Energy-Aware Profit Maximizing Scheduling Algorithm for Heterogeneous Computing Systems

With the advent of energy-aware scheduling algorithms, it is now possible to find solutions that trade-off performance for decreased energy usage. There are now efficient algorithms to find high quality Pareto fronts that can be used to select the desired balance between make span and energy consumption. One drawback of this approach is that it still requires a system administrator to select the desired operating point. In this paper, a market-oriented technique for scheduling is presented where the high performance computing system administrator is trying to maximize the return on investment. A model is developed where the users pay a given price to have a bag-of-tasks processed. The cost to the system administrator for processing this bag-of-tasks is strongly related to the energy consumption for executing these tasks. A novel algorithm is designed that efficiently finds the maximum profit resource allocation and tightly bounds the optimal solution. In addition, this algorithm has very desirable runtime and solution quality properties as the number of tasks and machines become large.