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      Big-data streaming applications scheduling based on staged multi-armed bandits

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      Author(s)
      Kanoun, K.
      Tekin, C.
      Atienza, D.
      Van Der Schaar, M.
      Date
      2016
      Source Title
      IEEE Transactions on Computers
      Print ISSN
      0018-9340
      Publisher
      Institute of Electrical and Electronics Engineers
      Volume
      65
      Issue
      12
      Pages
      3591 - 3605
      Language
      English
      Type
      Article
      Item Usage Stats
      266
      views
      276
      downloads
      Abstract
      Several techniques have been recently proposed to adapt Big-Data streaming applications to existing many core platforms. Among these techniques, online reinforcement learning methods have been proposed that learn how to adapt at run-time the throughput and resources allocated to the various streaming tasks depending on dynamically changing data stream characteristics and the desired applications performance (e.g., accuracy). However, most of state-of-the-art techniques consider only one single stream input in its application model input and assume that the system knows the amount of resources to allocate to each task to achieve a desired performance. To address these limitations, in this paper we propose a new systematic and efficient methodology and associated algorithms for online learning and energy-efficient scheduling of Big-Data streaming applications with multiple streams on many core systems with resource constraints. We formalize the problem of multi-stream scheduling as a staged decision problem in which the performance obtained for various resource allocations is unknown. The proposed scheduling methodology uses a novel class of online adaptive learning techniques which we refer to as staged multi-armed bandits (S-MAB). Our scheduler is able to learn online which processing method to assign to each stream and how to allocate its resources over time in order to maximize the performance on the fly, at run-time, without having access to any offline information. The proposed scheduler, applied on a face detection streaming application and without using any offline information, is able to achieve similar performance compared to an optimal semi-online solution that has full knowledge of the input stream where the differences in throughput, observed quality, resource usage and energy efficiency are less than 1, 0.3, 0.2 and 4 percent respectively.
      Keywords
      data mining
      machine learning
      many-core platforms
      multiple streams processing
      Scheduling
      Artificial intelligence
      Computer architecture
      Data reduction
      E-learning
      Embedded systems
      Energy efficiency
      Face recognition
      Learning systems
      Online systems
      Processing
      Reinforcement learning
      Concept drifts
      Energy-Efficient Scheduling
      Many core
      Multiple streams
      Reinforcement learning method
      Resource Constraint
      State-of-the-art techniques
      Streaming applications
      Big data
      Permalink
      http://hdl.handle.net/11693/36508
      Published Version (Please cite this version)
      http://dx.doi.org/10.1109/TC.2016.2550454
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      • Department of Electrical and Electronics Engineering 3868
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