In this paper we present discuss the task assignment problem for distributed systems. We also show how this problem is very similar to that of clustering transactions for load balancing purposes and for their efficient execution in a distributed environment. The formalization of these problems in terms of a graph theoretic representation of a distributed program, or of a set of related transactions, is given. The cost function which needs to be minimized by an assignment of tasks to processors or of transactions to clusters is detailed, and we survey related work, as well work on the dynamic load balancing problem. Since the task assignment problem is NP-hard, we present three novel heuristic algorithms that we have tested for solving it and compare them to the well-known greedy heuristic. These novel heuristics use neural networks, genetic algorithms and simulated annealing. Both the resulting performance and the computational cost for these algorithms is evaluated on a large number o...

Parallel simulation techniques have been investigated in a number of studies as a way to decrease long execution times of sequential simulations. In this work, we propose different parallel approaches for a sequential discrete event simulator developed previously to compare different strategies for task allocation when processors are subject to failures. The failure-repair mechanism is modeled by means of a parallel discrete event simulator. The main contribution of this work is to propose different parallel approaches for the discrete event simulator. A comparison is made of different heuristics which solve the tasks assignment problem. For the task allocation, the following heuristics are used: random neural model, genetic algorithm and simulated annealing. Some performance measures like program execution times, migration times, processor utilization factors, execution time of the heuristics are given for the different approaches. The performance is studied as a function of processor...