The Learning Behaviour of a Scheduler using a Stochastic Learning Automaton

This paper discusses a load balancing heuristic in a general-purpose distributed computer system. We implemented a task scheduler based on the concept of a Stochastic Learning Automaton on a network of Unix workstations. The used heuristic and our implementation are shortly described. Creating an executable artificial workload, a number of experiments examined different learning schemes. Using a linear reward-- penalty scheme resulted in the best performance of the scheduler. Another series of experiments looked at different ways to evaluate the goodness of a scheduling decision, another aspect of the learning behaviour. Instead of using a simple binary (qualitative) measure, a quantitative evaluation allowed for a more stable and therefore better learning behaviour. Index terms: Distributed systems, heuristics, load balancing, queueing systems, stochastic learning automata 1 Introduction The advantages of a distributed computer system over a single powerful general-purpose computi..