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The Group-Based Linear Time Causally Ordering Protocol in a Scalable P2PPS System

  • Takumi SaitoEmail author
  • Shigenari Nakamura
  • Tomoya Enokido
  • Makoto Takizawa
Conference paper
Part of the Lecture Notes in Networks and Systems book series (LNNS, volume 97)

Abstract

The P2PPS (peer-to-peer type of a topic-based PS (publish/ subscribe)) model is a distributed model where each peer subscribes interesting topics and publishes messages with topics. Messages which have some common publication topic are related with one another. In the P2PPS model, only messages related with respect to topics are required to be causally delivered to peers. In our previous studies, each message is broadcast to every peer and each peer only receives messages whose publication topics include some subscription topic. In scalable systems, it is not easy, maybe difficult to broadcast messages. The two-layered P2PPS (2P2PPS) model is proposed in our previous papers in order to efficiently publish and receive messages and causally deliver related messages in a scalable system. The 2P2PPS model is a group of peers which is composed of subgroups. Peers of each subgroup are interconnected in a local area network (LAN) and the subgroups are interconnected in a wide area network (WAN). Here, messages are broadcast to every member peer in each subgroup and messages are unicast among subgroups. We propose a TLCO (Two-Layered Causally Ordering) protocol which uses the linear time vector to causally deliver related messages in a 2P2PPS model in this paper. In the evaluation, we show the number of pairs of unnecessarily ordered messages can be reduced in the TLCO protocol than the linear time (LT) protocol.

Keywords

Topic-based publish/subscribe system P2P model 2P2PPS (two-layered P2PPS) model Unnecessarily ordered messages TLCO (Two-Layered Causally Ordering) protocol Linear time vector (LV) 

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Takumi Saito
    • 1
    Email author
  • Shigenari Nakamura
    • 1
  • Tomoya Enokido
    • 2
  • Makoto Takizawa
    • 1
  1. 1.Hosei UniversityTokyoJapan
  2. 2.Rissho UniversityTokyoJapan

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