Task offloading is one of key operations in edge computing, which is essential for reducing the latency of task processing and boosting the capacity of end devices. However, the heterogeneity among tasks generated by various users makes it challenging to design efficient task offloading algorithms. In addition, the assumption of complete information for offloading decision-making does not always hold in a distributed edge computing environment. In this article, we formulate the problem of heterogeneous task offloading in a distributed environment as a minority game (MG), in which each player must make decisions independently in each turn and the players who end up on the minority side win. The multi-player MG incentivizes players to cooperate with each other in the scenarios with incomplete information, where players don't have full information about other players (e.g., the number of tasks, the required resources). To address the challenges incurred by task heterogeneity and the divergence of naive MG approaches, we propose an MG based scheme, in which tasks are divided into subtasks and instructed to form into a set of groups as possible, and the left ones are scheduled to perform decision adjustment in a probabilistic manner. We prove that our proposed algorithm can converge to a near-optimal point, and also investigate its stability and price of anarchy in terms of task processing time. Finally, we conduct a series of simulations to evaluate the effectiveness of our proposed scheme and the results indicate that our scheme can achieve around 30% reduction of task processing time compared with other approaches. Moreover, our proposed scheme can converge to a near-optimal point, which cannot be guaranteed by naive MG approaches.

中文翻译：

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具有不完全信息的异构边缘卸载：一种少数博弈方法

任务卸载是边缘计算的关键操作之一，对于减少任务处理的延迟和提高终端设备的容量至关重要。然而，不同用户生成的任务之间的异构性使得设计有效的任务卸载算法具有挑战性。此外，用于卸载决策的完整信息的假设在分布式边缘计算环境中并不总是成立。在本文中，我们将分布式环境中的异构任务卸载问题表述为少数派博弈（MG），其中每个玩家必须在每一轮独立做出决策，最终处于少数派一方的玩家获胜。多人 MG 激励玩家在信息不完整的场景中相互合作，在这些场景中玩家不 t 有关于其他玩家的完整信息（例如，任务数量、所需资源）。为了解决任务异质性和朴素 MG 方法的分歧所带来的挑战，我们提出了一种基于 MG 的方案，其中任务被划分为子任务并被指示尽可能地形成一组组，而剩下的则被安排执行以概率方式进行决策调整。我们证明了我们提出的算法可以收敛到一个接近最优的点，并且还研究了它在任务处理时间方面的稳定性和无政府状态的代价。最后，我们进行了一系列模拟来评估我们提出的方案的有效性，结果表明，与其他方法相比，我们的方案可以减少约 30% 的任务处理时间。而且，