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Mobile Edge Cloud (MEC) technology is envisioned to play a key role in next generation mobile networks by supporting low-latency applications using geographically distributed local cloud clusters. However, MEC faces challenges of resource assignment and load balancing to support user mobility and latency-sensitive applications. Virtualized resource reallocation techniques including dynamic service migration are evolving to achieve load balance, fault tolerance and system maintenance objectives for resource constrained edge nodes. In this work, a compute and network-aware lightweight resource sharing framework with dynamic container migration, ShareOn, is proposed. The migration framework is validated using a set of heterogeneous edge cloud nodes distributed in San Francisco city, serving mobile taxicab users across that region. The end-to-end system is implemented using a container hypervisor called LXD (Linux Container Hypervisor) executing a real-time application to detect license number plates in automobiles. The system is evaluated based on key metrics associated with application quality-of-service (QoS) and network efficiency such as the average system response time and the migration cost for different combinations of load, compute resources, inter-edge cloud bandwidth, network and user latency. A detailed migration cost analysis enables evaluation of migration strategies to improve ShareOn's performance in comparison to alternative migration techniques, achieving a gain of 15-22% in system response time for highly loaded edge cloud nodes.INDEX TERMS Quality of experience (QoE), container migration, mobile edge computing, virtualization, real-time applications, edge cloud, migration cost, inter-edge cloud bandwidth. SHALINI CHOUDHURY received the B.Tech. degree in electrical engineering from Nagpur University, India, in 2011, and the M.S. degree from CUNY, The City College of New York, USA, in 2016. She is currently pursuing the Ph.D. degree in electrical and computer engineering with Rutgers University, New Jersey, USA. She has previously worked with the Interdigital's Research and Innovation Team and the Accenture Laboratory. Her research interests include wireless communications and networking specifically low latency mobility support for emerging 5G and beyond networks.

Private 5G networks will soon be ubiquitous across the future-generation smart wireless access infrastructures hosting a wide range of performance-critical applications. A highperforming User Plane Function (UPF) in the data plane is critical to achieving such stringent performance goals, as it governs fast packet processing and supports several key controlplane operations. Based on a private 5G prototype implementation and analysis, it is imperative to perform dynamic resource management and orchestration at the UPF. This paper leverages Mobile Edge Cloud-Intelligent Agent (MEC-IA), a logically centralized entity that proactively distributes resources at UPF for various service types, significantly reducing the tail latency experienced by the user requests while maximizing resource utilization. Extending the MEC-IA functionality to MEC layers further incurs data plane latency reduction. Based on our extensive simulations, under skewed uRLLC traffic arrival, the MEC-IA assisted bestfit UPF-MEC scheme reduces the worstcase latency of UE requests by up to 77.8% w.r.t. baseline. Additionally, the system can increase uRLLC connectivity gain by 2.40× while obtaining 40% CapEx savings.

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