An Automated Parameter Tuning Method for Ant Colony Optimization for Scheduling Jobs in Grid Environment

Abstract

The grid infrastructure has evolved as the integration and collaboration of multiple computer systems, networks, different databases and other network resources. The problem of scheduling in grid environment is an NP complete problem where conventional approaches like First Come First Serve (FCFS), Shortest Job First (SJF), Round Robin Scheduling algorithm (RR), Backfilling is not preferred because of the unexpectedly high computational cost and time in the worst case. Different algorithms, for example bio-inspired algorithms like Ant Colony Optimization (ACO), Artificial Bee Colony (ABC), Genetic Algorithm and Particle Swarm Optimization (PSO) are there which can be applied for solving NP complete problems. Among these algorithms, ACO is designed specifically to solve minimum cost problems and so it can be easily applied in grid environment to calculate the execution time of different jobs. Algorithms have different parameters and the performance of these algorithms extremely depends on the values of its parameters. In this paper, we have proposed a method to tune the parameters of ACO and discussed how parameter tuning affects the performance of ACO which in turn affects the performance of grid environment when applied for scheduling.

Parameters Sensitivity Analysis of Ant Colony Based Clustering: Application for Student Grouping in Collaborative Learning Environment

“…As a matter of fact, if it is large, the pheromone concentration will be highly concentrated, making the algorithm fall into a local optimum. In the same context, [15] proposed an algorithm for tuning ACO parameters (p and ∆ τ ) and they proved how they affect the performance of ACO which affects in its turn the performance of grid environment when applied for scheduling. Actually, the pheromone update quantity ∆ τ aims to enhance the diversity of algorithm search and to avoid getting into local optimal.…”

“…Actually, ant colony parameters, in literature, could have different labels. For example, [14] and [15] tuned the same parameter q 0 and p 0 respectively which control the probability of ants movements between objects. Thus, they possess the same role.…”

“…• P recision(i, j): the proportion of items in cluster j that are of class i given by ( 14). • Recall(i, j): the proportion of class i that belongs to that cluster j given by (15).…”

Planning and Design of Intelligent Scheduling System for Subway Staff

“…(3). The system should realize the automatic generation function of the station staff scheduling plan, be able to identify the rest days independently set by staff, and automatically complete the adjustment work of idle positions in relevant periods based on the analysis of total working hours difference data [12].…”