  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.
