Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Task scheduling in parallel and distributed systems
Task scheduling in parallel and distributed systems
Genetic algorithms + data structures = evolution programs (3rd ed.)
Genetic algorithms + data structures = evolution programs (3rd ed.)
Journal of Parallel and Distributed Computing - Special issue on parallel evolutionary computing
Simulated annealing and combinatorial optimization
DAC '86 Proceedings of the 23rd ACM/IEEE Design Automation Conference
Scheduling Multiprocessor Tasks with Genetic Algorithms
IEEE Transactions on Parallel and Distributed Systems
A comparison of list schedules for parallel processing systems
Communications of the ACM
Efficient Local Search for DAG Scheduling
IEEE Transactions on Parallel and Distributed Systems
Journal of Parallel and Distributed Computing
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
A taxonomy of scheduling in general-purpose distributed computing systems
IEEE Transactions on Software Engineering
Task Matching and Scheduling in Heterogeneous Systems Using Simulated Evolution
IPDPS '01 Proceedings of the 15th International Parallel & Distributed Processing Symposium
ICPADS '02 Proceedings of the 9th International Conference on Parallel and Distributed Systems
A simple scheduling heuristic for heterogeneous computing environments
ISPDC'03 Proceedings of the Second international conference on Parallel and distributed computing
Combinatorial optimization by stochastic evolution
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
Minimizing Energy Consumption for Precedence-Constrained Applications Using Dynamic Voltage Scaling
CCGRID '09 Proceedings of the 2009 9th IEEE/ACM International Symposium on Cluster Computing and the Grid
Reliability-Oriented Genetic Algorithm for Workflow Applications Using Max-Min Strategy
CCGRID '09 Proceedings of the 2009 9th IEEE/ACM International Symposium on Cluster Computing and the Grid
Contention-aware scheduling with task duplication
Journal of Parallel and Distributed Computing
Battery-aware task scheduling in distributed mobile systems with lifetime constraint
Proceedings of the 16th Asia and South Pacific Design Automation Conference
Future Generation Computer Systems
A parallel bi-objective hybrid metaheuristic for energy-aware scheduling for cloud computing systems
Journal of Parallel and Distributed Computing
Journal of Signal Processing Systems
Robust static resource allocation of DAGs in a heterogeneous multicore system
Journal of Parallel and Distributed Computing
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Consider DAG scheduling for a large heterogeneous system, which consists of processors with varying processing capabilities and network links with varying bandwidths. The search space of possible task schedules for this problem is immense. One possible approach for this optimization problem, which is NP-hard, is to start with the best task schedule found by a fast deterministic task scheduling algorithm, and then iteratively attempt to improve the task schedule by employing a general random guided search method. However, such an approach can lead to extremely long search times, and the solutions found are sometimes not significantly better than those found by the original deterministic task scheduling algorithm. In this paper, we propose an alternative strategy, termed Push-Pull, which starts with the best task schedule found by a fast deterministic task scheduling algorithm, and then iteratively attempts to improve the current best solution using a deterministic guided search method. Our simulation results show that, given similar running times, the Push-Pull algorithm performs well, achieving similar or better results than all of the other algorithms being compared.