While the 5G network technology is maturing and the number of commercial deployments is growing, the focus of the networking community is shifting to services and service delivery. 5G networks are designed to be a common platform for very distinct services with different characteristics. Network Slicing has been developed to offer service isolation between the different network offerings. Cloud-native services that are composed of a set of inter-dependent micro-services are assigned into their respective slices that usually span multiple service areas, network domains, and multiple data centers. Due to mobility events caused by moving end-users, slices with their assigned resources and services need to be re-scoped and re-provisioned. This leads to slice mobility whereby a slice moves between service areas and whereby the inter-dependent service and resources must be migrated to reduce system overhead and to ensure low-communication latency by following end-user mobility patterns. Recent advances in computational hardware, Artificial Intelligence, and Machine Learning have attracted interest within the communication community to study and experiment self-managed network slices. However, migrating a service instance of a slice remains an open and challenging process, given the needed co-ordination between inter-cloud resources, the dynamics, and constraints of inter-data center networks. For this purpose, we introduce a Deep Reinforcement Learning based agent that is using two different algorithms to optimize bandwidth allocations as well as to adjust the network usage to minimize slice migration overhead. We show that this approach results in significantly improved Quality of Experience. To validate our approach, we evaluate the agent under different configurations and in real-world settings and present the results.

Even a wider set of highly critical and latency-sensitive applications with resource needs from the access network and the edge will be supported by the 6G networks. Therefore, the 6G network will deal with diversification of service platforms. Optimizing the resource consumption of network slicing on top of a shared infrastructure will become essential to keep the operating costs on an acceptable level. Each vertical, e.g., eMBBPlus, BigCom, holographic and tactile communications can run on top of network slice with specific KPIs. Different verticals can have contradicting requirements running on top of the same infrastructure. This paper investigates the orchestration of network services within a federated end-to-end network slice, which may span over multiple cloud domains as expected to be a common scenario in 6G deployments. We introduce three optimization solutions that consider two conflicting objectives, the end-to-end delay and service relocation, for orchestrating network slice. While the first solution optimizes the end-to-end delay, the second solution optimizes the service relocation. Meanwhile, the third solution leverages the bargaining game theory for achieving optimal Pareto fair trade-off configuration to optimize both objectives. The simulation results demonstrate the efficiency of the proposed solutions to achieve their main design goals

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.

5G system and beyond will build on the network slicing for offering high customizable services with different requirements that run on top of the same shared infrastructure. Each network slice, such as Ultra-Reliable Low latency Communications (URLLC) and Enhanced Mobile Broadband (eMBB), has different requirements that can be even contradicting from a slice to another. A network slice consists of a set of physical or virtual network functions (VNF/PNF) that have various capabilities and run across multiple administrative and cloud domains of different technology. A user can simultaneously request multiple services from different network slices. In this paper, we address the problem of initial placement and live migration of multiple mobile services across centralized and edge cloud by taking into account service types, network conditions and users' mobility features. As a solution to this problem, in this paper, we suggest and evaluate a solution that orchestrates the network services in a cost-efficient way, ensuring that each user could be simultaneously served by multiple slices while perceiving a high QoS and ensuring that the service level agreements (SLAs) of the consumed services are not violated.

The virtual resources of 5G networks are expected to scale and support migration to other locations within the substrate. In this context, a configuration for 5G network slices details the instantaneous mapping of the virtual resources across all slices on the substrate, and a feasible configuration satisfies the Service-Level Objectives (SLOs) without overloading the substrate. Reconfiguring a network from a given source configuration to the desired target configuration involves identifying an ordered sequence of feasible configurations from the source to the target. The proposed solutions for finding such a sequence are optimized for data centers and cannot be used as-is for reconfiguring 5G network slices. We present Matryoshka, our divide-and-conquer approach for finding a sequence of feasible configurations that can be used to reconfigure 5G network slices. Unlike previous approaches, Matryoshka also considers the bandwidth and latency constraints between the network functions of network slices. Evaluating Matryoshka required a dataset of pairs of source and target configurations. Because such a dataset is currently unavailable, we analyze proof of concept roll-outs, trends in standardization bodies, and research sources to compile an input dataset. On using Matryoshka on our dataset, we observe that it yields close-to-optimal reconfiguration sequences 10X faster than existing approaches.

Network Slicing (NS) is a key enabler of the upcoming 5G and beyond system, 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 SFC handles specific 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's Operating Expenditure (OPEX).

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 data- centers 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.

5G networks leverage network slices for serving use cases with different and potentially conflicting requirements. Current approaches for composing network slices largely overlook the presence of multiple ingress and egress nodes in each network slice. This results in inefficient resource usage even when co-locating the Virtual Network Functions (VNFs) of a slice. The network is thus quickly saturated, and no more use cases can be served. We address this issue by introducing μslices, where each μslice is tied to a specific pair of ingress and egress nodes of a slice. Then, we optimize the bandwidth consumed by each μslice by co-locating its VNF instances that communicate the most. We observed that enabling μslicing was able to save about two times more control and data plane traffic and it also resulted in a more even distribution of computational and link load.

The use cases of the upcoming 5G mobile networks introduce new and complex user demands that will require support for fast reconfiguration of network resources. Software Defined Network (SDN) is a key technology that can address these requirements, as it decouples the control plane from the data plane of the network devices and logically centralizes the control plane in the SDN controller. SDN network operating system (ONOS) is a state-of-art SDN controller that aims to address this important scalability limitation from its design. An important feature of ONOS is that it allows network administrators to configure and manage networks with a high-level of abstraction by using Intent specifications. An Intent is a policy expression describing what is the desired outcome rather than how the outcome should be reached. The concept of Intents coupled with the distributed storage space are the key components for the theoretical scalability of ONOS. In this paper, we present our evaluation of the ONOS Intent northbound interface using a methodology that takes into consideration the interface access method, type of Intent and number of installed Intents. Our preliminary analysis indicates a linear increase in the computational cost with regards to the number of submitted Intents, with the access method being a major factor in the overall computational cost.

Given the constantly growing demand for inter- data-center services that 5G networks are bringing, live migration has become a covet and very challenging technology. Meanwhile, the emergence of Software Defined Networking (SDN) and Network Function Virtualization (NFV) technologies has completely transformed modern networks by offering more flexibility and at the same time more complexity. So far, investigations have been confined to integrating the live migration process with SDN/NFV paradigms in order to ensure the desired Quality of Experience (QoE). However, the simple integration is not sufficient to handle unexpected cases such as resources' unavailability, networking issues, and system control. For this purpose, we present MIRA!, a novel framework for managing reliable live migrations of virtual resources across different Infrastructure as a Service (IaaS), handling unexpected cases, while ensuring high QoS and a very low downtime without human intervention using an SDN aware solution. To validate our proposed framework, we performed a set of experimental evaluations under different configurations. The obtained results of our proposed framework show a 21% time reduction compared to a prior work and an interesting behavior while modifying the number of allocated CPU cores.

Network Slicing (NS) is expected to be a key functionality of the upcoming 5G systems. Coupled with Software Defined Networking (SDN) and Network Function Virtualization (NFV), NS will enable a flexible deployment of Network Functions belonging to multiple Service Function Chains (SFC) over a shared infrastructure. To address the complexities that arise from this new environment, we formulate a MILP optimization model that enables a cost- optimal deployment of network slices, allowing a Mobile Network Operator to efficiently allocate the underlying layer resources according to the users' requirements. For each network slice, the proposed solution guarantees the required delay and the bandwidth, while efficiently handling the usage of underlying nodes, which leads to reduced cost. The obtained results show the efficiency of the proposed solution in terms of cost and execution time for small-scale networks, while it shows an interesting behavior in the optimization of the mapping of slices into underlay nodes of the large-scale topologies.

Content delivery networks (CDNs) have been widely implemented to provide scalable cloud services. Such networks support resource pooling by allowing virtual machines to be dynamically running or stopping according to current users' demands. Recently, there has been an increasing interest in Network Function Virtualization (NFV) as an emerging technology that aims to reduce cost, enable scalability and flexibility by decoupling network functions from the underlying hardware. In this regard, this paper designs a novel architecture to provide CDN Slices as a Service and that is across multiple administrative cloud domains. The architecture is aligned with the NFV Management and Orchestration (MANO) models. The proposed platform consists of three virtual network functions (VNFs), namely virtual caches, virtual video streamers, and virtual video transcoders. Regarding the latter, the paper also proposes a scheme for load balancing the transcoding tasks of the uploaded videos over a distributed network of virtual transcoders. In this article, an extensive benchmark analysis is conducted in order to study the virtual transcoding behavior in different cloud environments. The experiment evaluations provides a solid knowledge base to predict the estimated transcoding time for an optimal workload management of videos, aiming to optimize the incurred efficient cost in terms of delivery time and latency.

Many ongoing research activities relevant to 5G mobile systems concern the virtualization of the mobile core network, including the evolved packet core (EPC) elements, aiming for system scalability, elasticity, flexibility, and cost-efficiency. Virtual EPC (vEPC)/5G core will principally rely on some key technologies, such as network function virtualization, software defined networking, and cloud computing, enabling the concept of mobile carrier cloud. The key idea beneath this concept, also known as core network as a service, consists in deploying virtual instances (i.e., virtual machines or containers) of key core network functions [i.e., virtual network functions (VNF) of 4G or 5G], such as the mobility management entity (MME), Serving GateWay (SGW), Packet Data network gateWay (PGW), access and mobility management function (AMF), session management function (SMF), authentication server function (AUSF), and user plane functions, over a federated cloud. In this vein, an efficient VNF placement algorithm is highly needed to sustain the quality of service (QoS) while reducing the deployment cost. Our contribution in this paper is twofold. First, we devise an algorithm that derives the optimal number of virtual instances of 4G (MME, SGW, and PGW) or 5G (AMF, SMF, and AUSF) core network elements to meet the requirements of a specific mobile traffic. Second, we propose an algorithm for the placement of these virtual instances over a federated cloud. While the first algorithm is based on mixed integer linear programming, the second is based on coalition formation game, wherein the aim is to build coalitions of cloud networks to host the virtual instances of the vEPC/5G core elements. The obtained results clearly indicate the advantages of the proposed algorithms in ensuring QoS given a fixed cost for vEPC/5G core deployment, while maximizing the profits of cloud operators.

This paper introduces a content delivery network as a service (CDNaaS) platform that allows dynamic deployment and life-cycle management of virtual content delivery network (CDN) slices running across multiple administrative cloud domains. The CDN slice consists of four virtual network function (VNF) types, namely virtual transcoders, virtual streamers, virtual caches, and a CDN-slice-specific Coordinator for the management of the slice resources across the involved cloud domains. To create an efficient CDN slice, the optimal placement of its composing VNFs using adequate amount of virtual resources for each VNF is of vital importance. In this vein, this paper devises mechanisms for allocating an appropriate set of VNFs for each CDN slice to meet its performance requirements and minimize as much as possible the incurred cost in terms of allocated virtual resources. A mathematical model is developed to evaluate the performance of the proposed mechanisms. We first formulate the VNF placement problem as two Linear Integer problem models, aiming at minimizing the cost and maximizing the quality of experience (QoE) of the virtual streaming service. By applying the bargaining game theory, we ensure an optimal tradeoff solution between the cost efficiency and QoE. Extensive simulations are conducted to evaluate the effectiveness of the proposed models in achieving their design objectives and encouraging results are obtained.

To support the much desired ultra-short latency of 5G mobile systems, many micro-data centers will be deployed in the vicinity of mobile users, defining a distributed edge cloud. Over this edge cloud, it is important to create optimal network slices to support different 5G verticals. Optimality is defined in terms of cost efficiency and QoS support. Therefore, it is important to understand the behavior of mobile users in terms of mobile service consumption. In this paper, we present, on one hand, a tool for developing a spatio-temporal model of mobile service usage over a particular geographical area. This tool will help to define the behavior of mobile users in terms of mobility patterns and mobile service consumption. On the other hand, based on this tool, we present a benchmark of some interesting Virtualized Network Functions (VNF) placement algorithms, among them our enhanced version of the predictive placement strategy. The comparison is based on data overload, overload of Virtual Machines (VMs) and QoS.

Extending cloud infrastructure to the Network Edge represents a breakthrough to support delay-sensitive applications in next 5G cellular systems. In this context, to enable ultrashort response times, fast relocation of service instances between edge nodes is required to cope with user mobility. To face this issue, proactive service replication is considered a promising strategy to reduce the overall migration time and to guarantee the desired Quality of Experience (QoE). On the other hand, the provisioning of replicas over multiple edge nodes increases the resource consumption of constrained edge nodes and the relevant deployment cost. Given the two conflicting objectives, in this paper we investigate different optimization models for proactive service migration at the Network Edge, which can exploit prediction of user mobility patterns. In particular, we define two Integer Linear Problem optimization schemes, which aim at respectively minimizing the QoE degradation due to service migration, and the cost of replicas' deployment. Performance evaluation shows the effectiveness of our proposed solutions.

Edge Cloud infrastructure will play a key role in extending the range of supported real-time cloud applications, by guaranteeing extremely fast response times. However, user mobility requires fast relocation of service instances, which represents an open challenge for resource-constrained cloudlets interconnected by high-latency and low-bandwidth links. In this paper, we investigate container-based virtualization techniques to support dynamic Mobile Edge Computing (MEC) environments. In particular, we design a framework to guarantee fast response time, by proactively exploiting service replication. A preliminary performance analysis is conducted to identify the possible advantages introduced by the proposed approach compared to classic migration procedures.

Mobile networks are expected to serve a wide range of verticals, however the Long Term Evolution (LTE) network is optimized for basic mobile operator services only. The network functions serving LTE networks are largely implemented as dedicated single function devices that offer poor customization options. This intrinsic inflexibility makes current LTE networks unable to meet the requirements of future mobile networks. For example, LTE networks experience signaling storms because the signals exchanged by the network functions cannot be optimized according to the current usage pattern of mobile services. Modularizing these network functions would enable a refactoring of the LTE network, allowing operators to compose networks that adapt and evolve with the influx of verticals. In this article, we present a new approach for refactoring the network functions serving LTE networks which can be leveraged to compose a modular mobile network optimized for the verticals using its services. As an example, we demonstrate that deploying network functions at the edge significantly reduces the signals exchanged within a mobile network.

Content Delivery Networks (CDNs) emerged to manage the great amount of content, as well as the transmissions over long distances. In recent years, this concept proves to be a promising solution for emergent enterprises. In this paper, we present a Content Delivery Network as a Service (CDNaaS) platform which can create virtual machines (VMs) through a network of data centers and provide a customized slice of CDN to users. CDNaaS manages a great number of videos by means of caches, transcoders, and streamers hosted in different VMs. However, an optimal placement of VMs with adequate flavors for the different images is required to obtain an efficient slice of CDN. In this work, we argue the need to find a convenient slice for the CDN owner while respecting his performance requirements and minimizing as much as possible the incurred cost. We first formulate the VMs placement problem as two Linear Integer problem solutions, aiming at minimizing the cost and maximizing the quality of experience of streaming. Then, extensive simulation results are presented to illustrate the effectiveness of the proposed models.

Wi-Fi networks were one of the first use-cases for Software-defined networking (SDN). However, to deploy a software-defined Wi-Fi network today, one has to rely on research prototypes with availability, documentation, hardware requirements, and scalability issues. To alleviate this situation, we demonstrate two simple techniques to bring SDN functionality to existing Wi-Fi networks and discuss their benefits and short-comings. Researchers can use our techniques to convert their existing Wi-Fi testbeds into software defined Wi-Fi testbeds. Our two techniques thus significantly lower the barrier-to-entry for deploying software-defined Wi-Fi networks.

This demonstration presents a novel software defined platform for achieving collaborative and energy-aware WiFi offloading. The platform consists of an extensible central controller, programmable offloading agents, and offloading extensions on mobile devices. Driven by our extensive measurements of energy consumption on smartphones, we propose an effective energy-aware offloading algorithm and integrate it to our platform. By enabling collaboration between wireless networks and mobile users, our solution can make optimal offloading decisions that improve offloading efficiency for network operators and achieve energy saving for mobile users. To enhance deployability, we have released our platform under open-source licenses on GitHub.

Privacy when using IP communication has become an important problem. It is a challenging problem that spans multiple layers in the OSI reference model with various constant identifiers that can be profiled. This leads to user privacy concerns. Location Privacy, on the other hand, is primarily concerned with revealing a user’s identity as a function of mobility. When a user visits a new network and uses a fixed identifier, such as a Mobile IPv6 Home Address for communication, on-lookers can determine that the user has roamed. We address this problem of location privacy and provide a Privacy-Tag for use in place of a fixed identifier to protect user privacy. Our method is readily implementable using the Mobile IPv6 Return Routability [2] protocol and the Binding Update procedure.