2019 Volume 27 Pages 136-148
Energy-aware task allocation of embedded systems is one of the most important issues in recent decades. A classical solution to solve the issue is Integer Linear Programming (ILP). However, given the considerable time consumption, it is effective only to the extent that the scale of the problem is small. How to use ILP to solve large allocation problems on heterogeneous multiprocessor systems to minimize energy consumption is still a challenge. This paper proposes two ILP formulations to deal with it. One complete ILP(1) is used to derive a feasible allocation, and the other simplified ILP(2) is for calculating the desired minimum energy. Then the desired minimum energy can be used as a reference to evaluate the intermediate solution of ILP(1) and decide its timeout. Besides, to find out the best-suited platform for a given workload, a flexible design which presents flexibilities and choices in core assignment, is considered for further energy saving. For example, the optimal core number design and core type design are generated as two independent ILP formulations, denoted as ILP(3) and ILP(4). The experimental results on randomly generated task sets demonstrate that, compared with the fixed platform, automatically synthesizing a flexible core assignment saves more energy.