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Conference Papers Year : 2019

Monte-Carlo Tree Search and Reinforcement Learning for Reconfiguring Data Stream Processing on Edge Computing

Abstract

Distributed Stream Processing (DSP) applications are increasingly used in new pervasive services that process enormous amounts of data in a seamless and near real-time fashion. Edge computing has emerged as a means to minimise the time to handle events by enabling processing (i.e., operators) to be offloaded from the Cloud to the edges of the Internet, where the data is often generated. Deciding where to execute such operations (i.e., edge or cloud) during application deployment or at runtime is not a trivial problem. In this work, we employ Reinforcement Learning (RL) and Monte-Carlo Tree Search (MCTS) to reassign operators during application runtime. Experimental results show that RL and MCTS algorithms perform better than traditional placement techniques. We also introduce an optimisation to a MCTS algorithm, called MCTS-Best-UCT, that achieves similar latency with fewer operator migrations and faster execution time. In certain scenarios, the time needed by MCTS-Best-UCT to find the best end-to-end latency is at least 33% smaller than the time required by the other algorithms.
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Dates and versions

hal-02305472 , version 1 (04-10-2019)

Identifiers

  • HAL Id : hal-02305472 , version 1

Cite

Alexandre da Silva Veith, Marcos Dias de Assuncao, Laurent Lefevre. Monte-Carlo Tree Search and Reinforcement Learning for Reconfiguring Data Stream Processing on Edge Computing. SBAC-PAD 2019 - International Symposium on Computer Architecture and High Performance Computing, Oct 2019, Campo Grande, Brazil. pp.1-8. ⟨hal-02305472⟩
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