Efficient Exploitation of Mobile Edge Computing for Virtualized 5G in EPC Architectures

Citations Over TimeTop 10% of 2016 papers

Abstract

To pave the way towards disclosing the full potential of 5G networking, emerging Mobile Edge Computing techniques are gaining momentum in both academic and industrial research as a means to enhance infrastructure scalability and reliability by moving control functions close to the edge of the network. After the promising results under achievement within the EU Mobile Cloud Networking project, we claim the suitability of deploying Evolved Packet Core (EPC) support solutions as a Service (EPCaaS) over a uniform edge cloud infrastructure of Edge Nodes, by following the concepts of Network Function Virtualization (NFV). This paper originally focuses on the support needed for efficient elasticity provisioning of EPCaaS stateful components, by proposing novel solutions for effective subscribers' state management in quality-constrained 5G scenarios. In particular, to favor flexibility and high-availability against network function failures, we have developed a state sharing mechanism across different data centers even in presence of firewall/network encapsulation. In addition, our solution can dynamically select which state portions should be shared and to which Edge Nodes. The reported experimental results, measured over the widely recognized Open5GCore testbed, demonstrate the feasibility and effectiveness of the approach, as well as its capability to satisfy "carrier-grade" quality requirements while ensuring good elasticity and scalability.

Related Papers

• → Deep Reinforcement Learning for Offloading and Resource Allocation in Vehicle Edge Computing and Networks(2019)570 cited
• → Multi-Objective Computation Sharing in Energy and Delay Constrained Mobile Edge Computing Environments(2020)194 cited
• → Optimized Task Allocation for IoT Application in Mobile-Edge Computing(2021)23 cited
• → Cooperative Task Offloading in UAV Swarm-based Edge Computing(2021)14 cited
• → Research on Intelligent Mobile Edge Computing and Task Unloading Method of UAV(2023)4 cited