Speculative Lock Management to Increase Concurrency in Mobile Environments

  • P. Krishna Reddy
  • Masaru Kitsuregawa
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1748)


Since mobile transactions are long-lived, in case of a conflict, the waiting transactions are either blocked for longer durations or aborted if two-phase locking is employed for concurrency control. In this paper, we propose mobile speculative locking (MSL) protocol to reduce the blocking of transactions. MSL allows a transaction to release a lock on a data object whenever it produces corresponding after-image value. By accessing both before- and after-images, the waiting transaction carries out speculative executions at the mobile host. Before the end of commit processing, the transaction that has carried out speculative executions retains appropriate execution based on the termination decisions of preceding transactions. The MSL approach requires extra resources at the mobile host to carry out speculative executions. Since mobile host is operated by single user, we assume that it can support a reasonable number of speculative executions. Through analysis it has been shown that MSL increases concurrency with limited resources available at mobile host.


Data Object Mobile Host Mobile Environment Concurrency Control Object Server 
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  1. 1.
    D. Agrawal, A. El Abbadi, and A.E. Lang, The performance of protocols based on locks with ordered sharing, IEEE Transactions on Knowledge and Data Engineering, vol.6,no.5, October 1994, pp. 805–818.CrossRefGoogle Scholar
  2. 2.
    R. Alonso and H.F. Korth, Database system issues in nomadic computing, Proc. of the 1993 ACM SIGMOD, pp. 388–392, 1993.Google Scholar
  3. 3.
    A. Burger and P. Thanisch, Branching transactions: a transaction model for parallel database systems, Lecture Notes in Computer Science 826.Google Scholar
  4. 4.
    Azer Bestavros and Spyridon Braoudakis, Value-cognizant speculative concurrency control, proc. of the 21th VLDB Conference, pp.122–133, 1995.Google Scholar
  5. 5.
    B.R. Badrinath and K. Ramamritam, Semantics based concurrency control: Beyond commutativity, ACM Transactions on Database Systems, vol.17,no.1, March 1992, pp. 163–199.CrossRefGoogle Scholar
  6. 6.
    P.A. Bernstein, V. Hadzilacos and N. Goodman, Concurrency control and recovery in database systems (Addison-Wesley, 1987).Google Scholar
  7. 7.
    M.H. Dunham, A. Helal, and S. Balakrishnan, A mobile transaction model that captures both the data and movement behavior, Mobile Networks and Applications, vol. 2, pp. 147–162, 1997.CrossRefGoogle Scholar
  8. 8.
    A.K. Elmagarmid, A survey of distributed deadlock detection algorithms, ACM SIGMOD RECORDS, 15(3), September 1986, pp. 37–45.CrossRefGoogle Scholar
  9. 9.
    T. Imielinksi, and B.R. Badrinath, Wireless mobile computing: Challenges in Data Management, Communications of ACM, 37(10), October 1994.Google Scholar
  10. 10.
    H.V. Jagadish, and O. Shmueli, A proclamation-based model for cooperation transactions, proceedings of the 18th VLDB Conference, Canada, 1992.Google Scholar
  11. 11.
    J. Jing, O. Bukhres, and A. Elmagarmid, Distributed lock management for mobile transactions, in proceedings of 15th International Conference on Distributed Computing Systems, pp. 118–125, June 1995.Google Scholar
  12. 12.
    E. Knapp. Deadlock detection in distributed databases, ACM Computing Surveys, 19(4), December 1987, pp.303–328.CrossRefGoogle Scholar
  13. 13.
    P. Krishna Reddy and Masaru Kitsuregawa, Improving performance in distributed database systems using speculative transaction processing, in proceedings of The Second European Parallel and Distributed Systems conference (Euro-PDS’98), 1998, Vienna, Austria.Google Scholar
  14. 14.
    E. Pitoura, and B. Bhargawa, Maintaining Consistency of Data in Mobile Computing Environments, in proceedings of 15th International Conference on Distributed Computing Systems, pp. 404–413, June 1995.Google Scholar
  15. 15.
    K. Salem, H. Garciamolina and J. Shands, Altruistic locking, ACM Transactions on Database Systems, vol. 19,no.1, March 1994, pp. 117–165.CrossRefGoogle Scholar
  16. 16.
    S.K. Madira, S.N. Maheswari, B. Chandra and Bharat Bhargawa, Crash Recovery algorithm in open and safe nested transaction model, Lecture Notes in Computer Science, vol. 1308, Springer-Verlag, 1997, pp. 440–451.Google Scholar
  17. 17.
    G.D. Walborn, and P.K. Chrysanthis, Supporting semantics-based transaction processing in mobile database applications, in proceedings of 14th IEEE Symposium on Reliable Distributed Systems, pp. 31–40, 1995.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1999

Authors and Affiliations

  • P. Krishna Reddy
    • 1
  • Masaru Kitsuregawa
    • 1
  1. 1.Institute of Industrial ScienceThe University of TokyoMinato-ku, TokyoJapan

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