  Smart devices have become an indispensable part of our lives and gain
increasing applicability in almost every area. Latency-aware applications such
as Augmented Reality (AR), autonomous driving, and online gaming demand more
resources such as network bandwidth and computational capabilities. Since the
traditional mobile networks cannot fulfill the required bandwidth and latency,
Mobile Edge Cloud (MEC) emerged to provide cloud computing capabilities in the
proximity of users on 5G networks. In this paper, we consider a heterogeneous
MEC network with numerous mobile users that send their tasks to MEC servers.
Each task has a maximum acceptable response time. Non-uniform distribution of
users makes some MEC servers hotspots that cannot take more. A solution is to
relocate the tasks among MEC servers, called Workload Migration. We formulate
this problem of task scheduling as a mixed-integer non-linear optimization
problem to minimize the number of Service Level Agreement (SLA) violations.
Since solving this optimization problem has high computational complexity, we
introduce a greedy algorithm called MESA, Migration Enabled Scheduling
Algorithm, which reaches a near-optimal solution quickly. Our experiments show
that in the term of SLA violation, MESA is only 8% and 11% far from the optimal
choice on the average and the worst-case, respectively. Moreover, the migration
enabled solution can reduce SLA violations by about 30% compare to assigning
tasks to MEC servers without migration.
