Cooperative offloading in mobile edge computing enables resource-constrained edge clouds to help each other with computation-intensive tasks. However, the power of such offloading could not be fully unleashed, unless trust risks in collaboration are properly managed. As tasks are outsourced and processed at the network edge, completion latency usually presents high variability that can harm the offered service levels. By jointly considering these two challenges, we propose OLCD, an Online Learning-aided Cooperative offloaDing mechanism under the scenario where computation offloading is organized based on accumulated social trust. Under co-provisioning of computation, transmission, and trust services, trust propagation is performed along the multi-hop offloading path such that tasks are allowed to be fulfilled by powerful edge clouds. We harness Lyapunov optimization to exploit the spatial-temporal optimality of long-term system cost minimization problem. By gap-preserving transformation, we decouple the series of bidirectional offloading problems so that it suffices to solve a separate decision problem for each edge cloud. The optimal offloading control can not materialize without complete latency knowledge. To adapt to latency variability, we resort to the delayed online learning technique to facilitate completion latency prediction under long-duration processing, which is fed as input to queued-based offloading control policy. Such predictive control is specially designed to minimize the loss due to prediction errors over time. We theoretically prove that OLCD guarantees close-to-optimal system performance even with inaccurate prediction, but its robustness is achieved at the expense of decreased stability. Trace-driven simulations demonstrate the efficiency of OLCD as well as its superiorities over prior related work.

中文翻译：

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用于可信协作移动边缘计算的学习辅助计算卸载

移动边缘计算中的协同卸载使资源受限的边缘云能够在计算密集型任务中相互帮助。但是，除非妥善管理协作中的信任风险，否则这种卸载的力量无法完全释放。由于任务是在网络边缘外包和处理的，完成延迟通常会呈现高可变性，这可能会损害所提供的服务水平。通过联合考虑这两个挑战，我们提出了 OLCD，一种基于累积社会信任组织计算卸载的场景下的在线学习辅助协作卸载机制。在计算、传输和信任服务的共同提供下，信任传播沿着多跳卸载路径进行，从而允许强大的边缘云完成任务。我们利用 Lyapunov 优化来利用长期系统成本最小化问题的时空最优性。通过保留间隙转换，我们将一系列双向卸载问题解耦，以便为每个边缘云解决单独的决策问题就足够了。如果没有完整的延迟知识，最佳卸载控制就无法实现。为了适应延迟可变性，我们采用延迟在线学习技术来促进长时间处理下的完成延迟预测，并将其作为输入提供给基于排队的卸载控制策略。这种预测控制是专门设计的，以最大限度地减少由于预测错误随时间推移造成的损失。我们从理论上证明，即使预测不准确，OLCD 也能保证接近最佳的系统性能，但它的稳健性是以降低稳定性为代价的。跟踪驱动的模拟证明了 OLCD 的效率及其优于先前相关工作的优势。