Edge Computing Landscapes: A Comprehensive Taxonomy and State-of-the-Art Analysis
TL;DR
This paper outlines research directions to enable the development and deployment of cloud-to-edge and cloud-to-thing computing environments in the future and presents future research challenges such as hardware heterogeneity, network dynamics, and the development of energy-efficient edge systems.
Abstract
The needs of scenarios such as the rapidly expanding Internet of Things (IoT) ecosystem or real-time services are examples of situations where a centralized cloud model may not suffice‚ as all data resides on the cloud and traditional cloud computing practices have limitations․ Low latency‚ high bandwidth and privacy issues arise due to the ever-growing amount of data being generated by connected devices․ One way to address these issues is to move computation‚ storage and networking resources closer to the edge where data is generated․ Edge computing can minimize latency in latency-sensitive applications and can also help reduce network congestion by offloading processing from the core network․ This paper surveys the recent works in edge computing‚ classifying the existing body of work in edge computing into four categories based on four dimensions: physical architecture‚ algorithmic optimization techniques‚ performance objectives‚ and security mechanisms․ The survey provides examples of representative works for each dimension‚ analyses the corresponding performance-energy trade-offs based on processing performance‚ energy consumption‚ and security objectives‚ and presents future research challenges such as hardware heterogeneity‚ network dynamics‚ and the development of energy-efficient edge systems․ The paper also outlines research directions to enable the development and deployment of cloud-to-edge and cloud-to-thing computing environments in the future․
