Efficient heuristics for joint optimization of performance, energy, and temperature in allocating tasks to multi-core processors

Abstract

This paper presents heuristic algorithms for solving the three-way joint optimization of Performance, Energy and temperature (PET) in scheduling tasks to multi-core processors. The problem, called as PET optimized scheduling (PETOS) problem is a high-complexity problem due to conflicting objectives. While solutions to the PETOS problem can be obtained by using conventional multi-objective optimization approaches, the time taken by such solvers is generally not feasible to be used at the scheduling level. Therefore, we explore heuristic methods that can explore the decision space while maintaining low computational complexity. The design of heuristic algorithms presents non-trivial challenges of incorporating all the PET quantities into the scheduling process. We present nine heuristics, each varying in its approach for selecting a core and picking the processor frequency. Each heuristic produces a set of solutions where each solution represents a complete schedule for assigning a set of tasks on a given multi-core system, thus identifying different trade-offs that exist between performance, energy, and temperature at scheduling level. A comparative study describes their trade-offs.