On the Regret of Online Edge Service Hosting
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Abstract (arXiv)
We consider the problem of service hosting where a service provider can dynamically rent edge resources via short term contracts to ensure better quality of service to its customers. The service can also be partially hosted at the edge, in which case, customers' requests can be partially served at the edge. The total cost incurred by the system is modeled as a combination of the rent cost, the service cost incurred due to latency in serving customers, and the fetch cost incurred as a result of the bandwidth used to fetch the code/databases of the service from the cloud servers to host the service at the edge. In this paper, we compare multiple hosting policies with regret as a metric, defined as the difference in the cost incurred by the policy and the optimal policy over some time horizon T. In particular we consider the Retro Renting (RR) and Follow The Perturbed Leader (FTPL) policies proposed in the literature and provide performance guarantees on the regret of these policies. We show that under i.i.d stochastic arrivals, RR policy has linear regret while FTPL policy has constant regret. Next, we propose a variant of FTPL, namely Wait then FTPL (W-FTPL), which also has constant regret while demonstrating much better dependence on the fetch cost. We also show that under adversarial arrivals, RR policy has linear regret while both FTPL and W-FTPL have regret O(√T) which is order-optimal.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 14 of its 32 references that are in the map. First posted on arXiv 13 Mar 2023.
Builds on
14 of 32 references.
References · 14
- 2017Mobile Edge Computing: A Survey on Architecture and Computation Offloadingcited by 53
- 2014Online Linear Optimization via Smoothingcited by 24
- 2019Learning to Cache With No Regretscited by 16
- 2020LeadCache: Regret-Optimal Caching in Networkscited by 14
- 2021No-Regret Caching via Online Mirror Descentcited by 12
- 2021Online Caching with Optimal Switching Regretcited by 11
- 2019Service Placement with Provable Guarantees in Heterogeneous Edge Computing Systemscited by 6
- 2019Budget-constrained Edge Service Provisioning with Demand Estimation via Bandit Learningcited by 5
- 2019Joint Optimization of Service Caching Placement and Computation Offloading in Mobile Edge Computing Systemscited by 5
- 2021Learning Augmented Index Policy for Optimal Service Placement at the Network Edgecited by 4
- 2015Dynamic Service Migration in Mobile Edge Computing Based on Markov Decision Processcited by 1
- 2019RetroRenting: An Online Policy for Service Caching at the Edgecited by 1
2 more
- 2021Online Partial Service Hosting at the Edgecited by 1
- 2021Online Service Caching and Routing at the Edge with Unknown Arrivalscited by 1
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Citing papers per year: 2025: 1.
1 paper.
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