Deterministic Learning Automata Solutions to the Equipartitioning Problem
IEEE Transactions on Computers
Learning automata: an introduction
Learning automata: an introduction
Multiprocessor scheduling using mean-field annealing
Future Generation Computer Systems - Special issue: Bio-inspired solutions to parallel processing problems
Static scheduling algorithms for allocating directed task graphs to multiprocessors
ACM Computing Surveys (CSUR)
Partitioning and Scheduling Parallel Programs for Multiprocessors
Partitioning and Scheduling Parallel Programs for Multiprocessors
Learning Automata and Stochastic Optimization
Learning Automata and Stochastic Optimization
Computers and Intractability: A Guide to the Theory of NP-Completeness
Computers and Intractability: A Guide to the Theory of NP-Completeness
Hypertool: A Programming Aid for Message-Passing Systems
IEEE Transactions on Parallel and Distributed Systems
A Comparison of Heuristics for Scheduling DAGs on Multiprocessors
Proceedings of the 8th International Symposium on Parallel Processing
An Incremental Genetic Algorithm Approach to Multiprocessor Scheduling
IEEE Transactions on Parallel and Distributed Systems
A comparison of multiprocessor task scheduling algorithms with communication costs
Computers and Operations Research
Journal of Parallel and Distributed Computing
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DAG scheduling is of great importance to optimal distribution of tasks in parallel and distributed systems. In this paper a novel approach to DAG scheduling, utilizing learning automata across distributed systems, is proposed. The learning process begins with an initial population of randomly generated learning automata. Each automaton by itself represents a stochastic scheduling. The scheduling is optimized within a learning process. Compared with current genetic approaches to DAG scheduling better results are achieved. The main reason underlying this achievement is that an evolutionary approach such as genetics looks for the best chromosomes within genetic populations whilst in the approach presented in this paper learning automata is applied to find the most suitable position for the genes in addition to looking for the best chromosomes. The scheduling resulted from applying our scheduling algorithm to some benchmark task graphs are compared with the existing ones.