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Collaborative Scheduling for Edge Computing

Collaborative Scheduling of Edge Mesh

Overview

Existing edge computing methods focus on task scheduling and computation offloading problems from end devices to edge devices or a centralized server, without fully exploiting communication ability and computing capabilities of distributed edge devices. To address this problem, we propose Edge Mesh as an abstraction of collaborative edge computing, which distributes the decision-making tasks among edge devices within the network instead of sending all the data to a centralized server. We identify unique characteristics of collaborative edge computing including distributed data sources and conflicting network flows while sending the data due to limited bandwidth. We have proposed solutions for task partitioning and offloading in collaborative edge computing environment for different applications models including multiple dependent tasks, multiple independent tasks, and multiple DAG tasks. We have also studied other resource management problems including resource allocation for live virtual machine migration, hybrid data dissemination for edge-assisted automated driving, etc.

Achievements

  • Sahni, Yuvraj, Jiannong Cao, Lei Yang, and Yusheng Ji. Multi-Hop Multi-Task Partial Computation Offloading in Collaborative Edge Computing. IEEE Transactions on Parallel and Distributed Systems 32, no. 5, 2021: 1133-1145.
  • Sahni, Yuvraj, Jiannong Cao, Lei Yang, and Yusheng Ji. Multi-Hop Offloading of Multiple DAG Tasks in Collaborative Edge Computing. IEEE Internet of Things Journal, 2020.
  • Lei Yang, Doudou Yang, Jiannong Cao, Yuvraj Sahni, and Xiaohua Xu. QoS guaranteed resource allocation for live virtual machine migration in edge clouds. IEEE Access 8, 2020: 78441-78451.
  • Lei Yang Lingling Zhang, Zongjian He, Jiannong Cao, and Weigang Wu. Efficient hybrid data dissemination for edge-assisted automated driving. IEEE Internet of Things Journal, no. 1, 2019: 148-159.
  • Lei Yang, Bo Liu, Jiannong Cao, Yuvraj Sahni, Zhenyu Wang. Joint Computation Partitioning and Resource Allocation for Latency Sensitive Applications in Mobile Edge Clouds. IEEE Transactions on Services Computing, 2019.
  • Jin Cao, Lei Yang, Jiannong Cao: Revisiting Computation Partitioning in Future 5G-Based Edge Computing Environments. IEEE Internet of Things Journal. 2019, 6(2): 2427-2438
  • Yuvraj Sahni, Jiannong Cao, Lei Yang: Data-Aware Task Allocation for Achieving Low Latency in Collaborative Edge Computing. IEEE Internet of Things Journal. 2019, 6(2): 3512-3524
  • Yuvraj Sahni, Jiannong Cao, Shigeng Zhang, Lei Yang. Edge Mesh: A New Paradigm to Enable Distributed Intelligence in Internet of Things. IEEE Access 5, 2017, 16441-16458
  • Lei Yang, Bo Liu, Jiannong Cao, Yuvraj Sahni, Zhenyu Wang. Joint Computation Partitioning and Resource Allocation for Latency Sensitive Applications in Mobile Edge Clouds. CLOUD 2017, 246-253
  • Lei Yang, Jiannong Cao, Zhenyu Wang, Weigang Wu. Network Aware Multi-User Computation Partitioning in Mobile Edge Clouds. ICPP 2017, 302-311

Members

Dr. Yuvraj Sahni, Dr. Lei Yang, Mingin Zhang, Qianyi Chen

Previous members include Dr. Kongyang Chen, Dan Wu. Thanks for their contributions.

Funding

HK RGC General Research Fund (GRF), HK$715,500, 01/2019-12/2021