Optimization in Edge Computing: A Survey

Authors

  • Raghad Jassim Mohammed Department of Computer Engineering, College of Engineering, AL-Nahrain University, Baghdad, Iraq
  • Shaymaa W. Al-Shammari Department of Computer Engineering, College of Engineering, AL-Nahrain University, Baghdad, Iraq

DOI:

https://doi.org/10.58564/IJSER.2.2.2023.72

Keywords:

Optimization, Edge Computing, IoT and Task Offloading

Abstract

Due to advancement, there are now more smart devices connected to the internet., which causes massive data traffic in the network. Resulting in many problems such as slow response time, largely consumed energy, high load in transmission channels, and bad use of the network resources in the traditional cloud. Edge computing facilities and brings the cloud's service to the network's edge. Edge computing a distributed computing near to source of data. This technology solved and helps to solve many problems in the cloud, but also has challenges and open issues, for example, the limited lifetime of the IoT devices, limited resources, and computation offloading. The performance of edge computing is affected by offloading. So, many optimization methods are used to solve this problem and improve the performance in edge computing. This paper presents a survey of the studies related to optimizing task offloading in edge computing. The difference between this work and the previous surveys is that this survey combined offloading and optimization with the types of optimization methods and takes into consideration the three layers of edge computing architecture IoT, the Edge layer, and the cloud layer. The previous surveys did not include all types of optimization or combine the offloading with optimization. The architecture of edge computing, challenges, and open issues, optimization methods are presented.

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Published

2023-06-01

How to Cite

Jassim Mohammed, R., & W. Al-Shammari, S. (2023). Optimization in Edge Computing: A Survey. Al-Iraqia Journal for Scientific Engineering Research, 2(2), 65–77. https://doi.org/10.58564/IJSER.2.2.2023.72

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