RetroRenting: An Online Policy for Service Caching at the Edge

TL;DR

RetroRenting is an online policy for service caching at the edge that outperforms traditional TTL policies, offering near-optimal performance across various stochastic and adversarial request scenarios.

Contribution

The paper introduces RetroRenting, an online policy that is order-optimal in competitive ratio and performs well under stochastic arrivals, improving upon TTL policies for edge service caching.

Findings

RetroRenting outperforms TTL policies in adversarial and stochastic settings.

RR is order-optimal among deterministic online policies.

Simulations show RR's near-optimal performance with real-world traces.

Abstract

The rapid proliferation of shared edge computing platforms has enabled application service providers to deploy a wide variety of services with stringent latency and high bandwidth requirements. A key advantage of these platforms is that they provide pay-as-you-go flexibility by charging clients in proportion to their resource usage through short-term contracts. This affords the client significant cost-saving opportunities, by dynamically deciding when to host its service on the platform, depending on the changing intensity of requests. A natural policy for our setting is the Time-To-Live (TTL) policy. We show that TTL performs poorly both in the adversarial arrival setting, i.e., in terms of the competitive ratio, and for i.i.d. stochastic arrivals with low arrival rates, irrespective of the value of the TTL timer. We propose an online policy called RetroRenting (RR) and show that in the class of deterministic online policies, RR is order-optimal with respect to the competitive ratio. In addition, we provide performance guarantees for RR for i.i.d. stochastic arrival processes coupled with negatively associated rent cost sequences and prove that it compares well with the optimal online policy. Further, we conduct simulations using both synthetic and real world traces to compare the performance of RR with the optimal offline and online policies. The simulations show that the performance of RR is near optimal for all settings considered. Our results illustrate the universality of RR.