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 Plus annual

Presents the access of premium content as premium feature

Premium Content

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 Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

We consider the optimization of distributed resource scheduling to minimizethe sum of task latency and energy consumption for all the Internet of thingsdevices (IoTDs) in a large-scale mobile edge computing (MEC) system. To addressthis problem, we propose a distributed intelligent resource scheduling (DIRS)framework, which includes centralized training relying on the globalinformation and distributed decision making by each agent deployed in each MECserver. More specifically, we first introduce a novel multi-agentensemble-assisted distributed deep reinforcement learning (DRL) architecture,which can simplify the overall neural network structure of each agent bypartitioning the state space and also improve the performance of a single agentby combining decisions of all the agents. Secondly, we apply action refinementto enhance the exploration ability of the proposed DIRS framework, where thenear-optimal state-action pairs are obtained by a novel L\'evy flight search.Finally, an imitation acceleration scheme is presented to pre-train all theagents, which can significantly accelerate the learning process of the proposedframework through learning the professional experience from a small amount ofdemonstration data. Extensive simulations are conducted to demonstrate that theproposed DIRS framework is efficient and outperforms the existing benchmarkschemes.

Distributed Resource Scheduling for Large-Scale MEC Systems: A Multi-Agent Ensemble Deep Reinforcement Learning with Imitation Acceleration