organization [3] which includes world’s most capable calculating platforms to fix highly complex problems by combining computing power. Various issues from e-Scientific disciplines are solved with grid computing, which involves building a supercomputer from different single computers systems placed in separate geographical location with the motive of improving efficiency of solving problem when compare to conventional network [4]. Grid type is classified into the utility grid, knowledge grid, data-centric grid, computational grid, this grid classification is on the bases of facts, calculation, intercommunication, service, knowledge, and application support [5] [6]. Grid computing has been placed so adequately to counter diverse real-life complexities [7] [8] [9]. A computational framework is significantly affected by scheduling algorithm [4]. Basic methodologies like greedy algorithm or FCFS can be utilized for the execution of the scheduling algorithm. In any case, grid framework which has heterogeneous network, desire for more developed algorithm with the perspective of achieving better

Amit Chhabra Department of computer engineering & technology GNDU Amritsar [email protected]

results. Describing problems in grid environment is known as NP-complete problem [9] and need more sophisticated algorithms. With the view of resolving such problems, heuristic and metaheuristic algorithms [10] are taken into concern. To develop optimum or near to optimum solution metaheuristic algorithms are efficient supporters. Certain bioinspired algorithms fall under the class of metaheuristic algorithms, such as Bat Algorithm (BA), Swam Optimization (PSO), Cuckoo Search (CS), Ant Colony Optimization (ACO) and Simulated Annealing (SA) [11].Out of these, ACO is a broadly acknowledged methodology for understanding and solving scheduling complexity [12]. ACO has turned out to be a productive algorithm for determining scheduling issues [13]. ACO is inspired from ant’s nature of looking for shortest path. Scheduling of jobs is done by allocating resources in grid environment by scheduler recommended by ACO. II.

TRADITIONAL ANT COLONY OPTIMIZATION

Marco Dorrigo designed “nature inspired" algorithm termed as ACO,encouraged from the forging role of ants[14] which produces an optimum or near-optimum path from their dwell to food as demonstrated in fig. 2. It had been discovered that ants when searching for meals, are generally very motivated by pheromones.

Fig. 1 ACO nature at different time intervals [12]

Fig. 1 Demonstrates behavior of ants in four trails to reach the optimized path:

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International Conference on Electrical, Electronics, and Optimization Techniques (ICEEOT) - 2016

a) Initiatory both the ants can opt any of the routes with equal probability since the surroundings are ‘clean’. b) Ants randomly traverse the path, briefer route is being embraced by one of the ants and longer route is chosen by the departed one. A synthetic substance termed as a pheromone is being laid by the ants in the process of migration. c) With the passage of time, pheromone deposition would godenser on the smaller trail. To find the future move, the probability function is being used byants that are weighed depending upon the pheromone deposition on the trail. Therefore,ants prefer the shortest path. d) After some specific period of time, evaporation of pheromone would begin and only the smaller trail would get denser by pheromone deposition. Therefore, ants will prefer the smaller route. III.

VARIANTS OF ANT COLONY ALGORITHM

A. ANT SYSTEM (AS) In 1991, Dorrigo proposed ANT SYSTEM as an enhancement of ACO and serviced “Travelling Salesman Problem” (TSP) [16] in the act of assessment issue. In TSP, there are T cities and one salesperson. T cities are hooked to one another. The salesperson is supposed to traverse every city of T exclusively once. The solution to the problem is finding the précised tour travel by the salesperson in a small interval of time. There are mainly two phases in AS. •

The first phase construction of solution is based on the random proportional rule. ೌ

ൣ௉ோ೐ǡ೑ሺ೎ሻ ൧ ൈൣ஻೐ǡ೑ ൧



݂݅‫ܼߝݒ‬௘ (1) ܴ௘ǡ௙ሺ௖ሻ ൌ ቐఀ೔ഄೋ೐ ൣ௉ோ೐ǡ೑ሺ೎ሻ൧ ൈൣ஻೐ǡ೑൧್ Ͳǡ ‫݁ݏ݅ݓݎ݄݁ݐ݋‬ ೌ

ܴ௘ǡ௙ሺ௖ሻ Is the transit expectations of e node where (e,f)is the edge of the graph and c is total nodes. ܴܲ௘ǡ௙ሺ௖ሻ is pheromone value on edge (e,f). ‫ܤ‬௘ǡ௙  ൌ ͳൗ ܶ And ܶ௘ǡ௙ is the area by which e and f are ௘ǡ௙

separated. ܼ௘ includes all nodes adjacent to e. a and b authorize the effect of the route and visionless respectively and i is the ithant. •

The second phase, Updating of pheromone includes pheromone evaporation and pheromone deposition. The abundance of pheromone is updated on the edge by the subsequent equation: ܴܲ௘ǡ௙ሺ௖ାଵሻ = (1-ȕ) ܴܲ௘ǡ௙ሺ௖ሻ )+

௡ ߑ௜ୀଵ ߂ܴܲ௘ǡ௙ (2)

ȕ is the parameter where (0< ȕ