A Scored Semantic Cache Replacement Strategy for Mobile Cloud Database Systems

Zachary Arani (1) , Drake Chapman (1) , Chenxiao Wang (1) , Le Gruenwald (1) , Laurent d'Orazio (2) , Taras Basiuk (1)

1 OU - University of Oklahoma
2 SHAMAN - A Symbolic and Human-centric view of dAta MANagement

Current mobile cloud database systems are widespread and require special considerations for mobile devices. Although many systems rely on numerous metrics for use and optimization, few systems leverage metrics for decisional cache replacement on the mobile device. In this paper we introduce the Lowest Scored Replacement (LSR) policy-a novel cache replacement policy based on a predefined score which leverages contextual mobile data and user preferences for decisional replacement. We show an implementation of the policy using our previously proposed MOCCAD-Cache as our decisional semantic cache and our Normalized Weighted Sum Algorithm (NWSA) as a score basis. Our score normalization is based on the factors of query response time, energy spent on mobile device, and monetary cost to be paid to a cloud provider. We then demonstrate a relevant scenario for LSR, where it excels in comparison to the Least Recently Used (LRU) and Least Frequently Used (LFU) cache replacement policies.

Mots clés: Big Data; Cloud Computing; Caching

Domaines: Base de données [cs.DB]

Zachary Arani, Drake Chapman, Chenxiao Wang, Le Gruenwald, Laurent d'Orazio, et al.. A Scored Semantic Cache Replacement Strategy for Mobile Cloud Database Systems. International Workshop on BI & BIG DATA APPLICATIONS (BBIGAP) @ ADBIS, Aug 2020, Lyon, France. ⟨hal-03128881⟩