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Mobile vehicles have been considered as potential edge servers to provide computation resources for the emerging Intelligent Transportation System (ITS) applications. However, how to fully utilize the mobile computation resources to satisfy the real-time arrived computation requests has not been explored yet. This work will address the critical challenges of limited computation resources, stringent computation delay and unknown requirement statistics of real-time tasks in realistic vehicular edge computing scenarios. Specifically, we design a distributed clustering strategy to classify vehicles into multiple cooperative edge servers according to the available computation resources, effective connection time and the distribution of tasks’ expected deadlines. Then, a ‘Less than or Equal to’ Generalized Assignment Problem (i.e., LEGAP) is formulated to maximize the system service revenue, and on this basis, we propose an offline Bound-and-Bound based Optimal (BBO) algorithm to make periodical scheduling with a global view of tasks’ requirement statistics. The quick branching is conducted by following a greedy solution and the upper bound at each branch is derived by solving a multiple-choice knapsack problem. In addition, we present an online heuristic algorithm which makes real-time offloading decision with the guarantees that the resource capacities of all the computing servers are never exceeded with new tasks arriving. Through comparing with the other four online algorithms, the BBO algorithm achieves the highest service revenues by offloading tasks with shorter delays, and the online heuristic algorithm has the best performance in improving the service ratios.
Wang et al. (Tue,) studied this question.