Using stochastic learning automata for job scheduling in distributed processing systems
Journal of Parallel and Distributed Computing
Learning automata: an introduction
Learning automata: an introduction
Optimal selection theory for superconcurrency
Proceedings of the 1989 ACM/IEEE conference on Supercomputing
Allocating Task Interaction Graphs to Processors in Heterogeneous Networks
IEEE Transactions on Parallel and Distributed Systems
Journal of Parallel and Distributed Computing - Special issue on parallel evolutionary computing
IEEE Transactions on Computers
Multiprocessor Scheduling with the Aid of Network Flow Algorithms
IEEE Transactions on Software Engineering
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A framework for task assignment in heterogeneous computing systems is presented in this work. The framework is based on a learning automata model. The proposed model can be used for dynamic task assignment and scheduling and can adapt itself to changes in the hardware or network environment. The important feature of the scheme is that it can work on multiple cost criteria, optimizing each criterion individually. The cost criterion could be a general metric like minimizing the total execution time, or an application specific metric defined by the user. The application task is modeled as a task flow graph(TFG), and the network of machines as a processor graph(PG). The automata model is constructed by associating every task in the TFG with a variable structure learning automaton [1]. The actions of each automaton correspond to the nodes in the PG. The reinforcement scheme of the automaton considered here is a linear scheme. Different heursitic techniques that guide the automata model to the optimal solution are presented. These heuristics are evaluated with respect to different cost metrics.