Online Partial Service Hosting at the Edge

We consider the problem of service hosting where an application provider can dynamically rent edge computing resources and serve user requests from the edge to deliver a better quality of service. A key novelty of this work is that we allow the service to be hosted partially at the edge that enables...

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Bibliographic Details
Published in:ACM transactions on modeling and performance evaluation of computing systems 2023-10, Vol.9 (1), p.1-31, Article 2
Main Authors: Borusu, V. S. Ch Lakshmi Narayana, Agarwala, Mohit, R, Sri Prakash, Karamchandani, Nikhil, Moharir, Sharayu
Format: Article
Language:English
Subjects:
Adversary models
Caching and paging algorithms
Probabilistic computation
Theory of computation
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Summary:We consider the problem of service hosting where an application provider can dynamically rent edge computing resources and serve user requests from the edge to deliver a better quality of service. A key novelty of this work is that we allow the service to be hosted partially at the edge that enables a fraction of the user query to be served by the edge. We model the total cost for (partially) hosting a service at the edge as a combination of the latency in serving requests, the bandwidth consumption, and the time-varying cost for renting edge resources. We propose an online policy called α-RetroRenting (α-RR) that dynamically determines the fraction of the service to be hosted at the edge in any time-slot, based on the history of the request arrivals and the rent cost sequence. As our main result, we derive an upper bound on α-RR’s competitive ratio with respect to the offline optimal policy that knows the entire request arrival and rent cost sequence in advance. In addition, we provide performance guarantees for our policy in the setting where the request arrival process is stochastic. We conduct extensive numerical evaluations to compare the performance of α-RR with various benchmarks for synthetic and trace-based request arrival and rent cost processes and find several parameter regimes where α-RR’s ability to store the service partially greatly improves cost-efficiency.
ISSN:2376-3639
2376-3647
DOI:10.1145/3616866