Joint Management of Compute and Radio Resources in Mobile Edge Computing: A Market Equilibrium Approach

Moro, Eugenio; Filippini, Ilario

doi:10.1109/tmc.2021.3091764

Cited by 16 publications

(3 citation statements)

References 48 publications

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“…In [183], joint management of communication and computation resources in edge computing is modeled as a multiple-leader common-follower Stackelberg game in which user devices are leaders, and the operator is the follower. A techno-economic market model was proposed in [184] for joint computing-networking resource management in order to meet the performance requirements of competing network slices sharing the edge infrastructure.…”

Section: B Technologies For Joint Resource Management In Edge Computingmentioning

confidence: 99%

Combining Federated Learning and Edge Computing toward Ubiquitous Intelligence: Challenges, Recent Advances, and Future Directions

Duan¹,

Huang²,

Hu³

et al. 2022

Preprint

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Full leverage of the huge volume of data generated on a large number of user devices for providing intelligent services in the 6G network calls for Ubiquitous Intelligence (UI). A key to developing UI lies in the involvement of the large number of network devices, which contribute their data to collaborative Machine Learning (ML) and provide their computational resources to support the learning process. Federated Learning (FL) is a new ML method that enables data owners to collaborate in model training without exposing private data, which allows user devices to contribute their data to developing UI. Edge computing deploys cloud-like capabilities at the network edge, which enables network devices to offer their computational resources for supporting FL. Therefore, a combination of FL and edge computing may greatly facilitate the development of ubiquitous intelligence. In this article, we present a comprehensive survey of the recent developments in technologies for combining FL and edge computing with a holistic vision across the fields of FL and edge computing. We conduct our survey from both the perspective of an FL framework deployed in an edge computing environment (FL in Edge) and the perspective of an edge computing system providing a platform for supporting FL (Edge for FL). From the FL in Edge perspective, we first identify the main challenges to FL in edge computing and then survey the representative technical strategies for addressing the challenges. From the Edge for FL perspective, we first analyze the key requirements for edge computing to support FL and then review the recent advances in edge computing technologies that may be exploited to meet the requirements. Then we discuss open problems and identify some possible directions for future research on combining FL and edge computing, with the hope of arousing the research community’s interest in this emerging and exciting interdisciplinary field.

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Section: B Technologies For Joint Resource Management In Edge Computingmentioning

confidence: 99%

Combining Federated Learning and Edge Computing toward Ubiquitous Intelligence: Challenges, Recent Advances, and Future Directions

Duan¹,

Huang²,

Hu³

et al. 2022

Preprint

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“…Simulation results validate that the method can reduce the task execution time up to 31.87% under this framework. Moro et al [77] designed an allocation mechanism using convex programming that solves the management problem of computing resources and radio resources in a MEC system, guaranteeing efficiency and fairness for the system. The simulation results confirm the theoretical nature of the market model.…”

Section: ) Resource Managementmentioning

confidence: 99%

A Survey of State-of-the-art on Edge Computing: Theoretical Models, Technologies, Directions, and Development Paths

Liu

Luo

Chen³

et al. 2022

IEEE Access

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In order to describe the roadmap of current edge computing research activities, we first gave a brief overview of the most advanced edge computing surveys published in the last six years. We found that edge computing has been adaptively integrated into growing number of applications. Edge computing theory and technology will bring substantial innovation and incentive, as well as a large number of application scenarios in different fields, such as edge computing assisted smart city, Internet of Vehicles(IoV), Industrial Internet and many other different fields. However, we believe that a comprehensive investigation of the essence of edge computing is still missing by using the most advanced theoretical models, technologies, directions and development paths. To fill this gap, we have conducted an in-depth survey by identifying and classifying the latest high-quality research results related to theoretical discoveries in edge computing, as well as edge convergence technologies that intersect with the frontiers of Information and Communication Technology (ICT). Finally, we also summarize several promising open issues as future research directions. We hope that this survey report will attract much more attention, stimulate fruitful discussions, and provide ideas and useful guidance for further research on the theoretical models, technologies, directions and development paths of edge computing.

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“…In [4], authors studied the multiple resource allocation for network slicing using different fairness criteria such as DRF, Ordered weighted averaging (OWA), weighted proportional fairness(WPF) and mood value rule. Some recent works, applied the Fisher Market model to study the multi-resource allocation problem for edge computing [5], [6], [7], [8]. In these works, the resource allocation is determined by solving Eisenberg-Gale [9] convex program to obtain the related market equilibrium.…”

Section: This Work Was Supported By Maestro-5g-anrmentioning

confidence: 99%

Multi Resource Allocation for Network Slices with Multi-Level fairness

Modina

Datar

El-Azouzi

et al. 2022

ICC 2022 - IEEE International Conference on Communications

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Network slicing is becoming the platform of choice for several applications and services. Nowadays most applications are virtualized to gain flexibility and portability. With network slicing, operators can create multiple network slices or tenants, which can be used for certain applications with specific requirements. Behind the network slicing, a slice expresses the need to access a precise service type, under a fully qualified set of computing and network requirements. Resource allocation decision encompasses a combination of different resource types (e.g., radio resource, CPU, memory, bandwidth). In this paper, we explore a differential pricing scheme that maximizes social welfare among slices as well as among end-users. To do so, we propose a pricing mechanism that makes fairness at multiple levels: fairness among slices and fairness among slice locations supported by each slice. Therefore, the proposed scheme is beneficial for both the slices and the end-users independent of their location. Additionally, we study the case where slices can manipulate their preferences to improve their utility. We show that the Fisher market game always has a pure Nash equilibrium and we prove Price of Anarchy is 1 N , where N is the number of slices. Finally, we conduct simulations using Amazon EC2 instances to numerically analyze and compare the performance of the mechanisms and confirm the theoretical properties of the market model.

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Joint Management of Compute and Radio Resources in Mobile Edge Computing: A Market Equilibrium Approach

Cited by 16 publications

References 48 publications

Combining Federated Learning and Edge Computing toward Ubiquitous Intelligence: Challenges, Recent Advances, and Future Directions

Combining Federated Learning and Edge Computing toward Ubiquitous Intelligence: Challenges, Recent Advances, and Future Directions

A Survey of State-of-the-art on Edge Computing: Theoretical Models, Technologies, Directions, and Development Paths

Multi Resource Allocation for Network Slices with Multi-Level fairness

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