Raven
Xinyue Hu, Eman Ramadan, Wei Ye, F. Tian, Zhi-Li Zhang
Abstract
Performance of caching algorithms not only determines the quality of experience for users, but also affects the operating and capital expenditures for cloud service providers. Today's production systems rely on heuristics such as LRU (least recently used) and its variants, which work well for certain types of workloads, and cannot effectively cope with diverse and time-varying workload characteristics. While learning-based caching algorithms have been proposed to deal with these challenges, they still impose assumptions about workload characteristics and often suffer poor generalizability.
Topics & Concepts
Computer scienceWorkloadHeuristicsGeneralizability theoryCloud computingQuality of serviceService (business)Distributed computingComputer networkOperating systemEconomyMathematicsStatisticsEconomicsCaching and Content DeliveryOptimization and Search ProblemsIoT and Edge/Fog Computing