Heuristic Algorithms for Scheduling Independent Tasks on Nonidentical Processors
Journal of the ACM (JACM)
Journal of Parallel and Distributed Computing
Distributed Systems: Principles and Paradigms
Distributed Systems: Principles and Paradigms
Experiments with Scheduling Using Simulated Annealing in a Grid Environment
GRID '02 Proceedings of the Third International Workshop on Grid Computing
Dynamic Matching and Scheduling of a Class of Independent Tasks onto Heterogeneous Computing Systems
HCW '99 Proceedings of the Eighth Heterogeneous Computing Workshop
Heuristics for Scheduling Parameter Sweep Applications in Grid Environments
Heuristics for Scheduling Parameter Sweep Applications in Grid Environments
Algorithms for flow time scheduling
Algorithms for flow time scheduling
Journal of Parallel and Distributed Computing
Computational models and heuristic methods for Grid scheduling problems
Future Generation Computer Systems
Statistical measures for quantifying task and machine heterogeneities
The Journal of Supercomputing
A Review on Task Performance Prediction in Multi-core Based Systems
CIT '11 Proceedings of the 2011 IEEE 11th International Conference on Computer and Information Technology
Scalable and Energy-Efficient Scheduling Techniques for Large-Scale Systems
CIT '11 Proceedings of the 2011 IEEE 11th International Conference on Computer and Information Technology
A parallel micro evolutionary algorithm for heterogeneous computing and grid scheduling
Applied Soft Computing
Information Sciences: an International Journal
Resource management of distributed virtual machines
International Journal of Ad Hoc and Ubiquitous Computing
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For heterogeneous distributed computing systems, important design issues are scalability and system optimization. Given such systems, it is crucial to develop low computational complexity algorithms to schedule tasks in a manner that exploits the heterogeneity of the resources and applications. In this paper, we report and evaluate three scalable, and fast scheduling heuristics for highly heterogeneous distributed computing systems. We conduct a comprehensive performance evaluation study using simulation. The benchmarking outlines the performance of the schedulers, representing scalability, makespan, flowtime, computational complexity, and memory utilization. The set of experimental results shows that our heuristics perform as good as the traditional approaches, for makespan and flowtime, while featuring lower complexity, lower running time, and lower used memory. The experimental results also detail the various scenarios under which certain algorithms excel and fail.