Collaborative Computing: Networking, Applications, and Worksharing. 11th International Conference, CollaborateCom 2015, Wuhan, November 10-11, 2015, China. Proceedings

Research Article

Multi-core Accelerated Operational Transformation for Collaborative Editing

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  • @INPROCEEDINGS{10.1007/978-3-319-28910-6_11,
        author={Weiwei Cai and Fazhi He and Xiao Lv},
        title={Multi-core Accelerated Operational Transformation for Collaborative Editing},
        proceedings={Collaborative Computing: Networking, Applications, and Worksharing. 11th International Conference, CollaborateCom 2015, Wuhan, November 10-11, 2015, China. Proceedings},
        proceedings_a={COLLABORATECOM},
        year={2016},
        month={2},
        keywords={Collaborative computing Collaborative editing Operational transformation Parallel computing},
        doi={10.1007/978-3-319-28910-6_11}
    }
    
  • Weiwei Cai
    Fazhi He
    Xiao Lv
    Year: 2016
    Multi-core Accelerated Operational Transformation for Collaborative Editing
    COLLABORATECOM
    Springer
    DOI: 10.1007/978-3-319-28910-6_11
Weiwei Cai1, Fazhi He1,*, Xiao Lv1
  • 1: Wuhan University
*Contact email: fzhe@whu.edu.cn

Abstract

This article proposes a parallel operational transformation (OT) algorithm for collaborative editing. OT maintains the eventual consistency of replicated data in optimistic way, allowing users to manipulate the shared document simultaneously. It has been the first choice for most collaborative applications. However, existing approaches must keep the number of operations generated in a session small so that it can provide a decent responsive time. The multi-core/many-core architectures are becoming pervasive in recent years. Unfortunately, there is no prior work which has explored accelerating operational transformation algorithms with available computation power. We present a lock-free operation history which are accessed by a batch of remote operations at the same time. Moreover, a data parallel computation model is constructed to accelerate the integration of local operations. To the best of our knowledge, this is the first parallel OT algorithm. Experimental results show our proposed algorithm outperforms the stat-of-art algorithms for collaborative editing.