A Generic Framework for Task Offloading in mmWave MEC Backhaul Networks

With the emergence of millimeter-Wave (mmWave) communication technology, the capacity of mobile backhaul networks can be significantly increased. On the other hand, Mobile Edge Computing (MEC) provides an appropriate infrastructure to offload latency-sensitive tasks. However, the amount of resources in MEC servers is typically limited. Therefore, it is important to intelligently manage the MEC task offloading by optimizing the backhaul bandwidth and edge server resource allocation in order to decrease the overall latency of the offloaded tasks. This paper investigates the task allocation problem in MEC environment, where the mmWave technology is used in the backhaul network. We formulate a Mixed Integer NonLinear Programming (MINLP) problem with the goal to minimize the total task serving time. Its objective is to determine an optimized network topology, identify which server is used to process a given offloaded task, find the path of each user task, and determine the allocated bandwidth to each task on mmWave backhaul links. Because the problem is difficult to solve, we develop a two-step approach. First, a Mixed Integer Linear Program (MILP) determining the network topology and the routing paths is optimally solved. Then, the fractions of bandwidth allocated to each user task are optimized by solving a quasi-convex problem. Numerical results illustrate the obtained topology and routing paths for selected scenarios and show that optimizing the bandwidth allocation significantly improves the total serving time, particularly for bandwidth-intensive tasks

Inducing optimal service capacities via performance-based allocation of demand in a queueing system with multiple servers

Theme: Soft Computing Techniques for Advanced Manufacturing and Service SystemsIn this paper, we study the use of performance-based allocation of demand in a multiple-server queueing system. The same problem with two servers have been studied in the literature. Specifically, it has been proposed and proved that the linear allocation and mixed threshold allocation policies are, respectively, the optimal state-independent and state-dependent allocation policy in the two-server case. The multiple-server linear allocation has also been shown to be the optimal state-independent policy with multiple servers. In our study, we focus on the use of a multiple-server mixed threshold allocation policy to replicate the demand allocation of a given state-independent policy to achieve a symmetric equilibrium with lower expected sojourn time. Our results indicate that, for any given multiple-server state-independent policy that prohibits server overloading, there exists a multiple-server mixed threshold policy that gives the same demand allocation and thus have the same Nash equilibrium (if any). Moreover, such a policy can be designed so that the expected sojourn time at a symmetric equilibrium is minimized. Therefore, our results concur with previous two-server results and affirm that a trade-off between incentives and efficiency need not exist in the case of multiple servers.published_or_final_versionThe 40th International Conference on Computers and Industrial Engineering, (CIE 2010), Hyogo, Japan, 25-28 July 2010. In Proceedings of the International Conference on Computers & Industrial Engineering, 2010, p. 1-