BIG Cache Abstraction for Cache Networks

Eman Ramadan, Arvind Narayanan, Zhi-Li Zhang, Runhui Li, Gong Zhang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

10 Scopus citations


In this paper, we advocate the notion of 'BIG' cache as an innovative abstraction for effectively utilizing the distributed storage and processing capacities of all servers in a cache network. The 'BIG' cache abstraction is proposed to partly address the problem of (cascade) thrashing in a hierarchical network of cache servers, where it has been known that cache resources at intermediate servers are poorly utilized, especially under classical cache replacement policies such as LRU. We lay out the advantages of 'BIG' cache abstraction and make a strong case both from a theoretical standpoint as well as through simulation analysis. We also develop the dCLIMB cache algorithm to minimize the overheads of moving objects across distributed cache boundaries and present a simple yet effective heuristic for addressing the cache allotment problem in the design of 'BIG' cache abstraction.

Original languageEnglish (US)
Title of host publicationProceedings - IEEE 37th International Conference on Distributed Computing Systems, ICDCS 2017
EditorsKisung Lee, Ling Liu
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages11
ISBN (Electronic)9781538617915
StatePublished - Jul 13 2017
Event37th IEEE International Conference on Distributed Computing Systems, ICDCS 2017 - Atlanta, United States
Duration: Jun 5 2017Jun 8 2017

Publication series

NameProceedings - International Conference on Distributed Computing Systems


Other37th IEEE International Conference on Distributed Computing Systems, ICDCS 2017
Country/TerritoryUnited States

Bibliographical note

Funding Information:
This research was supported in part by NSF grants CNS-1411636, CNS 1618339 and CNS 1617729 and a Huawei gift.


  • BIG Cache
  • Cache Replacement Policies
  • Caching
  • Content Network Distribution
  • Hierarchical Caching


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