Proactive Caching for Low Access-Delay Services under Uncertain Predictions
Ran Liu, Edmund M. Yeh, Atilla Eryılmaz · Proceedings of the ACM on Measurement and Analysis of Computing Systems · 2019
Network traffic of delay-sensitive services has become a dominant part in the network. Proactive caching with the aid of predictive information has been proposed as a promising method to enhance the delay performance, which is one of the principal concerns of such services. In this paper, we analytically investigate the problem of how to efficiently utilize uncertain predictive information to design proactive caching strategies with provably good access-delay characteristics. First, we derive an upper bound for the average amount of proactive service per request that the system can support. Then we analyze the behavior of a family of threshold-based proactive strategies with a Markov chain, which shows that the average amount of proactive service per request can be maximized by properly selecting the threshold. Finally, we propose the UNIFORM strategy, which is the threshold-based strategy with the optimal threshold, and show that it outperforms the commonly used Earliest-Deadline-First (EDF) type proactive strategies in terms of delay. We perform extensive numerical experiments to demonstrate the influence of thresholds on delay performance under the threshold-based strategies, and specifically compare the EDF strategy and the UNIFORM strategy to verify our results.