Allocating Modules to Processors in a Distributed System
IEEE Transactions on Software Engineering
Journal of Parallel and Distributed Computing - Special issue on parallel evolutionary computing
The grid: blueprint for a new computing infrastructure
The grid: blueprint for a new computing infrastructure
Heuristic Algorithms for Scheduling Independent Tasks on Nonidentical Processors
Journal of the ACM (JACM)
IEEE Transactions on Computers
Benchmarking and comparison of the task graph scheduling algorithms
Journal of Parallel and Distributed Computing
Static scheduling algorithms for allocating directed task graphs to multiprocessors
ACM Computing Surveys (CSUR)
Journal of Parallel and Distributed Computing
Dynamic, Competitive Scheduling of Multiple DAGs in a Distributed Heterogeneous Environment
HCW '98 Proceedings of the Seventh Heterogeneous Computing Workshop
Dynamic Matching and Scheduling of a Class of Independent Tasks onto Heterogeneous Computing Systems
HCW '99 Proceedings of the Eighth Heterogeneous Computing Workshop
Task Execution Time Modeling for Heterogeneous Computing Systems
HCW '00 Proceedings of the 9th Heterogeneous Computing Workshop
Iterative list scheduling for heterogeneous computing
Journal of Parallel and Distributed Computing
Mapping subtasks with multiple versions on an ad hoc grid
Parallel Computing - Heterogeneous computing
A survey on grid task scheduling
International Journal of Computer Applications in Technology
A parallel micro evolutionary algorithm for heterogeneous computing and grid scheduling
Applied Soft Computing
Scheduling in HC and Grids Using a Parallel CHC
Computational Intelligence
Solving very large instances of the scheduling of independent tasks problem on the GPU
Journal of Parallel and Distributed Computing
An efficient implementation of the Min-Min heuristic
Computers and Operations Research
Energy-Aware Scheduling on Multicore Heterogeneous Grid Computing Systems
Journal of Grid Computing
The Journal of Supercomputing
Hi-index | 0.00 |
Mixed-machine heterogeneous computing (HC) environments utilize a distributed suite of different high-performance machines, interconnected with high-speed links, to perform groups of computing-intensive applications that have diverse computational requirements and constraints. The problem of optimally mapping a class of independent tasks onto the machines of an HC environment has been proved, in general, to be NP-complete, thus requiring the development of heuristic techniques for practical usage. If the mapping has real-time requirements such that the mapping process is performed during task execution, fast greedy heuristics must be adopted. This paper investigates fast greedy heuristics for this problem and identifies the importance of the concept of task consistency in designing this mapping heuristic. We further propose task priority graph based fast greedy heuristics, which consider the factors of both task consistency and machine consistency (the same concept of consistency as in previous studies). A collection of 20 greedy heuristics, including 17 newly proposed ones, are implemented, analyzed, and systematically compared within a uniform model of task execution time. This model is implemented by the coefficient-of-variation based method. The experimental results illuminate the circumstances when a specific greedy heuristic would outperform the other 19 greedy heuristics.