Heuristic allocation of computational resources

This study considers an actual real-world problem encountered by ARM, the world's leading semiconductor intellectual property (IP) supplier, concerning the multi-year assignment of weeks-long computationally intensive projects (executable pieces of code) across a number of capacity-limited clusters. The quality of a projects-to-cluster assignment is measured in terms of several metrics such as the even utilization of clusters, being able to realize all projects, and spreading projects of different research groups evenly across the clusters. The first (theoretical) contribution of this work is to motivate and formally define this novel application and put it in context with related literature. The second (experimental) contribution of this work is about gaining an understanding about the problem and performing an initial investigation on how different algorithm types (random search, an EMOA, and greedy search) fare on the problem. Our study revealed that the problem has many infeasible solutions and is challenging to optimize especially for long planning horizons (more than 3 years). While the EMOA is able to outperform random and greedy search (and also the current approach used at ARM) in terms of solution quality discovered, greedy search was the computationally most efficient approach and suitable for short term planning horizons (up to 1 year).