Quality-aware dstributed data delivery for continuous query services

  • Authors:
  • Bugra Gedik;Ling Liu

  • Affiliations:
  • Georgia Institute of Technology;Georgia Institute of Technology

  • Venue:
  • Proceedings of the 2006 ACM SIGMOD international conference on Management of data
  • Year:
  • 2006

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Abstract

We consider the problem of distributed continuous data delivery services in an overlay network of heterogeneous nodes. Each node in the system can be a source for any number of data streams and at the same time be a consumer node that is receiving streams sourced at other nodes. A consumer node may define a filter on a source stream such that only the desired portion of the stream is delivered, minimizing the amount of unnecessary bandwidth consumption. By heterogeneous, we mean that nodes not only may have varying network bandwidths and computing resources but also different interests in terms of the filters and the rates of the data streams they are interested in. Our objective is to construct an efficient stream delivery network in which nodes cooperate in forwarding data streams in the presence of constrained resources. We formalize this distributed stream delivery problem as an optimization one by starting with a simple setup where the network topology is fixed and node bandwidth characteristics are known. The goal of the optimization is to find valid delivery graphs with minimum bandwidth consumption. We extend this problem formulation to QoS-aware stream delivery, in order to handle the bandwidth constrained cases in which unwanted drops and delays are inevitable. We provide a classification of delivery graph construction schemes, and in light of this classification we develop pragmatic quality-aware stream delivery (QASD) algorithms. These algorithms aim at constructing efficient stream delivery graphs in a distributed setting, where global knowledge is not available and network characteristics are not known in advance. We introduce a set of evaluation metrics and provide experimental results to illustrate the effectiveness of our proposed algorithms under these metrics.