Optimizing Energy Efficient Cloud Architectures for Edge Computing: A Comprehensive Review

Abstract

Now-a-days, edge computing and cloud computing are considered for collaborating together to produce computing solutions that are more effective, scalable and adaptable. The proliferation of cloud infrastructures has drastically increased energy consumption leading to the need for more research in optimizing energy efficiency for sustainable and efficient systems with reduced operational costs. In addition, the edge computing paradigm has gained wide attention during the last few decades due to the rise of the Internet of Things (IoT) devices, the emergence of applications that require low latency, and the widespread demand for environmentally friendly computing. Moreover, lowering cloud-edge systems' energy footprints is essential for fostering sustainability in light of growing concerns about environmental effects. This research presents a comprehensive review of strategies aimed at optimizing energy efficiency in cloud architectures designed for edge computing environments. Various strategies, including workload optimization, resource allocation, virtualization technologies, and adaptive scaling methods, have been identified as techniques that are widely utilized by contemporary research in reducing energy consumption while maintaining high performance. Furthermore, the paper investigates how advancements in machine learning and AI can be leveraged to dynamically manage resource distribution and energy-efficient enhancements in cloud-edge systems. In addition, challenges to the approaches for energy optimization have been discussed in detail to further provide insights for future research. The conducted comprehensive review provides valuable insights for future research in the edge computing paradigm, particularly emphasizing the critical importance of enhancing energy efficiency in these systems.

Topics & Concepts