Secure workflow scheduling algorithm utilizing hybrid optimization in mobile edge computing environments

IF 4.5 3区计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS

Computer Communications Pub Date : 2024-08-23 DOI:10.1016/j.comcom.2024.107929

Dileep Kumar Sajnani, Xiaoping Li, Abdul Rasheed Mahesar

{"title":"Secure workflow scheduling algorithm utilizing hybrid optimization in mobile edge computing environments","authors":"Dileep Kumar Sajnani, Xiaoping Li, Abdul Rasheed Mahesar","doi":"10.1016/j.comcom.2024.107929","DOIUrl":null,"url":null,"abstract":"<div><p>The rapid advancement of mobile communication technology and devices has greatly improved our way of life. It also presents a new possibility that data sources can be used to accomplish computing tasks at nearby locations. Mobile Edge Computing (MEC) is a computing model that provides computer resources specifically designed to handle mobile tasks. Nevertheless, there are certain obstacles that must be carefully tackled, specifically regarding the security and quality of services in the workflow scheduling over MEC. This research proposes a new method called Feedback Artificial Remora Optimization (FARO)-based workflow scheduling method to address the issues of scheduling processes with improved security in MEC. In this context, the fitness functions that are taken into account include multi-objective, such as CPU utilization, memory utilization, encryption cost, and execution time. These functions are used to enhance the scheduling of workflow tasks based on security considerations. The FARO algorithm is a combination of the Feedback Artificial Tree (FAT) and the Remora Optimization Algorithm (ROA). The experimental findings have demonstrated that the developed approach surpassed current methods by a large margin in terms of CPU use, memory consumption, encryption cost, and execution time, with values of 0.012, 0.010, 0.017, and 0.036, respectively.</p></div>","PeriodicalId":55224,"journal":{"name":"Computer Communications","volume":"226 ","pages":"Article 107929"},"PeriodicalIF":4.5000,"publicationDate":"2024-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Computer Communications","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0140366424002767","RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, INFORMATION SYSTEMS","Score":null,"Total":0}

引用次数: 0

Abstract

The rapid advancement of mobile communication technology and devices has greatly improved our way of life. It also presents a new possibility that data sources can be used to accomplish computing tasks at nearby locations. Mobile Edge Computing (MEC) is a computing model that provides computer resources specifically designed to handle mobile tasks. Nevertheless, there are certain obstacles that must be carefully tackled, specifically regarding the security and quality of services in the workflow scheduling over MEC. This research proposes a new method called Feedback Artificial Remora Optimization (FARO)-based workflow scheduling method to address the issues of scheduling processes with improved security in MEC. In this context, the fitness functions that are taken into account include multi-objective, such as CPU utilization, memory utilization, encryption cost, and execution time. These functions are used to enhance the scheduling of workflow tasks based on security considerations. The FARO algorithm is a combination of the Feedback Artificial Tree (FAT) and the Remora Optimization Algorithm (ROA). The experimental findings have demonstrated that the developed approach surpassed current methods by a large margin in terms of CPU use, memory consumption, encryption cost, and execution time, with values of 0.012, 0.010, 0.017, and 0.036, respectively.

查看原文本刊更多论文

移动边缘计算环境中利用混合优化的安全工作流调度算法

移动通信技术和设备的飞速发展极大地改善了我们的生活方式。这也带来了一种新的可能性，即数据源可用于完成附近地点的计算任务。移动边缘计算（MEC）是一种计算模式，它提供专门用于处理移动任务的计算机资源。然而，有一些障碍必须认真解决，特别是在 MEC 上工作流调度的安全性和服务质量方面。本研究提出了一种新方法，即基于反馈人工雷莫拉优化（FARO）的工作流调度方法，以解决在 MEC 中提高安全性的流程调度问题。在这种情况下，考虑的适应度函数包括多目标，如 CPU 利用率、内存利用率、加密成本和执行时间。这些函数用于基于安全考虑因素加强工作流任务的调度。FARO 算法是反馈人工树（FAT）和 Remora 优化算法（ROA）的结合。实验结果表明，所开发的方法在 CPU 占用、内存消耗、加密成本和执行时间方面大大超过了现有方法，其值分别为 0.012、0.010、0.017 和 0.036。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文求助全文

来源期刊

Computer Communications 工程技术-电信学

CiteScore

14.10

自引率

5.00%

发文量

397

审稿时长

66 days

期刊介绍： Computer and Communications networks are key infrastructures of the information society with high socio-economic value as they contribute to the correct operations of many critical services (from healthcare to finance and transportation). Internet is the core of today''s computer-communication infrastructures. This has transformed the Internet, from a robust network for data transfer between computers, to a global, content-rich, communication and information system where contents are increasingly generated by the users, and distributed according to human social relations. Next-generation network technologies, architectures and protocols are therefore required to overcome the limitations of the legacy Internet and add new capabilities and services. The future Internet should be ubiquitous, secure, resilient, and closer to human communication paradigms. Computer Communications is a peer-reviewed international journal that publishes high-quality scientific articles (both theory and practice) and survey papers covering all aspects of future computer communication networks (on all layers, except the physical layer), with a special attention to the evolution of the Internet architecture, protocols, services, and applications.