Self-adjusting Advertisement of Cache Indicators with Bandwidth Constraints

TL;DR

This paper introduces a self-adjusting algorithm for cache advertisement strategies that optimizes access cost and bandwidth without prior knowledge, adapting to various parameters and workloads.

Contribution

It presents a novel adaptive algorithm that dynamically selects cache advertisement policies without requiring prior configuration or workload knowledge.

Findings

Achieves near-optimal cost comparable to the best static configuration

Works effectively across multiple cache policies and real workloads

Does not require prior information about cache or workload parameters

Abstract

Cache advertisements reduce the access cost by allowing users to skip the cache when it does not contain their datum. Such advertisements are used in multiple networked domains such as 5G networks, wide area networks, and information-centric networking. The selection of an advertisement strategy exposes a trade-off between the access cost and bandwidth consumption. Still, existing works mostly apply a trial-and-error approach for selecting the best strategy, as the rigorous foundations required for optimizing such decisions is lacking. Our work shows that the desired advertisement policy depends on numerous parameters such as the cache policy, the workload, the cache size, and the available bandwidth. In particular, we show that there is no ideal single configuration. Therefore, we design an adaptive, self-adjusting algorithm that periodically selects an advertisement policy. Our algorithm does not require any prior information about the cache policy, cache size, or workload, and does not require any apriori configuration. Through extensive simulations, using several state-of-the-art cache policies, and real workloads, we show that our approach attains a similar cost to that of the best static configuration (which is only identified in retrospect) in each case.