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Distributed transactional memory for general networks

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Abstract

We consider the problem of implementing transactional memory in large-scale distributed networked systems. We present Spiral, a novel distributed directory-based protocol for transactional memory, and theoretically analyze and experimentally evaluate it for the performance boundaries of this approach from the worst-case perspective. Spiral is designed for the data-flow distributed implementation of software transactional memory which supports three basic operations: publish, allowing a shared object to be inserted in the directory so that other nodes can find it; lookup, providing a read-only copy of the object to the requesting node; move, allowing the requesting node to write the object locally after the node gets it. The protocol runs on a hierarchical directory construction based on sparse covers, where clusters at each level are ordered to avoid race conditions while serving concurrent requests. Given a shared object the protocol maintains a directory path pointing to the object. The basic idea is to use “spiral” paths that grow outward to search for the directory path of the object in a bottom-up fashion. For general networks, this protocol guarantees an \(\mathcal{O}(\log ^2 n\cdot \log D)\) approximation in sequential and one-shot concurrent executions of a finite set of move requests, where \(n\) is the number of nodes and \(D\) is the diameter of the network. It also guarantees poly-log approximation for any single lookup request. Our bounds are deterministic and hold in the worst-case. Moreover, this protocol requires only polylogarithmic bits of memory per node. Experimental evaluations in real networks also confirm our theoretical findings. To the best of our knowledge, this is the first deterministic consistency protocol for distributed transactional memory that achieves poly-log approximation in general networks.

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Notes

  1. Given a graph \(G'=(V',E')\) and a spanning tree \(T\) of the graph \(G'\), for nodes \(u,v\in V'\), the stretch of the spanning tree \(T\) is defined as \(S_{T}{:=}\max _{u,v\in V'}\frac{d_{T}(u,v)}{d_{G'}(u,v)}\), where \(d_{T}(u,v)\) denote the distance between \(u\) and \(v\) on \(T\), and \(d_{G'}(u,v)\) denote the distance between \(u\) and \(v\) on \(G'\).

  2. The doubling dimension metric is defined as follows: Let the space within radius \(\delta \) of a point be called the ball centered at that point. A point set \(\varGamma \) has doubling dimension \(\rho \) if any set of points in \(\varGamma \) that are covered by a ball of radius \(\delta \) can be covered by \(2^\rho \) balls of radius \(\frac{\delta }{2}\). We say that a metric is doubling and has a low dimension if \(\rho \) is bounded by a constant and is small.

  3. To be more precise \(\varTheta (\log \log n)\) of the highest covers are equal to \(Z_h\) but in those (except \(Z_h\)) we will not remove the replicated clusters.

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Acknowledgments

The authors would like to thank Srivathsan Srinivasagopalan, the anonymous reviewers of IPDPS 2012, and the anonymous reviewers and associate editor Yehuda Afek of this journal for their careful reading and very constructive suggestions on the earlier drafts of this work.

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  1. School of Electrical Engineering and Computer Science, Louisiana State University, Baton Rouge, LA , 70803, USA

    Gokarna Sharma & Costas Busch

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  1. Gokarna Sharma
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Correspondence to Gokarna Sharma.

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This research is supported in part by the National Science Foundation Grant No. CCF-1320835. A preliminary version of this paper appears in Proceedings of the 26th International Parallel and Distributed Processing Symposium (IPDPS), pp. 1045–1056, 2012 [46].

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Sharma, G., Busch, C. Distributed transactional memory for general networks. Distrib. Comput. 27, 329–362 (2014). https://doi.org/10.1007/s00446-014-0214-7

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