Streaming graph computations with a helpful advisor

Authors:
Graham Cormode;Michael Mitzenmacher;Justin Thaler
Affiliations:
AT & T Labs-Research;Harvard University, School of Engineering and Applied Sciences;Harvard University, School of Engineering and Applied Sciences
Venue:
ESA'10 Proceedings of the 18th annual European conference on Algorithms: Part I
Year:
2010

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Abstract

Motivated by the trend to outsource work to commercial cloud computing services, we consider a variation of the streaming paradigm where a streaming algorithm can be assisted by a powerful helper that can provide annotations to the data stream. We extend previous work on such annotation models by considering a number of graph streaming problems. Without annotations, streaming algorithms for graph problems generally require significant memory; we show that for many standard problems, including all graph problems that can be expressed with totally unimodular integer programming formulations, only constant memory is needed for single-pass algorithms given linearsized annotations. We also obtain a protocol achieving optimal tradeoffs between annotation length and memory usage for matrix-vector multiplication; this result contributes to a trend of recent research on numerical linear algebra in streaming models.