  Multiple access mobile edge computing is an emerging technique to bring
computation resources close to end mobile users. By deploying edge servers at
WiFi access points or cellular base stations, the computation capabilities of
mobile users can be extended. Existing works mostly assume the remote cloud
server can be viewed as a special edge server or the edge servers are willing
to cooperate, which is not practical. In this work, we propose an edge-cloud
cooperative architecture where edge servers can rent for the remote cloud
servers to expedite the computation of tasks from mobile users. With this
architecture, the computation offloading problem is modeled as a mixed integer
programming with delay constraints, which is NP-hard. The objective is to
minimize the total energy consumption of mobile devices. We propose a greedy
algorithm as well as a simulated annealing algorithm to effectively solve the
problem. Extensive simulation results demonstrate that, the proposed greedy
algorithm and simulated annealing algorithm can achieve the near optimal
performance. On average, the proposed greedy algorithm can achieve the same
application completing time budget performance of the Brute Force optional
algorithm with only 31\% extra energy cost. The simulated annealing algorithm
can achieve similar performance with the greedy algorithm.
