Static mapping heuristics for tasks with dependencies, priorities, deadlines, and multiple versions in heterogeneous environments

Heterogeneous computing (HC) environments composed of interconnected machines with varied computational capabilities are well suited to meet the computational demands of large, diverse groups of tasks. The problem, of mapping (defined as matching and scheduling) these tasks onto the machines of a distributed HC environment. has been shown, in general, to be NP-complete. Therefore; the development of heuristic techniques to find near-optimal solutions is required. In the HC environment investigated, tasks had deadlines, priorities, multiple versions, and may be composed of communicating subtasks. The best static (off-line) techniques from some previous studies were adapted and applied to this mapping problem: a genetic algorithm (GA), a GENITOR-style algorithm., and a greedy Min-min technique. Simulation studies compared the performance of these heuristics in several overloaded scenarios, i.e., not all tasks executed. The performance measure used was a sum of weighted priorities of tasks that completed before their deadline, adjusted based on the version of the task used. It is shown that for the cases studied here, the GENITOR technique found the best results, but the faster Min-min approach also performed very well.