Fair, Fast and Frugal Large-Scale Matchmaking for VM Placement

VM placement, be it in public or private clouds, has a decisive impact on the provider’s interest and the customer’s needs alike, both of which may vary over time and circumstances. However, current resource management practices are either statically bound to specific policies or unilaterally favor the needs of Cloud operators. In this paper we argue for a flexible and democratic mechanism to map virtual to physical resources, trying to balance satisfaction on both sides of the involved stakeholders. To that end, VM placement is expressed as an Equitable Stable Matching Problem (ESMP), where each party’s policy is translated to a preference list. A practical approximation for this NP-hard problem, modified accordingly to ensure efficiency and scalability, is applied to provide equitable matchings within a reasonable time frame. Our experimental evaluation shows that, requiring no more memory than what a high-end desktop PC provides and knowing no more than the top 20% of the agent’s preference lists, our solution can efficiently resolve more than 90% of large-scale ESMP instances within \( N \sqrt{N} \) rounds of matchmaking.