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

Workload Scheduling on heterogeneous Mobile Edge Cloud in 5G networks to Minimize SLA Violation