• DocumentCode
    1554619
  • Title

    Volume leases for consistency in large-scale systems

  • Author

    Yin, Jian ; Alvisi, Lorenzo ; Dahlin, Michael ; Lin, Calvin

  • Author_Institution
    Dept. of Comput. Sci., Texas Univ., Austin, TX, USA
  • Volume
    11
  • Issue
    4
  • fYear
    1999
  • Firstpage
    563
  • Lastpage
    576
  • Abstract
    This article introduces volume leases as a mechanism for providing server-driven cache consistency for large-scale, geographically distributed networks. Volume leases retain the good performance, fault tolerance, and server scalability of the semantically weaker client-driven protocols that are now used on the Web. Volume leases are a variation of object leases, which were originally designed for distributed file systems. However, whereas traditional object leases amortize overheads over long lease periods, volume leases exploit spatial locality to amortize overheads across multiple objects in a volume. This approach allows systems to maintain good write performance even in the presence of failures. Using trace-driven simulation, we compare three volume lease algorithms against four existing cache consistency algorithms and show that our new algorithms provide strong consistency while maintaining scalability and fault-tolerance. For a trace-based workload of Web accesses, we find that volumes can reduce message traffic at servers by 40 percent compared to a standard lease algorithm, and that volumes can considerably reduce the peak load at servers when popular objects are modified
  • Keywords
    Internet; cache storage; data integrity; information resources; software fault tolerance; software performance evaluation; World Wide Web; cache consistency; client-driven protocols; fault tolerance; large-scale networks; large-scale systems consistency; message traffic; object leases; performance; server scalability; server-driven cache consistency; trace-driven simulation; volume leases; Access protocols; Delay; Fault tolerance; Fault tolerant systems; File servers; File systems; Humans; Large-scale systems; Network servers; Scalability;
  • fLanguage
    English
  • Journal_Title
    Knowledge and Data Engineering, IEEE Transactions on
  • Publisher
    ieee
  • ISSN
    1041-4347
  • Type

    jour

  • DOI
    10.1109/69.790806
  • Filename
    790806