Near-Optimal Scheduling based on Immune Algorithms in Distributed Environments

One of the most important management aspects in Grid systems is task scheduling. This component should try to achieve two main objectives: efficient use of available resources and high performance in solving tasks given by members of virtual organizations. The scheduling problem is one of the hardest problems and it is proved that it is an NP-Complete problem. In order to have good performances heuristic algorithms are required. This paper presents a near-optimal algorithm for dependent task scheduling in distributed systems. The algorithm is based on both genetic and immune algorithms. The genetic componment is used in order to evolve a population of chromosomes representing potential solutions. In order to increase the performances of the genetic algorithm in terms of convergence times, in the initialization stage it is used an immune algorithm that produces an initial population with a good average fitness.