A Mechanism Design and Learning Approach for Revenue Maximization on Cloud Dynamic Spot Markets

Abstract

Modern large-scale computing deployments consist of complex elastic applications running over machine clusters. A current trend adopted by providers is to set unused virtual machines, or else spot instances, in low prices to take advantage of spare capacity. In this paper we present a group of efficient allocation and pricing policies that can be used by vendors for their spot price mechanisms. We model the procedure of acquiring virtual machines as a truthful knapsack auction and we deploy dynamic allocation and pricing rules that achieve near-optimal revenue and social welfare. As the problem is NP-hard our solutions are based on approximate algorithms. First, we propose two solutions that do not use prior knowledge. Then, we enhance them with three learning algorithms. We evaluate them with simulations on the Google Cluster dataset and we benchmark them against the Uniform Price, the Optimal Single Price and the Ex-CORE mechanisms. Our proposed dynamic mechanism is robust, achieves revenue up to 89% of the Optimal Single Price auction, and computes the allocation in polynomial time making our contribution computationally tractable in realtime scenarios.