Reinforcement Learning for Security-Aware Workflow Application Scheduling in Mobile Edge Computing
Reinforcement Learning for Security-Aware Workflow Application Scheduling in Mobile Edge Computing
复制标题
移动边缘计算中安全感知工作流应用程序调度的强化学习
DOI：
10.1155/2021/5532410
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发表时间：
2021-05
影响因子：
--
通讯作者：
Wang Shangguang
中科院分区：
文献类型：
--
作者：
Huang Binbin;Xiang Yuanyuann;Yu Dongjin;Wang Jiaojiao;Li Zhongjin;Wang Shangguang
移动边缘计算作为一种新的计算范式，将远程云资源提供给移动用户附近的边缘服务器。在移动用户的单跳通信范围内，部署了大量的边缘服务器，这些服务器配备了巨大的计算和存储资源。移动用户可以将工作流应用程序的部分或全部计算任务卸载到边缘服务器，从而大大缩短工作流应用程序的完成时间。然而，由于移动边缘计算环境的开放性，这些任务被卸载到边缘服务器上，很容易被恶意攻击者故意窃听或篡改。此外，边缘计算环境具有动态性和时变性，导致现有的准静态工作流应用调度方案无法应用于动态移动边缘计算中存在恶意攻击的工作流调度问题。针对这两个问题，本文将存在恶意攻击的动态边缘计算环境下具有风险概率约束的工作流调度问题表述为马尔可夫决策过程。为了解决这一问题，本文设计了一种基于强化学习的安全感知工作流调度方案。为了证明我们提出的SAWS方案的有效性，本文将SAWS与MSAWS、AWM、Greedy和HEFT基线算法在不同的性能参数（包括风险概率、安全服务和风险系数）方面进行了比较。大量的实验结果表明，在不同规模的工作流中，与四种基线算法相比，SAWS策略在满足风险概率约束的情况下可以获得更好的执行效率。
Mobile edge computing as a novel computing paradigm brings remote cloud resource to the edge servers nearby mobile users. Within one-hop communication range of mobile users, a number of edge servers equipped with enormous computation and storage resources are deployed. Mobile users can offload their partial or all computation tasks of a workflow application to the edge servers, thereby significantly reducing the completion time of the workflow application. However, due to the open nature of mobile edge computing environment, these tasks, offloaded to the edge servers, are susceptible to be intentionally overheard or tampered by malicious attackers. In addition, the edge computing environment is dynamical and time-variant, which results in the fact that the existing quasistatic workflow application scheduling scheme cannot be applied to the workflow scheduling problem in dynamical mobile edge computing with malicious attacks. To address these two problems, this paper formulates the workflow scheduling problem with risk probability constraint in the dynamic edge computing environment with malicious attacks to be a Markov Decision Process (MDP). To solve this problem, this paper designs a reinforcement learning-based security-aware workflow scheduling (SAWS) scheme. To demonstrate the effectiveness of our proposed SAWS scheme, this paper compares SAWS with MSAWS, AWM, Greedy, and HEFT baseline algorithms in terms of different performance parameters including risk probability, security service, and risk coefficient. The extensive experiments results show that, compared with the four baseline algorithms in workflows of different scales, the SAWS strategy can achieve better execution efficiency while satisfying the risk probability constraints.
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