Service Placement with Provable Guarantees in Heterogeneous Edge Computing Systems
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Abstract (arXiv)
Mobile edge computing (MEC) is a promising technique for providing low-latency access to services at the network edge. The services are hosted at various types of edge nodes with both computation and communication capabilities. Due to the heterogeneity of edge node characteristics and user locations, the performance of MEC varies depending on where the service is hosted. In this paper, we consider such a heterogeneous MEC system, and focus on the problem of placing multiple services in the system to maximize the total reward. We show that the problem is NP-hard via reduction from the set cover problem, and propose a deterministic approximation algorithm to solve the problem, which has an approximation ratio that is not worse than left(1-e-1right)/4. The proposed algorithm is based on two sub-routines that are suitable for small and arbitrarily sized services, respectively. The algorithm is designed using a novel way of partitioning each edge node into multiple slots, where each slot contains one service. The approximation guarantee is obtained via a specialization of the method of conditional expectations, which uses a randomized procedure as an intermediate step. In addition to theoretical guarantees, simulation results also show that the proposed algorithm outperforms other state-of-the-art approaches.
Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). First posted on arXiv 17 Jun 2019.
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Citing papers per year: 2021: 2; 2022: 1; 2023: 2; 2024: 1.
6 papers.
- 2024SplitLLM: Collaborative Inference of LLMs for Model Placement and Throughput Optimizationcited by 10
- 2023Adaptive Compression-Aware Split Learning and Inference for Enhanced Network Efficiencycited by 8
- 2021Learning Augmented Index Policy for Optimal Service Placement at the Network Edgecited by 4
- 2022Digital Twin-Empowered Network Planning for Multi-Tier Computingcited by 4
- 2021Tailored Learning-Based Scheduling for Kubernetes-Oriented Edge-Cloud Systemcited by 3
- 2023On the Regret of Online Edge Service Hostingcited by 1
- 2024SplitLLM: Collaborative Inference of LLMs for Model Placement and Throughput Optimization14 Oct 2024
- 2023Adaptive Compression-Aware Split Learning and Inference for Enhanced Network Efficiency9 Nov 2023
- 2023On the Regret of Online Edge Service Hosting13 Mar 2023
- 2022Digital Twin-Empowered Network Planning for Multi-Tier Computing6 Oct 2022
- 2021Tailored Learning-Based Scheduling for Kubernetes-Oriented Edge-Cloud System17 Jan 2021
- 2021Learning Augmented Index Policy for Optimal Service Placement at the Network Edge10 Jan 2021