Optimal power allocation and load balancing for non-dedicated heterogeneous distributed embedded computing systems

Abstract

This paper investigates on the optimal power allocation and load balancing problem encountered by heterogeneous and distributed embedded systems with mixed tasks. Given that each node has real and different urgent tasks in the majority of practical heterogeneous embedded systems, three priority disciplines are considered: dedicated jobs without priority, prioritized dedicated jobs without preemption, and prioritized dedicated jobs with preemption. A model is established for heterogeneous embedded processors with dedicated-task-dependent dynamic power and load balancing management; each processor is considered as an M/M/1 queueing sub-model with mixed generic and dedicated tasks. The processors have different levels of power consumption, and each one can employ any of the three disciplines. The objective of this study is to find an optimal load balancing (for generic tasks) and power allocation strategy for heterogeneous processors preloaded by different amounts of dedicated tasks such that the average response time of generic tasks is minimized. Considering that this problem is a multi-constrained, multi-variable optimization problem for which a closed-form solution is unlikely to be obtained, we propose an optimal power allocation and load balancing scheme by employing Lagrange method and binary search approach, which are completed by utilizing two new rules established by observing numerical variations of parameters. Several numerical examples are presented to demonstrate the effectiveness of our solution. To the best of our knowledge, this is the first work on analytical study that combines load balancing, energy efficiency, and priority of tasks in heterogeneous and distributed embedded systems.