Abstract
In a distributed heterogeneous computing system, the resources have different capabilities and tasks have different requirements. To maximize the performance of the system, it is essential to assign the resources to tasks (match) and order the execution of tasks on each resource (schedule) to exploit the heterogeneity of the resources and tasks. Dynamic mapping (defined as matching and scheduling) is performed when the arrival of tasks is not known a priori. In the heterogeneous environment considered in this study, tasks arrive randomly, tasks are independent (i.e., no inter-task communication), and tasks have priorities and multiple soft deadlines. The value of a task is calculated based on the priority of the task and the completion time of the task with respect to its deadlines. The goal of a dynamic mapping heuristic in this research is to maximize the value accrued of completed tasks in a given interval of time. This research proposes, evaluates, and compares eight dynamic mapping heuristics. Two static mapping schemes (all arrival information of tasks are known) are designed also for comparison. The performance of the best heuristics is 84% of a calculated upper bound for the scenarios considered.
Original language | English |
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Pages (from-to) | 154-169 |
Number of pages | 16 |
Journal | Journal of Parallel and Distributed Computing |
Volume | 67 |
Issue number | 2 |
DOIs | |
Publication status | Published - 2007 Feb |
Externally published | Yes |
Keywords
- Deadlines
- Distributed computing
- Dynamic mapping
- Heterogeneous computing
- Priority
- Resource allocation
- Resource management
- Scheduling
- Static mapping
ASJC Scopus subject areas
- Software
- Theoretical Computer Science
- Hardware and Architecture
- Computer Networks and Communications
- Artificial Intelligence