RALaaS: Resource-Aware Learning-as-a-Service in Edge-Cloud Collaborative Smart Connected Communities

Chao Sang, Jun Wu, Jianhua Li, Ali Kashif Bashir, Fucai Luo, Rupak Kharel

研究成果

1 被引用数 (Scopus)

抄録

As increasingly advanced data collection and computing abilities are equipped by devices at the network edge, accompanying the vigorous development of machine learning, edge devices become both the consumer and provider of data. Due to the timeliness of some learning demands and the necessity of learning results, learning resources such as data collection, transmission, and learning should be unified and converged to meet timely learning needs. In this paper, we propose a framework to implement a distributed Learning-as-a-Service function by edge-cloud collaboratively integrating resources required by a learning task. First, the architecture of RALaaS and underlying information interaction are proposed. We then formulate the learning-resource allocation problem and propose a deep reinforcement learning based solution to minimize the required learning resource and achieve better accuracy. More precisely, an A3C algorithm is presented to schedule tasks among smart connected communities and aggregate models. Finally, evaluation results show that our proposed framework can improve the accuracy by 10% compared with conventional algorithms and save about 50% edge resources when 30 nodes participate in the learning task.

本文言語English
ホスト出版物のタイトル2020 IEEE Global Communications Conference, GLOBECOM 2020 - Proceedings
出版社Institute of Electrical and Electronics Engineers Inc.
ISBN(電子版)9781728182988
DOI
出版ステータスPublished - 2020 12
外部発表はい
イベント2020 IEEE Global Communications Conference, GLOBECOM 2020 - Virtual, Taipei, Taiwan, Province of China
継続期間: 2020 12 72020 12 11

出版物シリーズ

名前2020 IEEE Global Communications Conference, GLOBECOM 2020 - Proceedings

Conference

Conference2020 IEEE Global Communications Conference, GLOBECOM 2020
国/地域Taiwan, Province of China
CityVirtual, Taipei
Period20/12/720/12/11

ASJC Scopus subject areas

  • メディア記述
  • モデリングとシミュレーション
  • 器械工学
  • 人工知能
  • コンピュータ ネットワークおよび通信
  • ハードウェアとアーキテクチャ
  • ソフトウェア
  • 安全性、リスク、信頼性、品質管理

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