Resource Trading in Edge Computing-enabled IoV: An Efficient Futures-based Approach

TL;DR

This paper proposes a futures-based resource trading approach for edge computing-enabled Internet of Vehicles, using forward contracts and optimization to improve trading efficiency, fairness, and reliability amid dynamic conditions.

Contribution

It introduces a novel futures-based trading model with an efficient negotiation method, addressing latency, failure, and fairness issues in vehicular edge computing resource trading.

Findings

Reduces trading failures and improves fairness.

Decreases negotiation latency and cost.

Enhances utility for both buyers and sellers.

Abstract

Mobile edge computing (MEC) has become a promising solution to utilize distributed computing resources for supporting computation-intensive vehicular applications in dynamic driving environments. To facilitate this paradigm, the onsite resource trading serves as a critical enabler. However, dynamic communications and resource conditions could lead unpredictable trading latency, trading failure, and unfair pricing to the conventional resource trading process. To overcome these challenges, we introduce a novel futures-based resource trading approach in edge computing-enabled internet of vehicles (IoV), where a forward contract is used to facilitate resource trading related negotiations between an MEC server (seller) and a vehicle (buyer) in a given future term. Through estimating the historical statistics of future resource supply and network condition, we formulate the futures-based resource trading as the optimization problem aiming to maximize the seller's and the buyer's expected utility, while applying risk evaluations to relieve possible losses incurred by the uncertainties in the system. To tackle this problem, we propose an efficient bilateral negotiation approach which facilitates the participants reaching a consensus. Extensive simulations demonstrate that the proposed futures-based resource trading brings considerable utilities to both participants, while significantly outperforming the baseline methods on critical factors, e.g., trading failures and fairness, negotiation latency and cost.