Cloud computing providers must constantly hold many idle compute instances available (e.g., for maintenance or for users with long-term contracts). A natural idea, which should intuitively increase the provider’s profit, is to sell these idle instances on a secondary market, for example, via a preemptible spot market. However, this ignores possible “market cannibalization” effects that may occur in equilibrium as well as the additional costs the provider experiences due to preemptions. To study the viability of offering a spot market, we model the provider’s profit optimization problem by combining queuing theory and game theory to analyze the equilibria of the resulting queuing system. Our main result is an easy-to-check condition under which a provider can simultaneously achieve a profit increase and create a Pareto improvement for the users by offering a spot market (using idle resources) alongside a fixed-price market. Finally, we illustrate our results numerically to demonstrate the effects that the provider’s costs and her strategy have on her profit. This paper was accepted by Gabriel Weintraub, revenue management and market analytics.
No abstract
Cloud computing providers face the problem of matching heterogeneous customer workloads to resources that will serve them. This is particularly challenging if customers, who are already running a job on a cluster, scale their resource usage up and down over time. The provider therefore has to continuously decide whether she can add additional workloads to a given cluster or if doing so would impact existing workloads’ ability to scale. Currently, this is often done using simple threshold policies to reserve large parts of each cluster, which leads to low efficiency (i.e., low average utilization of the cluster). We propose more sophisticated policies for controlling admission to a cluster and demonstrate that they significantly increase cluster utilization. We first introduce the cluster admission problem and formalize it as a constrained Partially Observable Markov Decision Process (POMDP). As it is infeasible to solve the POMDP optimally, we then systematically design admission policies that estimate moments of each workload’s distribution of future resource usage. Via extensive simulations grounded in a trace from Microsoft Azure, we show that our admission policies lead to a substantial improvement over the simple threshold policy. We then show that substantial further gains are possible if high-quality information is available about arriving workloads. Based on this, we propose an information elicitation approach to incentivize users to provide this information and simulate its effects.
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