In the fifth generation (5G) mobile networks, the number of user-plane gateways has increased, and, in contrast to previous generations they can be deployed in a decentralized way and auto-scaled independently from their control-plane functions. Moreover, the performance of the user-plane gateways can be boosted with the adoption of advanced acceleration techniques such as Vector Packet Processing (VPP). However, the increased number of user-plane gateways has also made load balancing a necessity, something we find has so far received little attention. Moreover, the introduction of VPP poses a challenge to the design of the auto-scaling of user-plane gateways. In this paper, we address these two challenges by proposing a novel performance indicator for making better auto-scaling decisions, and by proposing three new dynamic load-balancing algorithms for the user plane of a VPP-based, softwarized 5G network. The novel performance indicator is estimated based on the VPP vector rate and is used as a threshold for the auto-scaling process. The dynamic load-balancing algorithms take into account the number of bearers allocated for each user-plane gateway and their VPP vector rate. We validate and evaluate our proposed solution in a 5G testbed. Our experiment results show that the scaling helps to reduce the packet latency for the user-plane traffic, and that our proposed load-balancing algorithms can give a better distribution of traffic load as compared to traditional static algorithms.

In this paper, we aim at tackling the scalability problem of the Mobility Management Entity (MME), which plays a crucial role of handling control plane traffic in the current 4G Evolved Packet Core as well as the next generation mobile core, 5G. One of the solutions to this problem is to virtualize the MME by applying Network Function Virtualization principles and then deploy it as a cluster of multiple virtual MME instances (vMMEs) with a front-end load balancer. Although several designs have been proposed, most of them assume the use of simple algorithms such as random and round-robin to balance the incoming traffic without any performance assessment. To this end, we implemented a weighted round robin algorithm which takes into account the heterogeneity of resources such as the capacity of vMMEs. We compare this algorithm with a random and a round-robin algorithm under two different system settings. Experimental results suggest that carefully selected load balancing algorithms can significantly reduce the control plane latency as compared to simple random or round-robin schemes.

With the introduction of combined positron emission tomography (PET)/computed tomography (CT) or PET/magnetic resonance imaging (MRI) scanners, there is an increasing emphasis on reconstructing PET images with the aid of the anatomical side information obtained from X-ray CT or MRI scanners. In this paper, we propose a new approach to incorporating prior anatomical information into PET reconstruction using the nonlocal regularization method. The nonlocal regularizer developed for this application is designed to selectively consider the anatomical information only when it is reliable. As our proposed nonlocal regularization method does not directly use anatomical edges or boundaries which are often used in conventional methods, it is not only free from additional processes to extract anatomical boundaries or segmented regions, but also more robust to the signal mismatch problem that is caused by the indirect relationship between the PET image and the anatomical image. We perform simulations with digital phantoms. According to our experimental results, compared to the conventional method based on the traditional local regularization method, our nonlocal regularization method performs well even with the imperfect prior anatomical information or in the presence of signal mismatch between the PET image and the anatomical image.