Volume 7 Number 2 (Feb. 2012)
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JSW 2012 Vol.7(2): 420-430 ISSN: 1796-217X
doi: 10.4304/jsw.7.2.420-430

A Comprehensive Failure Recovery Algorithm for LRTs Based on Paired Net

Xiaoyong Mei1, 2, Yiyan Fan1, Changqin Huang3, and Xiaolin Zheng4
1School of Computer Science and Technology, Hunan University of Arts and Science, Changde 415000, China
2School of Information Science and Technology, Sun Yat-sen University, Guangzhou 510275, China
3College of Educational Information Technology, South China Normal University, Guangzhou 510631
4College of Computer Science and Technology, Zhejiang University, Hangzhou 310027

Abstract—Failure recovery optimization is an important way for enhancing efficiency of Long Running Transaction (LRT) processing. In this paper, to solve the efficiency problem of LRT failure recovery, a Comprehensive Recovery Model for LRTs (LCRM) is constructed, which divides LRTs into a series of sub-transactions in different levels, and supports versatile transaction properties of LRT. Based on LCRM, a Comprehensive Failure Recovery algorithm for LRTs (LCFR) is proposed. This algorithm uses methods of forward recovery, backward recovery and alternative recovery. It supports auto-recovery of failures during the execution of LRTs. LCFR guarantees LRT’s semantic atomicity property and durability property. By restricting the recovery scope in lower level of complex LRTs, LCFR limits the quantity of sub-transactions to be recovered. Thus, it reduces unnecessary loss of time and enhances the efficiency of failure recovery. Experiment results show that LCFR can reduce the time required for failure recovery and decrease the failure rate of LRT processing.

Index Terms—long running transaction modeling, scopebased recovery, hierarchical recovery strategy, failure recovery algorithm


Cite: Xiaoyong Mei, Yiyan Fan, Changqin Huang, Xiaolin Zheng, "A Comprehensive Failure Recovery Algorithm for LRTs Based on Paired Net," Journal of Software vol. 7, no. 2, pp. 420-430, 2012.

General Information

ISSN: 1796-217X (Online)
Frequency: Monthly (2006-2019); Bimonthly (Since 2020)
Editor-in-Chief: Prof. Antanas Verikas
Executive Editor: Ms. Yoyo Y. Zhou
Abstracting/ Indexing: DBLP, EBSCO, Google Scholar, ProQuest, INSPEC, ULRICH's Periodicals Directory, WorldCat, etc
E-mail: jsw@iap.org
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