On formulation of online algorithm and framework of near-optimally tractable eviction for nonuniform caches

• Published in: Computer networks (Amsterdam, Netherlands : 1999) Vol. 178; p. 107332
• Main Authors: Banditwattanawong, Thepparit, Masdisornchote, Masawee
• Format: Journal Article
• Language: English
• Published: Amsterdam Elsevier B.V 04.09.2020; Elsevier Sequoia S.A
• Subjects: Algorithms; Cloud computing; Cost saving ratio; Data retrieval; Deep neural network; Digital media; Neural networks; Nonuniformity; Offline cache eviction; Optimization; Time-to-live; Upper bounds; Cost saving ratio; Deep neural network; Offline cache eviction; Time-to-live; Nonuniformity
• ISSN: 1389-1286; 1872-7069
• DOI: 10.1016/j.comnet.2020.107332

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.