English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Tools annual

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

With the development of the Internet of Things (IoT), certain IoT deviceshave the capability to not only accomplish their own tasks but alsosimultaneously assist other resource-constrained devices. Therefore, this paperconsiders a device-assisted mobile edge computing system that leveragesauxiliary IoT devices to alleviate the computational burden on the edgecomputing server and enhance the overall system performance. In this study,computationally intensive tasks are decomposed into multiple partitions, andeach task partition can be processed in parallel on an IoT device or the edgeserver. The objective of this research is to develop an efficient onlinealgorithm that addresses the joint optimization of task partitioning andparallel scheduling under time-varying system states, posing challenges toconventional numerical optimization methods. To address these challenges, aframework called online task partitioning action and parallel scheduling policygeneration (OTPPS) is proposed, which is based on deep reinforcement learning(DRL). Specifically, the framework leverages a deep neural network (DNN) tolearn the optimal partitioning action for each task by mapping input states.Furthermore, it is demonstrated that the remaining parallel scheduling problemexhibits NP-hard complexity when considering a specific task partitioningaction. To address this subproblem, a fair and delay-minimized task scheduling(FDMTS) algorithm is designed. Extensive evaluation results demonstrate thatOTPPS achieves near-optimal average delay performance and consistently highfairness levels in various environmental states compared to other baselineschemes.

Joint Task Partitioning and Parallel Scheduling in Device-Assisted Mobile Edge Networks