  Real-time applications in the next generation networks often rely upon
offloading the computational task to a \textit{nearby} server to achieve
ultra-low latency. Augmented reality applications for instance have strict
latency requirements which can be fulfilled by an interplay between cloud and
edge servers. In this work, we study the impact of load on a hybrid edge cloud
system. The resource distribution between central cloud and edge affects the
capacity of the network. Optimizing delay and capacity constraints of this
hybrid network is similar to maximum cardinal bin packing problem which is
NP-hard. We design a simulation framework using a city-scale access point
dataset to propose an enhanced capacity edge cloud network while answering
following questions: (a) how much load an edge cloud network can support
without affecting the performance of an application, (b) how is application
delay-constraint limit affects the capacity of the network, (c) what is the
impact of load and resource distribution on goodput, (d) under what
circumstances, cloud can perform better than edge network and (e) what is the
impact of inter-edge networking bandwidth on the system capacity. An evaluation
system and model is developed to analyze the tradeoffs of different edge cloud
deployments and results are shown to support the claims.
