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Delay-Sensitive Multi-Period Computation Offloading with Reliability Guarantees in Fog Networks

机译:延迟敏感的多时期计算卸载雾网络中的可靠性保证

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Computation offloading over fog computing has the potential to improve reliability and reduce latency in future networks. This paper considers a scenario where roadside units (RSUs) are installed for offloading tasks to the computation nodes including nearby fog nodes and a cloud center. To guarantee the reliable communication, we formulate the first subproblem of power allocation, and leverage the conditional value-at-risk approach to analyze the successful transmission probability in the worse-case channel condition. To complete computation tasks with low latency, we formulate the second subproblem of task allocation into a multi-period generalized assignment problem (MPGAP), which aims at minimizing the total delay by offloading tasks to the 'right' fog nodes at 'right' period. Then, we propose a modified branch-and-bound algorithm to derive the optimal solution and a heuristic greedy algorithm to obtain approximate performance. In addition, the master problem is proposed as a non-convex optimization problem, which considers both the reliability-guaranteed and delay-sensitive requirements. We design the Lagreedy algorithm by combining the subgradient algorithm and the heuristic algorithm. Comprehensive evaluations demonstrate that the Lagreedy is able to obtain the shortest delay with a high power consumption, while the branch-and-bound algorithm can achieve both shorter delay and lower power consumption with reliability guarantees.
机译:通过雾计算卸载的计算具有可提高可靠性并降低未来网络的延迟。本文考虑了一个方案,其中安装了路边单元(RSU),用于将任务卸载到计算节点,包括附近的雾节点和云中心。为了保证可靠的通信,我们制定了电力分配的第一个子问题,并利用条件值 - at风险方法分析了更糟糕的信道条件下的成功传输概率。要完成具有低延迟的计算任务,我们将任务分配的第二个子问题分配到多个期间广义分配问题(MPGAP)中,该分配问题(MPGAP)旨在通过将任务卸载到“正确”期间的“右”雾节点来最小化总延迟。然后,我们提出了一种修改的分支和绑定算法来导出最佳解决方案和启发式贪婪算法,以获得近似性能。此外,提出了主问题作为非凸优化问题,这考虑了可靠性和延迟敏感要求。我们通过组合子校验算法和启发式算法来设计Lagreedy算法。综合评估表明,Lagreedy能够获得高功耗的最短延迟,而分支机构算法可以通过可靠性保证实现较短的延迟和较低的功耗。

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