Mobile-edge computing (MEC) is a new paradigm that provides cloud computing services at the edge of networks. To achieve better performance with limited computing resources, peer offloading between cooperative edge servers (e.g., MEC-enabled base stations) has been proposed as an effective technique to handle bursty and spatially imbalanced arrival of computation tasks. While various performance metrics of peer offloading policies have been considered in the literature, the worst case response time, a common quality of service (QoS) requirement in real-time applications, yet receives much less attention. To fill the gap, we formulate the peer offloading problem based on a stochastic arrival model and propose two online algorithms for cases with and without prior knowledge of task arrival rate. Our goal is to maximize the utility function of time-average throughput under constraints of energy consumption and worst case response time. Both theoretical analysis and numerical results show that our algorithms are able to produce close to optimal performance.

中文翻译：

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具有最坏情况响应时间保证的移动边缘计算中的对等卸载

移动边缘计算（MEC）是一种新的范例，可在网络边缘提供云计算服务。为了在有限的计算资源下实现更好的性能，协作边缘服务器（例如，启用MEC的基站）之间的对等卸载已被提出作为一种有效的技术来处理计算任务的突发性和空间上不平衡的到达。尽管在文献中已经考虑了对等体卸载策略的各种性能指标，但最坏情况下的响应时间是实时应用程序中常见的服务质量（QoS）要求，但很少受到关注。为了填补这一空白，我们基于随机到达模型来制定对等卸载问题，并针对有或没有事先知道任务到达率的情况，提出了两种在线算法。我们的目标是在能耗和最坏情况响应时间的约束下，最大化时间平均吞吐量的效用函数。理论分析和数值结果均表明，我们的算法能够产生接近最佳的性能。