Joint Data compression and Computation offloading in Hierarchical Fog-Cloud Systems

Data compression has the potential to significantly improve the computation offloading performance in hierarchical fog-cloud systems. However, it remains unknown how to optimally determine the compression ratio jointly with the computation offloading decisions and the resource allocation. This joint optimization problem is studied in the current paper where we aim to minimize the maximum weighted energy and service delay cost (WEDC) of all users. First, we consider a scenario where data compression is performed only at the mobile users. We prove that the optimal offloading decisions have a threshold structure. Moreover, a novel three-step approach employing convexification techniques is developed to optimize the compression ratios and the resource allocation. Then, we address the more general design where data compression is performed at both the mobile users and the fog server. We propose three efficient algorithms to overcome the strong coupling between the offloading decisions and resource allocation. We show that the proposed optimal algorithm for data compression at only the mobile users can reduce the WEDC by a few hundred percent compared to computation offloading strategies that do not leverage data compression or use sub-optimal optimization approaches. Besides, the proposed algorithms for additional data compression at the fog server can further reduce the WEDC

Computation Offloading and Resource Allocation for Backhaul Limited Cooperative MEC Systems

In this paper, we jointly optimize computation offloading and resource allocation to minimize the weighted sum of energy consumption of all mobile users in a backhaul limited cooperative MEC system with multiple fog servers. Considering the partial offloading strategy and TDMA transmission at each base station, the underlying optimization problem with constraints on maximum task latency and limited computation resource at mobile users and fog servers is non-convex. We propose to convexify the problem exploiting the relationship among some optimization variables from which an optimal algorithm is proposed to solve the resulting problem. We then present numerical results to demonstrate the significant gains of our proposed design compared to conventional designs without exploiting cooperation among fog servers and a greedy algorithm

Comparing the effectiveness of online and onsite learning in English proficiency classes: Learners’ perspectives

Online education has significantly gained popularity due to new technology and more importantly, the growing impact of the digitalization of the economy. Despite its prominent advantages such as accessibility, affordability and flexibility, the effectiveness of online education is still a constant debate and needs extensive investigations in different research contexts. This study aimed to evaluate the effectiveness of online learning in comparison to traditional learning in the context of English language teaching. This descriptive study was undertaken with learners of English as a foreign language (EFL) in English proficiency preparation classes, employing an online questionnaire together with final scores of proficiency tests. The results revealed that the participants had relatively positive perceptions towards online learning in all four aspects: course content, teachers, learning environment and course supports. The significant finding was that when comparing the final results of the VSTEP exams, the online learners generally were able to perform better than the learners in traditional classrooms, though the difference was not largely remarkable. Online education in the new normal will continue to excel and the effectiveness of this learning mode certainly needs further investigation from different perspectives