A new rule-based power-aware job scheduler for supercomputers

The fast processing speeds of the current generation of supercomputers provide a great convenience to scientists dealing with extremely large data sets. The next generation of exascale supercomputers could provide accurate simulation results for the automobile industry, aerospace industry, and even nuclear fusion reactors for the very first time. However, the energy cost of super-computing is extremely high, with a total electricity bill of 9 million dollars per year. Thus, conserving energy and increasing the energy efficiency of supercomputers have become critical in recent years. Many researchers have studied this problem and are trying to conserve energy by incorporating the dynamic voltage frequency scaling technique into their methods. However, this approach is limited, especially when the workload is high. In this paper, we developed a power-aware job scheduler by applying a rule-based control method and taking into consideration real-world power and speedup profiles to improve power efficiency while adhering to predetermined power constraints. The intensive simulation results showed that our proposed method is able to achieve the maximum utilization of computing resources as compared to baseline scheduling algorithms while keeping the energy cost under the threshold. Moreover, by introducing a power performance factor based on the real-world power and speedup profiles, we are able to increase the power efficiency by up to 75%.