# Edge Federation: Towards an Integrated Service Provisioning Model

## TL;DR

This paper introduces an edge federation model that enables resource cooperation among multiple edge infrastructure providers, improving resource utilization and reducing costs for edge service provisioning.

## Contribution

It proposes a novel integrated resource provisioning framework for edge computing that overcomes limitations of private environments and efficiently manages resources across providers.

## Key findings

- Edge federation reduces provider costs by up to 24.5%.
- The LP-based scheduling algorithm effectively handles dynamic user demands.
- Experimental results validate the efficiency of the proposed model.

## Abstract

Edge computing is a promising computing paradigm for pushing the cloud service to the network edge. To this end, edge infrastructure providers (EIPs) need to bring computation and storage resources to the network edge and allow edge service providers (ESPs) to provision latency-critical services to users. Currently, EIPs prefer to establish a series of private edge-computing environments to serve specific requirements of users. This kind of resource provisioning mechanism severely limits the development and spread of edge computing for serving diverse user requirements. To this end, we propose an integrated resource provisioning model, named edge federation, to seamlessly realize the resource cooperation and service provisioning across standalone edge computing providers and clouds. To efficiently schedule and utilize the resources across multiple EIPs, we systematically characterize the provisioning process as a large-scale linear programming (LP) problem and transform it into an easily solved form. Accordingly, we design a dynamic algorithm to tackle the varying service demands from users. We conduct extensive experiments over the base station networks in Toronto city. Compared with the existing fixed contract model and multihoming model, edge federation can reduce the overall cost of EIPs by 23.3% to 24.5%, and 15.5% to 16.3%, respectively.