Delay-aware power optimization model for mobile edge computing systems

Reducing the total power consumption and network
delay are among the most interesting issues facing
large-scale Mobile Cloud Computing (MCC) systems and
their ability to satisfy the Service Level Agreement (SLA).
Such systems utilize cloud computing infrastructure to support
offloading some of user’s computationally heavy tasks
to the cloud’s datacenters. However, the delay incurred
by such offloading process lead the use of servers (called
cloudlets) placed in the physical proximity of the users, creating
what is known as Mobile Edge Computing (MEC).
The cloudlet-based infrastructure has its challenges such
as the limited capabilities of the cloudlet system (in terms
of the ability to serve different request types from users
in vast geographical regions). To cover the users demand
for different types of services and in vast geographical
regions, cloudlets cooperate among each other by passing
user requests from one cloudlet to another. This cooperation
affects both power consumption and delay. In this work,
we present a mixed integer linear programming (MILP)