Redundant Virtual Machine Placement in Mobile Edge Computing Mobile edge computing (MEC), as an extension of the cloud computing paradigm to edge networks, overcomes some obstacles of traditional mobile cloud computing by offering ultrashort latency and less core network traffic. The edge resources are virtualized to be shared among multiple mobile users. Consolidated server systems using server virtualization involves serious risks of hosting server failures. Redundant virtual machine placement can be an effective countermeasure. This problem has been well solved in cloud computing. However, it is a more nontrivial task to efficiently deploy virtual machines redundantly in MEC networks. To address this issue, a redundant virtual machine placement method in MEC is proposed. Firstly, this method estimates the minimum number of virtual machines required based on the performance requirements of the applications. Then, the redundant virtual machine placement problem is formulated as a combinatorial optimization formulation. Finally, a optimal placement strategy searching algorithm is proposed to determine the optimal location of the virtual machines. The strategy can preserve the minimum configuration in the event of failure of any k edge servers. The experiment results illustrates the effectiveness of our algorithm.

Reusing Subchains of Network Functions to Support MEC Services Mobile Edge Computing (MEC) and Network Function Virtualization (NFV) are widely considered to be key players in the 5G era. Whereas MEC enables to reduce latency significantly by allowing applications to be deployed close to end users, NFV allows for flexible deployment of virtualized network functions. The performance and flexibility can be improved further by combining MEC and NFV. Existing frameworks for managing and orchestrating MEC applications and NFV are either tightly coupled or completely separated. The former design is inflexible and increases the complexity of one framework, while the latter leads to inefficient use of computation resources. In this paper, we extend our Automated Provisioning Framework for MEC (APMEC), which combines each MEC application and its respective network service (comprising a chain of virtual network functions) in a MEC service. We propose a novel MEC service placement algorithm allowing to reuse a subset of yet underloaded network functions. Our evaluation results, obtained from a testbed implementation and simulations, show that our solution allows to remarkably increase the utilization of network functions and simultaneously reduce routing cost. Specifically, it allows to accept at least 60% more user requests, and at the same time lowering the routing cost by more than 30%, as compared to the baseline approach.