A Unified Comparative Study of Heuristic Algorithms for Double Combinatorial Auctions - Locality-Constrained Resource Allocation Problems.

Market-oriented resource allocation in cloud computing is driven by increasingly stringent needs for flexibility, fine-grained allocation, and more critically, revenue maximization. Double combinatorial auctions aptly address these demands, but their (mathcal {NP})-hardness has hindered them from being widely adopted. Heuristic algorithms, with their input-dependent performance and solution quality, have failed to offer a robust alternative. We posit that a unifying approach for evaluating all existing algorithms, under the umbrella of a consistent problem formulation and a variety of common test cases, can propel combinatorial auctions towards real-world usage.