DistOS-2011W Distributed Data Structures: a survey

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A. M. AbdelRahman
PhD Student
Dept. of Systems and Computer Engineering
Carleton University
Ottawa, ON
Canada K1S 5B6
amoham16@connect.carleton.ca

Abstract

Access to stored data in main memory is much faster than access to local disks/hard drives. Well maintained data structures can utilize such access. In addition, the performance of the memory fetch, dispatch and store operations on Distributed Operating Systems would augment if they exploit a highly scalable distributed data structures. This paper describes the concept of Distributed Data Structures (DDS) which has been made viable by multicomputers. Scalability aspects and their implementation are reviewed for a chosen subset of classic data structures. Open research issues for SDDS are also discussed.

Introduction

Multicomputers are collections of autonomous workstations or PCs on a network (network multicomputers), or of share-nothing processors with local storage linked through a high-speed network or bus (switched multicomputer)<ref> A. S. Tanenbaum, Distributed Operating Systems. Prentice Hall, 1995.</ref>. Recent advances in multicomputers showed that they became in need for new data structures that scale well with the number of components to make effective use of their aggregate performance <ref> W. Litwin, R. Moussa, and T. Schwarz, “Lh*_RS —a highly- available scalable distributed data structure,” ACM Trans. Database Syst., vol. 30, pp. 769–811, September 2005. [Online]. Available: http://doi.acm.org/10.1145/1093382.1093386 </ref>. In addition, low latency interconnected mulicomputers allow significant flexibility to the size of data structure without suffering from space utilization or access time penalties <ref>V. Gupta, M. Modi, and A. D. Pimentel, “Performance evaluation of the lh*lh scalable, distributed data structure for a cluster of workstations,” in Proceedings of the 2001 ACM symposium on Applied computing, ser. SAC ’01. New York, NY, USA: ACM, 2001, pp. 544–548. [Online]. Available: http://doi.acm.org/10.1145/372202.372458</ref>

The rest of this paper is organized as follows. Section 2 presents the work done to implement a distributed version of classical data structures including trees, sets, lists, graphs and queues, binary indexed trees (BITs) and distributed hash tables (DHT). The described work hide the distinction between local and distributed data structure manipulation, simplifying the programming model at some performance cost. Section 3 focuses on scalability issues in distributed data structure models that are based on linear hashing. Section 4 provides a performance parameters dealing with SDDS research outcomes. Finally, in section 5, a conclusion is drawn summarizing the presented work.

References

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