Mobile edge computing (MEC) literally pushes cloud computing from remote datacenters to the life radius of end users. By leveraging the widely adopted ETSI network function virtualization (NFV) architecture, MEC provisions elastic and resilient mobile edge applications with proximity. Typical MEC virtualization infrastructure allows configurable placement policy to deploy mobile edge applications as virtual machines (VMs): affinity can be used to put VMs on the same host for inter-VM networking performance, while anti-affinity is to separate VMs for high availability. In this paper, we propose a novel model to track the availability and cost impact from placement policy changes of the mobile edge applications. We formulate our model as a stochastic programming problem. To minimize complexity challenge, we also propose a heuristic algorithm. With our model, the unit resource cost increases when there are less resources left on a host. Applying affinity would take up more resources of the host but saves network bandwidth cost because of co-location. When enforcing anti-affinity, experimental results show increases of both availability and inter-host network bandwidth cost. For applications with different resource requirements, our model is able to find their sweet points with the consideration of both resource cost and application availability, which is vital in a less robust MEC cloud environment.

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Keywords 5G, Cloud computing, Mobile edge computing, Placement policy, Stochastic optimization
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Conference 2017 IEEE Global Communications Conference, GLOBECOM 2017
Zhu, H. (He), & Huang, C. (2018). Availability-aware mobile edge application placement in 5G networks. In 2017 IEEE Global Communications Conference, GLOBECOM 2017 - Proceedings (pp. 1–6). doi:10.1109/GLOCOM.2017.8254591