Volume Leases for Consistency in Large-Scale Systems

نویسندگان

  • Jian Yin
  • Lorenzo Alvisi
  • Michael Dahlin
  • Calvin Lin
چکیده

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% compared to a standard lease algorithm, and that volumes can considerably reduce the peak load at servers when popular objects are modified.

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عنوان ژورنال:
  • IEEE Trans. Knowl. Data Eng.

دوره 11  شماره 

صفحات  -

تاریخ انتشار 1999