Sustainable Offloading in Mobile Cloud Computing

Abstract

Mobile Cloud Computing (MCC) has been extensively explored to be applied as a vital tool to enhance the capabilities of mobile devices, increasing computing power, expanding storage capacity, and prolonging battery life. Offloading works as the fundamental feature that enables MCC to relieve task load and extend data storage through an accessible cloud resource pool. Several initiatives have drawn attention to delivering MCC-supported energy-oriented offloading as a method to cope with a lately steep increase in the number of rich mobile applications and the enduring limitations of battery technologies. However, MCC offloading relieves only the burden of energy consumption of mobile devices; performance concerns about Cloud resources, in most cases, are not considered when dynamically allocating them for dealing with mobile tasks. The application context of MCC, encompassing urban computing, aggravates the situation with very large-scale scenarios, posing as a challenge for achieving greener solutions in the scope of Cloud resources. Thus, this article gathers and analyzes recent energy-aware offloading protocols and architectures, as well as scheduling and balancing algorithms employed toward Cloud green computing. This survey provides a comparison among system architectures by identifying their most notable advantages and disadvantages. The existing enabling frameworks are categorized and compared based on the stage of the task offloading process and resource management types, describing current open challenges and future research directions.

A review on the computation offloading approaches in mobile edge computing: A game‐theoreticperspective

“…As an advantage, the survey is constructed by reviewing plenty of MEC related researches and articles of which 23% are newly published at the time of acceptance. However, the reviewed article has the following shortages: In another survey, energy-awareness in MCC and review protocols, architecture, scheduling, and balancing algorithms in the realm of MCC and green cloud computing strategies was the consideration of Boukerche et al 18 Next, the advantages and disadvantages of those researches in terms of offloading process and resource management types are compared and categorized. The strength point of this article is its professional review on the subject that has been well organized according to the energy-aware issues.…”

“…15 In the MEC environments, all the studied surveys applied a way to compare related published articles in which optimization of service utilization, recourse utilization, and time utilization in offloading computations were the main concerns. 8,[16][17][18][19][20][21][22][23][24] However, to the best of our knowledge, despite its importance, there is not any systematic survey and review in terms of offloading covering the GT-based mechanisms, recognizing the need of researchers to do their work in mentioned criterion. Therefore, this survey is to review the existing works and explore different techniques in MEC environment, covering GT-based mechanisms comprehensively and systematically.…”

What the Fog? Edge Computing Revisited: Promises, Applications and Future Challenges

“…They further identify processing of continuous data streams as an ideal edge application, especially when those data streams are on end user premises and have a low access rate (e.g., video surveillance). Boukerche et al [68] survey energy-efficient offloading in Mobile Cloud Computing from the perspective of both the mobile device and the cloud infrastructure. We argue that this is not only relevant in cloud computing, but also in edge environments, where surrogates might not be optimized for energy-efficient computations.…”

A Survey and Taxonomy on Task Offloading for Edge-Cloud Computing

“…Even so, many applications are hard to perform on a smart device due to its limited battery capacity, compute resources and wireless bandwidth because of its limited size [4], [5]. To address the problem, some work have proposed to employ cloud computing for extending resources of user devices [6], [7] by offloading some of users' tasks to a cloud to improve processing time and computing energy for users devices as the cloud has abundant processing resources. Task offloading increases the transmission data amount of devices, resulting in a longer transmission time and a more transmission energy due to the unstable performance of wireless networks [8], [9], which may cause a shorter device battery life and a poorer application performance.…”