Battery aware dynamic scheduling for periodic task graphs

Battery lifetime, a primary design constraint for mobile embedded systems, has been shown to depend heavily on the load current profile. This paper explores how scheduling guidelines from battery models can help in extending battery capacity. It then presents a 'battery-aware scheduling' methodology for periodically arriving task graphs with real time deadlines and precedence constraints. Scheduling of even a single taskgraph while minimizing the weighted sum of a cost function has been shown to be NP-hard (Lawler, 1978). The presented methodology divides the problem in to two steps. First, a good DVS algorithm dynamically determines the minimum frequency of execution. Then, a greedy algorithm allows a near optimal priority function (Gruian, 2002) to choose the task which would maximize slack recovery. The methodology also ensures adherence of real time deadlines independent of the choice of the DVS algorithm and priority function used, while following battery guidelines to maximize battery lifetime. Battery simulations carried out on the profile generated by our methodology for a large set of taskgraphs show that battery life time is extended up to 23.3% as compared to existing dynamic scheduling schemes