Online Cooperative Resource Allocation at the Edge: A Privacy-Preserving Approach

Abstract

Mobile edge computing provides a platform facilitating individual servers to pool their resources locally for cooperative computation. One fundamental problem in this new paradigm is how to effectively allocate crowdsourced edge resources to users competing in a highly unpredicted environment. This, apparently, cannot be realized without a truthful open market. On the other hand, enforcing truthfulness potentially incurs privacy problems. There have been efforts in differentially private auctions, in which exponential mechanism, designed for single-sided single-item auctions, is a common solution. However, such an approach is not applicable in two-sided combinatorial edge markets, further complicated by the extra migration cost on energy-constrained users often imposed by online allocation. In this paper, we propose OPTA, an online privacy-preserving truthful double auction mechanism for dynamic resource cooperation at the edge. Given uncertainties in future market behaviors, we harness competitive analysis by decomposing the online optimization into a series of single-round auctions such that their objectives are iteratively adjusted to capture the temporally-coupled nature of the problem. In each round, by jointly considering the features of exponential mechanism and greedy heuristic, we design a near-optimal allocation policy with efficiency and privacy guarantee. We further implement a critical-value pricing scheme for winners, realizing the truthfulness in expectation. Building upon the single-round results, our overall online algorithm achieves a provable competitive ratio. We validate the desirable properties of OPTA through theoretical analysis and extensive simulations.