Combinatorial Auction Algorithm Selection for Cloud Resource Allocation Using Machine Learning

Abstract

Demands for flexibility, efficiency and fine-grained control for the allocation of cloud resources have steered the research in this field towards market-inspired approaches. Combinatorial auctions can fulfill these demands, but their inherent NP -hardness makes them impractical if an optimal solution is desired in a reasonable time. Various heuristic algorithms that yield good allocations fast have been proposed, but their performance and solution quality are highly dependent on the input. In this paper, we investigate which features of a problem instance are predictive of algorithm performance and quality, and propose an algorithm selection method that uses machine learning to find the best heuristic for each given input. We introduce a new cost model for the trade-off between execution time and solution quality, which enables quantitative algorithm comparison. Using feature-based classification to train the algorithm selection model, we can show that our approach outperforms the single best algorithm, as well as a random algorithm selection.