Optimal Posted Prices for Online Cloud Resource Allocation

We study online resource allocation in a cloud computing platform, through a posted pricing mechanism: The cloud provider publishes a unit price for each resource type, which may vary over time; upon arrival at the cloud system, a cloud user either takes the current prices, renting resources to execute its job, or refuses the prices without running its job there. We design pricing functions based on the current resource utilization ratios, in a wide array of demand-supply relationships and resource occupation durations, and prove worst-case competitive ratios of the pricing functions in terms of social welfare. In the basic case of a single-type, non-recycled resource (i.e., allocated resources are not later released for reuse), we prove that our pricing function design is optimal, in that any other pricing function can only lead to a worse competitive ratio. Insights obtained from the basic cases are then used to generalize the pricing functions to more realistic cloud systems with multiple types of resources, where a job occupies allocated resources for a number of time slots till completion, upon which time the resources are returned back to the cloud resource pool

POEM: Pricing Longer for Edge Computing in the Device Cloud

Multiple access mobile edge computing has been proposed as a promising technology to bring computation services close to end users, by making good use of edge cloud servers. In mobile device clouds (MDC), idle end devices may act as edge servers to offer computation services for busy end devices. Most existing auction based incentive mechanisms in MDC focus on only one round auction without considering the time correlation. Moreover, although existing single round auctions can also be used for multiple times, users should trade with higher bids to get more resources in the cascading rounds of auctions, then their budgets will run out too early to participate in the next auction, leading to auction failures and the whole benefit may suffer. In this paper, we formulate the computation offloading problem as a social welfare optimization problem with given budgets of mobile devices, and consider pricing longer of mobile devices. This problem is a multiple-choice multi-dimensional 0-1 knapsack problem, which is a NP-hard problem. We propose an auction framework named MAFL for long-term benefits that runs a single round resource auction in each round. Extensive simulation results show that the proposed auction mechanism outperforms the single round by about 55.6% on the revenue on average and MAFL outperforms existing double auction by about 68.6% in terms of the revenue.Comment: 8 pages, 1 figure, Accepted by the 18th International Conference on Algorithms and Architectures for Parallel Processing (ICA3PP

A Study of Competitive Cloud Resource Pricing under a Smart Grid Environment

In the current IaaS cloud market, to achieve profit maximization, multiple cloud providers compete non-cooperatively by offering diverse price rates. At the same time, tenant consumers judiciously adjust demands accordingly, which in turn affects cloud resource prices. In this paper, we tackle this fundamental but daunting cloud price competition problem with Bertrand game modeling, and propose a dynamic game to achieve Nash equilibrium in a distributed manner. Specifically, we realistically consider spot electricity prices under a smart grid environment, and systematically investigate the impact of different system parameters such as network delay, renewable availability, and cloud resource substitutability. We also perform stability analysis to investigate the convergence of the proposed dynamic game to Nash equilibrium. Cooperation among cloud providers can achieve aggregate cloud profit maximization, but is subject to strategic manipulations. We then propose our Striker strategy to stimulate cooperation, the efficiency of which is validated by repeated game analysis. Our evaluation is augmented with realistic electricity prices in the spot energy market, and reveals insightful observations for both theoretic analysis and practical pricing scheme design.published_or_final_versio