On formulation of online algorithm and framework of near-optimally tractable eviction for nonuniform caches
Distributed data sharing in Internet, social, and cloud computing paradigms incurs nonuniform costs such as consumed downstream bandwidth, downloading delays, and cloud-data-out monetary charges. To alleviate such costs, caches have been widely deployed and researched to find efficient online-cache-...
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Published in | Computer networks (Amsterdam, Netherlands : 1999) Vol. 178; p. 107332 |
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Main Authors | , |
Format | Journal Article |
Language | English |
Published |
Amsterdam
Elsevier B.V
04.09.2020
Elsevier Sequoia S.A |
Subjects | |
Online Access | Get full text |
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Summary: | Distributed data sharing in Internet, social, and cloud computing paradigms incurs nonuniform costs such as consumed downstream bandwidth, downloading delays, and cloud-data-out monetary charges. To alleviate such costs, caches have been widely deployed and researched to find efficient online-cache-eviction algorithms. However, existing online algorithms have no offline foundation, thus they are far from a global optimum. This paper proposes a framework for developing an online cache eviction algorithm, which is grounded in a near-optimally and tractably offline algorithm namely Shortest Maximum Forward Distance (SMFD). On the formulation of the framework, the near-optimality and tractability properties of existing offline algorithms were evaluated through the formal evaluation of the optimality and computational tractability based upon variable object costs. Subsequently, the lowest-complexity suboptimal schemes were empirically investigated to seek a near-optimal offline one, which appears to be SMFD. Also, the results originally show that SMFD and its variant can practically achieve multiple conventionally upper bounds of limitless cache sizes in a stable and simultaneous manner. SMFD was then transformed into novel online SMFD by quantifying the offline property, maximum forward distance, of SMFD with cache-scope time-to-live approximation. To guarantee the effectiveness of online SMFD over evolving request streams, a safety bound guideline and a cache-scope time-to-live distribution model are also proposed. Finally, experience on the framework was gained via experiments based on deep-neural-network models. |
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ISSN: | 1389-1286 1872-7069 |
DOI: | 10.1016/j.comnet.2020.107332 |