Paper Info
Title
Solving Energy-Latency Dilemma: Task Allocation for Parallel Applications in Heterogeneous Embedded Systems
Abstract
Parallel applications with energy and low-latency constraints are emerging in various networked embedded systems like digital signal processing, vehicle tracking, and infrastructure monitoring. However, conventional energy-driven task allocation schemes for a cluster of embedded nodes only concentrate on energy-saving when making allocation decisions. Consequently, the length of the schedules could be very long, which is unfavorable or in some situations even not tolerated. In this paper, we address the issue of allocating a group of parallel tasks on a heterogeneous embedded system with an objective of energy-saving and short-latency. A novel task allocation strategy, or BEATA (Balanced Energy- Aware Task Allocation), is developed to find an optimal allocation that minimizes overall energy consumption while confining the length of schedule to an ideal range. Experimental results show that BEATA significantly improves the performance of embedded systems in terms of energy-saving and schedule length over an existing allocation scheme.
Year
DOI
Venue
2006
10.1109/ICPP.2006.66
ICPP
Keywords
Field
DocType
embedded node,schedule length,novel task allocation strategy,allocation decision,parallel applications,task allocation,various networked embedded system,heterogeneous embedded system,energy-latency dilemma,conventional energy-driven task allocation,optimal allocation,existing allocation scheme,embedded system,low latency,digital signal processing,vehicle tracking,embedded systems,parallel processing,scheduling,energy conservation,resource allocation
Energy conservation,Digital signal processing,Scheduling (computing),Computer science,Latency (engineering),Parallel computing,Schedule,Resource allocation,Vehicle tracking system,Energy consumption,Distributed computing,Embedded system
Conference
ISSN
ISBN
Citations 
0190-3918
0-7695-2636-5
16
PageRank 
References 
Authors
0.78
14
3
Name
Order
Citations
PageRank
Tao Xie125914.19
Xiao Qin21836125.69
Mais Nijim314414.08