An energy-delay tunable task allocation strategy for collaborative applications in networked embedded systems
Collaborative applications with energy and low-delay constraints are emerging in various networked embedded systems like wireless sensor networks and multimedia terminals. Conventional energy-aware task allocation schemes developed for collaborative applications only concentrated on energy savings when making allocation decisions. Consequently, the length of the schedules generated by such allocation schemes could be very long, which is unfavorable or, in some situations, even not tolerated. To remedy this problem, we developed a novel task allocation strategy called Balanced Energy-Aware Task Allocation (BEATA) for collaborative applications running on heterogeneous networked embedded systems. The BEATA algorithm aims at blending an energy-delay efficiency scheme with task allocations, thereby making the best trade-offs between energy savings and schedule lengths. Aside from that, we introduced the concept of an energy-adaptive window, which is a critical parameter in the BEATA strategy. By fine-tuning the size of the energy-adaptive window, users can readily customize BEATA to meet their specific energy-delay trade-off needs imposed by applications. Further, we built a mathematical model to approximate the energy consumption caused by both computation and communication activities. Experimental results show that BEATA significantly improves the performance of embedded systems in terms of energy savings and schedule length over existing allocation schemes.
task allocation collaborative applications energy conservation energy latency trade-off heterogeneous embedded systems