A novel data size‐aware offloading technique for resource provisioning in mobile cloud computing

Abstract

Mobile device users are involved in social networking, gaming, learning, and even some office work, so the end users expect mobile devices with highresponse computing capacities, storage, and high battery power consumption. The data-intensive applications, such as text search, online gaming, and face recognition usage, have tremendously increased. With such high complex applications, there are many issues in mobile devices, namely, fast battery draining, limited power, low storage capacity, and increased energy consumption. The novelty of this work is to strike a balance between time and energy consumption of mobile devices while using data-intensive applications by finding the optimal offloading decisions. This paper proposes a novel efficient Data Size-Aware Offloading Model (DSAOM) for data-intensive applications and to predict the appropriate resource provider for dynamic resource allocation in mobile cloud computing. Based on the data size, the tasks are separated and gradually allocated to the appropriate resource providers for execution. The task is placed into the appropriate resource provider by considering the availability services in the fog nodes or the cloud. The tasks are split into smaller portions for execution in the neighbor fog nodes. To execute the task in the remote side, the offloading decision is made by using the min-cut algorithm by considering the monetary cost of the mobile device. This proposed system achieves low-latency time 13.2% and low response time 14.1% and minimizes 24% of the energy consumption over the existing model. Finally, according to experimental findings, this framework efficiently lowers energy use and improves performance for data-intensive demanding application activities, and the task offloading strategy is effective for intensive offloading requests.

Smart Resource Allocation in Mobile Cloud Next-Generation Network (NGN) Orchestration with Context-Aware Data and Machine Learning for the Cost Optimization of Microservice Applications

“…In this section, the authors work on three main components for implementation, i.e., the Mobile Application User Layer, the Mobile Cloud Layer to manage resources, and the Mobile Agent Layer to offload the selected tasks through the machine learning engine [38]. The authors design all these through the Agent Console REST Application Programming Interface (API) [39].…”

Privacy-Friendly Task Offloading for Smart Grid in 6G Satellite–Terrestrial Edge Computing Networks

“…Constrained by the size and battery capacity, the IoT terminal devices' computing resources and storage capabilities are limited. To this end, some solutions [24][25][26] that offload computational tasks to edge or clouds for execution have been well investigated. Although promising, it will fail in remote regions due to the lack of edge or cloud nodes.…”