Learning Driven Computation Offloading for Asymmetrically Informed Edge Computing
Learning Driven Computation Offloading for Asymmetrically Informed Edge Computing
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DOI：
10.1109/tpds.2019.2893925
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发表时间：
2019-08
影响因子：
5.3
通讯作者：
Miao Hu;Zhuang Lei;Di Wu;Yipeng Zhou;Xu Chen;Liang Xiao
中科院分区：
文献类型：
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作者：
Miao Hu;Zhuang Lei;Di Wu;Yipeng Zhou;Xu Chen;Liang Xiao
边缘计算作为一种有前途的范例出现，将计算能力分散到网络边缘，从而通过任务卸载来改善用户体验。如果所有任务的执行时间可以事先知道，用户可以完美地调度他的任务在边缘服务器上执行。然而，在执行实际卸载之前很难知道任务执行时间（泰特），其通常在具有不同软件和硬件配置的边缘服务器上变化。此外，由于安全考虑，这样的配置信息并不总是对终端用户可用。在本文中，我们首先提出了一种学习驱动的算法，以准确预测在这种不对称信息的边缘计算环境中所有任务的Tcycle。其基本思想是通过利用Tcnt和边缘服务器配置之间的潜在相关性，仅使用Tcnt的一小部分样本集来预测未知的Tcnt。接下来，我们将任务卸载问题转化为一个约束优化问题，不幸的是，该问题被证明是NP难的。为了解决上述挑战，我们设计了一个任务卸载算法，称为最大效率优先（MEFO），以达到接近最优的效率。现场测量和实验表明，只要Tbits的采样率大于一个小的预定义阈值，我们提出的学习驱动算法就可以比其他算法更准确地预测Tbits，并且我们提出的MEFO算法在边缘服务器信息非常有限的情况下实现了更高的任务卸载成功率和更短的处理延迟。
Edge computing emerges as a promising paradigm to decentralize computation power to the edge of the network and thus improve user experience by task offloading. A user can perfectly schedule his tasks to be executed on edge servers if the execution time of all tasks can be known beforehand. However, it is difficult to know the task execution time (TET) before performing actual offloading, which normally varies on edge servers with different software and hardware configurations. Moreover, such configuration information is not always available to end users due to security concerns. In this paper, we first propose a learning-driven algorithm to accurately predict TETs of all tasks in such an asymmetrically informed edge computing environment. The basic idea is to predict unknown TETs using only a small sampled set of TETs by exploiting the underlying correlation between TETs and edge server configurations. Next, we formulate the problem of task offloading into a constrained optimization problem, which is unfortunately proved to be NP-hard. To address the above challenge, we design a task offloading algorithm, called Maximum Efficiency First Ordered (MEFO), to achieve near-optimal efficiency. Field measurements and experiments have been conducted to demonstrate that our proposed learning-driven algorithm can predict TETs more accurately than other algorithms as long as the fraction of sampled TETs is larger than a small predefined threshold, and our proposed MEFO algorithm achieves a much higher success rate of task offloading and a shorter processing delay with very limited information of edge servers.