IEEE Transactions on Parallel and Distributed Systems
Multiprocessor scheduling in a genetic paradigm
Parallel Computing
Journal of Parallel and Distributed Computing - Special issue on parallel evolutionary computing
Efficient scheduling of arbitrary task graphs to multiprocessors using a parallel genetic algorithm
Journal of Parallel and Distributed Computing - Special issue on parallel evolutionary computing
On Exploiting Task Duplication in Parallel Program Scheduling
IEEE Transactions on Parallel and Distributed Systems
Performance-Effective and Low-Complexity Task Scheduling for Heterogeneous Computing
IEEE Transactions on Parallel and Distributed Systems
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 Genetic Algorithm for Multiprocessor Scheduling
IEEE Transactions on Parallel and Distributed Systems
DSC: Scheduling Parallel Tasks on an Unbounded Number of Processors
IEEE Transactions on Parallel and Distributed Systems
An Incremental Genetic Algorithm Approach to Multiprocessor Scheduling
IEEE Transactions on Parallel and Distributed Systems
Artificial immune systems applied to multiprocessor scheduling
PPAM'05 Proceedings of the 6th international conference on Parallel Processing and Applied Mathematics
A heuristic-based hybrid genetic algorithm for heterogeneous multiprocessor scheduling
Proceedings of the 12th annual conference on Genetic and evolutionary computation
Information Sciences: an International Journal
Review Article: Recent Advances in Artificial Immune Systems: Models and Applications
Applied Soft Computing
Proceedings of the 15th annual conference on Genetic and evolutionary computation
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Multiprocessor task scheduling is a widely studied optimization problem in the field of parallel computing. Many heuristic-based approaches have been applied to finding schedules that minimize the execution time of computing tasks on parallel processors. In this paper, we design an algorithm based on Artificial Immune Systems (AIS) to scheduling for heterogeneous computing environments. This approach distinguishes itself from many existing approaches in two aspects. First, it restricts the use of AIS to find optimal task-processor mapping, while taking advantage of heuristics used by deterministic scheduling approaches for task sequence assignment. Second, the calculation of the affinity takes into account both the solution quality and the distribution of population in the solution space. Empirical studies on benchmark task graphs show that this algorithm significantly outperforms HEFT, a deterministic algorithm. Further experiments also indicate that the algorithm is able to maintain high quality search even though a wide range of parameter settings are used.