Network slicing offers numerous benefits, particularly the ability to deliver highly customizable services to new industry sectors that have been unserved or inadequately served by current mobile network operators. Among new industry use cases that are targeted by the fifth generation (5G) mobile systems, there exist scenarios that go beyond what the current device-centric mobility approaches can support. The mobility of low latency communication services, shared by a group of moving devices, e.g., autonomous vehicles that share sensor data, is a prime example of these cases. These use cases' demands for ultra-low latency can be addressed by leveraging the Multi-Access Edge Computing (MEC) concept, techniques for live migration of virtual resources, Software Defined Networking (SDN), and network slicing. In this paper, we define different slice mobility patterns, different methods for grouping users, and different triggers for network slice mobility. Furthermore, we evaluate the mobility of services and network slices based on the simultaneous migrations of multiple containers.

Network Slicing (NS) is a key enabler of the upcoming 5G system and beyond, leveraging on both Network Function Virtualization (NFV) and Software Defined Networking (SDN), NS will enable a flexible deployment of Network Functions (NFs) belonging to multiple Service Function Chains (SFC) over various administrative and technological domains. Our novel architecture addresses the complexities and heterogeneities of verticals targeted by 5G systems, whereby each slice consists of a set of SFCs, and each SFCs handling specified traffic within the slice. In this paper, we propose and evaluate a MILP optimization model to solve the complexities that arise from this new environment, our proposed model enables a cost-optimal deployment of network slices allowing a mobile network operator to efficiently allocate the underlying layer resources according to its users' requirements. We also design a greedy-based heuristic to investigate the possible trade-offs between execution runtime and network slice deployment. For each network slice, the proposed solution guarantees the required delay and the bandwidth, while efficiently handling the use of both the VNF nodes and the physical nodes, reducing the service provider Operating Expenditure (OPEX).

The need for faster and higher-capacity networks that can sustain modern, high-demanding applications has driven the development of 5G technology. Moreover, low-latency communication (1ms-10ms) is a key requirement of 5G systems. Multi-access Edge Computing (MEC) can be leveraged to attain the 5G objectives, since it allows the shift of part of services towards the vicinity of users, allowing the infrastructure to host various services closer to its end-users. Motivated by the evolution of real-time applications, we propose and evaluate two different mechanisms to improve the end-user experience by leveraging container-based live migration technologies. The first solution is aware of the users' mobility patterns, while the other is oblivious to the users' paths. Our results display a closer to 50% reduction on downtime, which shows the efficiency of the proposed solutions compared to prior works based on either a similar underlying technology, i.e., LXC or Docker.

Given the indispensable need for a reliable network architecture to cope with 5G networks, 3GPP introduced a covet technology dubbed 5G Service Based Architecture (5G-SBA). Meanwhile, Multi-access Edge Computing (MEC) combined with SBA conveys a better experience to end-users by bringing application hosting from centralized data centers down to the network edge, closer to consumers and the data generated by applications. Both the 3GPP and the ETSI proposals offered numerous benefits, particularly the ability to deliver highly customizable services. Nevertheless, compared to large datacenters that tolerate the hosting of standard virtualization technologies (Virtual Machines (VMs) and servers), MEC nodes are characterized by lower computational resources, thus the debut of lightweight micro-service based applications. Motivated by the deficiency of current micro-services-based applications to support users' mobility and assuming that all these issues are under the umbrella of Service Function Chain (SFC) migrations, we aim to introduce, explain and evaluate diverse SFC migration patterns. The obtained results demonstrate that there is no clear vanquisher, but selecting the right SFC migration pattern depends on users' motion, applications' requirements, and MEC nodes' resources.

Recent 5G trials have demonstrated the usefulness of the Network Slicing concept that delivers customizable services to new and under-serviced industry sectors. However, user mobility's impact on the optimal resource allocation within and between slices deserves more attention. Slices and their dedicated resources should be offered where the services are to be consumed to minimize network latency and associated overheads and costs. Different mobility patterns lead to different resource re-allocation triggers, leading eventually to slice mobility when enough resources are to be migrated. The selection of the proper triggers for resource re-allocation and related slice mobility patterns is challenging due to triggers' multiplicity and overlapping nature. In this paper, we investigate the applicability of two Deep Reinforcement Learning based algorithms for allowing a fine-grained selection of mobility triggers that may instantiate slice and resource mobility actions. While the first proposed algorithm relies on a value-based learning method, the second one exploits a hybrid approach to optimize the action selection process. We present an enhanced ETSI Network Function Virtualization edge computing architecture that incorporates the studied mechanisms to implement service and slice migration. We evaluate the proposed methods' efficiency in a simulated environment and compare their performance in terms of training stability, learning time, and scalability. Finally, we identify and quantify the applicability aspects of the respective approaches.