Cache Policies for Linear Utility Maximization

• Published in: IEEE/ACM transactions on networking Vol. 26; no. 1; pp. 302 - 313
• Main Authors: Neglia, Giovanni, Carra, Damiano, Michiardi, Pietro
• Format: Journal Article
• Language: English
• Published: New York IEEE 01-02-2018; The Institute of Electrical and Electronics Engineers, Inc. (IEEE); IEEE/ACM
• Subjects: Approximation algorithms; Asymptotic properties; Cache storage; Computer Science; content distribution networks; Cost analysis; Detectors; Estimation; Formulations; Heuristic algorithms; IEEE transactions; Knapsack problem; Maximization; Modeling and Simulation; Networking and Internet Architecture; Optimization; Policies; Retrieval; Simulated annealing; State of the art; Utilities
• ISSN: 1063-6692; 1558-2566
• DOI: 10.1109/TNET.2017.2783623

Cache policies to minimize the content retrieval cost have been studied through competitive analysis when the miss costs are additive and the sequence of content requests is arbitrary. More recently, a cache utility maximization problem has been introduced, where contents have stationary popularities and utilities are strictly concave in the hit rates. This paper bridges the two formulations, considering linear costs and content popularities. We show that minimizing the retrieval cost corresponds to solving an online knapsack problem, and we propose new dynamic policies inspired by simulated annealing, including DynqLRU, a variant of qLRU. We prove that DynqLRU asymptotically asymptotic converges to the optimum under the characteristic time approximation. In a real scenario, popularities vary over time and their estimation is very difficult. DynqLRU does not require popularity estimation, and our realistic, trace-driven evaluation shows that it significantly outperforms state-of-the-art policies, with up to 45% cost reduction.