An Extendable Layered Architecture for Collective Computing to Support Concurrent Multi-sourced Heterogeneous Tasks

Yang Li, Yunlong Zhao, Bin Guo, Zhenhua Zhang, Qian Geng, Ran Wang

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

Gregory D. Abowd shows a new vision of computer framework - collective computing. In this framework, kinds of remote computing devices, including even people who are regarded as a kind of computing device, are connected with each other into a group to complete a complex work. Therefore, the various computing devices with the different computing capacities can be fully used in different tasks. However, most of the relevant researches focus on improving infrastructure to address specific functions, the heterogeneous tasks performed in a common architecture and the large-scale integration are not paid enough attention. This paper presents a collective computing architecture for supporting concurrent multi-sourced heterogeneous tasks. The whole architecture is layered to provide different functions and obtain extensibility, loads balance, centralized dispatch and low delay communication. The extendible collective computing engine is used for analysing and allocating heterogeneous tasks, and the distributed device management controls heterogeneous computing devices. This architecture provides a common infrastructure for processing heterogeneous tasks by heterogeneous devices which dose not only design for some specialized systems or functions. At last, we implement a prototype system by this architecture for proving that the architecture can perform multi-sourced heterogeneous tasks well.

Original languageEnglish
Pages (from-to)884-898
Number of pages15
JournalMobile Networks and Applications
Volume26
Issue number2
DOIs
StatePublished - Apr 2021

Keywords

  • Architecture
  • Collective computing
  • Heterogeneous devices
  • Heterogeneous tasks
  • Integration

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