Optimising energy and overhead for large parameter space simulations
This work addresses optimization challenges in simulation systems for researchers or practitioners dealing with energy and efficiency trade-offs, but it is incremental as it applies an existing method (Genetic Algorithm) to a specific domain.
The paper tackled the problem of optimizing multiple conflicting objectives (energy consumption and task overhead) in large parameter spaces by applying a Genetic Algorithm to identify the Pareto frontier for the HTC-Sim simulation system. The result was a reduction in energy consumption by approximately 36% compared to previous work, without significantly increasing overhead.
Many systems require optimisation over multiple objectives, where objectives are characteristics of the system such as energy consumed or increase in time to perform the work. Optimisation is performed by selecting the `best' set of input parameters to elicit the desired objectives. However, the parameter search space can often be far larger than can be searched in a reasonable time. Additionally, the objectives are often mutually exclusive -- leading to a decision being made as to which objective is more important or optimising over a combination of the objectives. This work is an application of a Genetic Algorithm to identify the Pareto frontier for finding the optimal parameter sets for all combinations of objectives. A Pareto frontier can be used to identify the sets of optimal parameters for which each is the `best' for a given combination of objectives -- thus allowing decisions to be made with full knowledge. We demonstrate this approach for the HTC-Sim simulation system in the case where a Reinforcement Learning scheduler is tuned for the two objectives of energy consumption and task overhead. Demonstrating that this approach can reduce the energy consumed by ~36% over previously published work without significantly increasing the overhead.