Volume 18, No. 6, 2021
An Analysis Of Edge-Cloud Computing Networks For Computation Offloading
Naga Lakshmi Somu , Dr Prasadu Peddi
Abstract
The proliferation of large amounts of data brought about by the Internet of Things in the commercial and academic spheres has resulted in a significant rise in the number of cloud data centers that provide data analytics services. As a result of the persistent and pervasive requirement to analyze data in close proximity to its original source, edge computing has become more common in recent years. This is one of the reasons why edge computing has become so popular. As a result of edge computing, the processing load on the network's periphery as well as the data centre may be reduced. In addition to this, it is more private and may better accommodate the requirements of the service providers. Edge computing offloads processing by dynamically distributing workloads between a cloud data centre, edge servers, and an edge device. This helps edge computing improve the transmission of network traffic and increases the responsiveness of the system. In this study, we undertake a thorough literature analysis in order to demonstrate how far we have come in the field of computational offloading to edge computing. Specifically, we want to illustrate how far we have progressed in the area of edge computing. Analyses are conducted on a number of elements of offloading computation, including its influence on energy usage, service quality, and customer happiness. Methods for allocating resources are covered, such as gaming, along with methods for balancing system performance and overheads while offloading computations.
Pages: 7983-7994
Keywords: Task partitioning, Offloading, Edge computing, computation offloading, Optimization, edge-cloud collaboration.