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SPARCLE: Stream Processing Applications over Dispersed Computing Networks

EasyChair Preprint no. 3907

11 pagesDate: July 18, 2020


In this paper, we propose SPARCLE, a novel scheduling system offering network-aware polynomial-time task assignment and resource allocation algorithms for stream processing applications in dispersed computing networks. In particular, we address two major challenges. The first one concerns the assignment of both computation and transport tasks comprising a stream processing application to computing nodes and communication links of the network, respectively, in order to maximize the application’s processing rate. The second one concerns the resource allocation of multiple stream processing applications to satisfy their requested QoE. Our experimental results on a real image stream processing application and extensive simulations show that SPARCLE can increase the application’s processing rate by 9× and 3×, compared to the cloud computing case and state-of-the-art algorithms, respectively.

Keyphrases: dispersed computing, Edge Computing, Fog Computing, stream processing

BibTeX entry
BibTeX does not have the right entry for preprints. This is a hack for producing the correct reference:
  author = {Parisa Rahimzadeh and Jinsung Lee and Youngbin Im and Siun-Chuon Mau and Eric C. Lee and Bradford O. Smith and Fatemah Al-Duoli and Carlee Joe-Wong and Sangtae Ha},
  title = {SPARCLE: Stream Processing Applications over Dispersed Computing Networks},
  howpublished = {EasyChair Preprint no. 3907},

  year = {EasyChair, 2020}}
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