移动边缘计算环境下面向安全和能耗感知的服务工作流调度方法  

Security and energy aware scheduling for service workflow in mobile edge computing

作　　者：李万清[1] 刘辉 李忠金[1] 袁友伟[1] LI Wanqing;LIU Hui;LI Zhongjin;YUAN Youwei(College of Computer and Technology,Hangzhou Dianzi University,Hangzhou 310018,China)

机构地区：[1]杭州电子科技大学计算机学院,浙江杭州310018

出　　处：《计算机集成制造系统》2020年第7期1831-1842,共12页Computer Integrated Manufacturing Systems

基　　金：浙江省公益性资助项目(2016C33170);国家自然科学基金资助项目(61802095)。

摘　　要：在移动边缘计算(MEC)环境中,用户将应用任务迁移至MEC端执行可以有效降低延时并减少能耗。然而,MEC环境面临潜在的恶意攻击,这些攻击可能导致隐私数据的丢失或泄露。基于此,提出了面向安全和能耗感知的服务工作流调度方法(SEA),该算法能在满足移动应用的风险率和截止时间限制条件下,最小化移动设备的能耗。SEA是基于粒子群优化算法,在编码中考虑了任务的调度位置、机密性服务和完整性服务。此外,还构建了新的安全模型,分别包括数据量、多核CPU、计算频率与安全开销之间的关系。最后,通过仿真实验证明了所提算法的可行性与有效性。In the Mobile Edge Computing(MEC) environment, users migrate application tasks to MEC for execution to effectively reduce delay and reduce energy consumption. However, the MEC still faces data security problems, and the potential malicious attacks can result in the loss or disclosure of private data. On this basis, a Security and Energy Aware(SEA) scheduling for service workflow was proposed in MEC environment, which could minimize the energy consumption of mobile devices under the risk rate and deadline constraints of mobile applications. Based on Particle Swarm Optimization(PSO) algorithm, SEA algorithm considered the scheduling position, confidentiality service and integrity service of the tasks. In addition, a new security model was constructed, which included the relationship among data volume, multi-core CPU, computing frequency and security overhead. Simulation experiments demonstrated the feasibility and effectiveness of the proposed algorithm.

关 键 词：移动边缘计算 工作流调度 安全模型 粒子群优化算法 

分 类 号：TP311[自动化与计算机技术—计算机软件与理论]