Security Challenges of Small Cell as a Service in Virtualized Mobile Edge Computing Environments

It is well acknowledged that one of the key enabling factors for the realization of future 5G networks will be the small cell (SC) technology. Furthermore, recent advances in the fields of network functions virtualization (NFV) and software-defined networking (SDN) open up the possibility of deploying advanced services at the network edge. In the context of mobile/cellular networks this is referred to as mobile edge computing (MEC). Within the scope of the EU-funded research project SESAME we perform a comprehensive security modelling of MEC-assisted quality-of-experience (QoE) enhancement of fast moving users in a virtualized SC wireless network, and demonstrate it through a representative scenario toward 5G. Our modelling and analysis is based on a formal security requirements engineering methodology called Secure Tropos which has been extended to support MEC-based SC networks. In the proposed model, critical resources which need protection, and potential security threats are identified. Furthermore, we identify appropriate security constraints and suitable security mechanisms for 5G networks. Thus, we reveal that existing security mechanisms need adaptation to face emerging security threats in 5G networks.

The increasing demand in Mobile Edge Computing (MEC) networks has led to the emergence of Network Function Virtualization (NFV) technology. Through the virtualization technique, NFV can decouple each Virtual Network Function (VNF) from the hardware and deploy it flexibly in the MEC. Each service request in NFV assembles a Service Function Chain (SFC) by tying different VNFs and processes the traffic in the chained order. How to allocate resources and deploy SFC in MEC to meet Quality of Service (QoS) requirements is an important challenge for NFV. An increase in the number of VNFs with traffic passing one after the other causes high delay in SFC assembly. Also, the demanded resources may change during SFC assembly, which is clearly neglected in existing works. Therefore, high delay and resource demand uncertainty during SFC assembly affect QoS. With this motivation, we solve the SFC deployment problem by Parallelizing VNFs under Resource Demand Uncertainty (PVRDU) in MEC. We use the dependency between VNFs to transform the original SFC into a parallel SFC and then assemble several sub-SFCs based on a Deep Reinforcement Learning (DRL) approach. Meanwhile, PVRDU uses a Markov-based approximation algorithm to handle resource demand uncertainty. We perform extensive trace-driven simulations to verify the effectiveness of the proposed algorithm. The evaluation results of PVRDU are promising in different aspects compared to state-of-the-art methods. Specifically, for the considered scenario, DPC reduced SFC delay by as much as 8.7%.

With the emergence of Network Function Virtualization (NFV) and Software-Defined Networks (SDN), Service Function Chaining (SFC) has evolved into a popular paradigm for carrying and fulfilling network services. However, the implementation of Mobile Edge Computing (MEC) in sixth-generation (6G) mobile networks requires efficient resource allocation mechanisms to migrate virtual network functions (VNFs). Deep learning is a promising approach to address this problem. Currently, research on VNF migration mainly focuses on how to migrate a single VNF while ignoring the VNF sharing and concurrent migration. Moreover, most existing VNF migration algorithms are complex, unscalable, and time-inefficient. This paper assumes that each placed VNF can serve multiple SFCs. We focus on selecting the best migration location for concurrently migrating VNF instances based on actual network conditions. First, we formulate the VNF migration problem as an optimization model whose goal is to minimize the end-to-end delay of all influenced SFCs while guaranteeing network load balance after migration. Next, we design a Deep Learning-based Two-Stage Algorithm (DLTSA) to solve the VNF migration problem. Finally, we combine previous experimental data to generate realistic VNF traffic patterns and evaluate the algorithm. Simulation results show that the SFC delay after migration calculated by DLTSA is close to the optimal results and much lower than the benchmarks. In addition, it effectively guarantees the load balancing of the network after migration.

The Mobile Edge Computing (MEC) paradigm emerges as a promising technology to provide services for various mobile applications at edges of core networks while meeting stringent service delay requirements of users. Orthogonal with the MEC, Network Function Virtualization (NFV) provides the network resource management with flexibility and scalability, where Virtual Network Functions (VNFs) are deployed over edge servers in a chained manner as Service Function Chains (SFCs) for enabling service applications. Provisioning reliable SFC-enabled services in MEC thus is fundamentally important. However, the VNF instances deployed usually are not reliable and affected by multiple factors, including the software implementation, the request execution duration, and so on. Empowered by digital twin techniques that can maintain the states of VNF instances by digital twins in a real-time manner and predict the reliability of VNF instances in edge servers, in this paper we study SFC-enabled reliable service provisioning in an MEC network by exploring the dynamics of VNF instance placement reliability. We first formulate a novel SFC-enabled reliable service problem in MEC networks - the online throughput maximization problem, and show its NP-hardness. We then propose an Integer Linear Programming (ILP) solution to the offline version of the problem, and develop an online algorithm with a provable competitive ratio for the problem. We finally evaluated the performance of the proposed algorithm through experimental simulations, and the results demonstrate that the proposed algorithm is promising.

Emerging 5G cellular networks are expected to face a dramatic increase in the volume of mobile traffic and IoT user requests due to the massive growth in mobile devices and the emergence of new compute-intensive applications. Running high-intensive compute applications on resource-constrained mobile devices has recently become a major concern, given the constraints of finite computation and limited storage capacities. Mobile Edge Computing (MEC) has recently become the key technology to overcome these issues by providing cloud computing capabilities and placing IT infrastructures at the mobile network edge. In this survey, we present a list of relevant research papers for the MEC infrastructure implementation phases, including (1) MEC infrastructure designing and dimensioning, (2) MEC infrastructure virtualization using Network Function Virtualization (NFV) concept, and the use of virtualized service placement and auto-scaling methods to deploy an agile system framework, (3) MEC resource management frameworks, and (4) approaches used to optimize the MEC resources on the physical infrastructure. The main focus of this survey is to determine the required aspects to implement an auto-scaled and proactive MEC-NFV infrastructure to support a dynamic and heterogenous mobile users’ demand at mobile network operators.

In future 6G networks, mobile edge computing (MEC) is envisioned to offer integrated computing, communication, and storage services at the network edge, enhancing both computational efficiency and communication quality. However, most existing MEC designs neglect the impact of jamming attacks, especially those from intelligent and adaptive jammers. To fill this important research gap, this paper investigates a jamming-resilient MEC framework that aims to improve communication reliability and reduce system delay under adversarial interference. Leveraging the emerging movable antenna (MA) technology, which allows dynamic adjustment of antenna positions and orientations, we propose a novel MA-assisted anti-jamming MEC architecture. Unlike existing works, our model explicitly considers the movement delay caused by MA, which is critical for practical deployment. We jointly optimize the MA positions at both the user equipment (UE) and the base station (BS), BS transmit beamforming, and task offloading ratios to minimize the total system delay. The resulting optimization problem is non-convex and highly coupled. Thus, we develop an efficient algorithm based on penalty dual decomposition (PDD) and successive convex approximation (SCA). Simulation results demonstrate that the proposed scheme significantly outperforms traditional fixed-position antenna (FPA) baselines in terms of jamming resilience and delay minimization, offering new insights into robust MEC system design for 6G networks.

In this chapter, we present a comprehensive end-to-end (E2E) network slicing framework for fifth-generation (5G) wireless and core networks and provide our vision on network slicing for beyond 5G (B5G) networks using artificial intelligence (AI) technologies. We first give an introduction on the 5G communication networks by specifying distinct 5G networking characteristics. As the key features of 5G pose challenges on quality-of-service (QoS)-oriented resource provisioning, a new concept of network slicing is presented, based on software-defined networking (SDN) and network function virtualization (NFV) technologies, to enhance the overall resource utilization while guaranteeing QoS isolation among services. We focus on developing specific network slicing solutions for both 5G wireless networks and core networks. In addition, the evolution of B5G networks is discussed in terms of a potential networking paradigm shift with new service key performance indicators (KPIs). To satisfy B5G service requirements, a framework of AI-assisted network slicing lifecycle is developed to automate the slice creation with reduced slice management complexity. A case study is presented to demonstrate the effectiveness of the proposed E2E network slicing solutions.Keywords5G communication networksNetwork slicingWireless networksRadio resource slicingCore networksComputing and transmission resource slicingBeyond 5G networksAI-assisted network slicingQoSE2E delay

5G networks control new technical concepts like ultra-low latency, ultra-high bandwidth, ultra-reliability, ultra-massive device access and manage the continually rising demands of diverse applications. The architecture and hierarchical framework of 5G networks need to understand and introduced. Security has been a primary focus for many telecommunications businesses since risks might have serious consequences for their front-line applications. A new class of security issues will be raised by network softwarization and new technologies including software-defined networking, network function virtualization, mobile-access edge computing and network slicing. This paper emphasizes the security concerns that 5G will raise and demands urgent security fixes. We also discuss the future of safe 5G systems and security fixes for these problems. To increase network security, DDOS attacks are detected and mitigated using a variety of SDN approaches.

5G networks represent a paradigm shift from their 4G predecessors, engineered to cater to the burgeoning demands of densely populated areas and the high data throughput requirements of the Internet of Things (IoT) and smart city infrastructures. Beyond enhancing fundamental network attributes such as reliability, availability, and security, 5G introduces architectural innovations to accommodate these advanced needs. This paper delves into the architectural nuances of 5G, focusing primarily on the Standalone (SA) deployment method, distinguished by its utilization of network slicing. This approach leverages cutting-edge virtual network technologies, including Software-Defined Networking (SDN) and Network Function Virtualization (NFV), to forge virtual network slices tailored to diverse consumer needs. Such customization is imperative for meeting the expansive and varied connectivity demands characteristic of 5G networks.To navigate the complexities of 5G architecture, this study advocates for the adoption of Reference Architectures (RAs), which provide a high-level abstraction framework for dissecting and understanding intricate systems. By proposing a model that outlines the standalone architecture of 5G networks, our research lays the groundwork for evolving this model into a comprehensive RA for 5G. This foundational work is instrumental in equipping system designers with the tools necessary for generalizing systems to manage design and deployment challenges effectively, as well as the heterogeneity inherent in system components. Additionally, our proposed model facilitates the establishment of a common vocabulary, promoting a coherent understanding of component interactions and offering a unified system perspective. Ultimately, this paper contributes a pivotal model that not only enhances our comprehension of 5G network architecture but also paves the way for future advancements in network design and deployment, promising to significantly impact the development of smart cities and IoT ecosystems.Keywords: 5G, 5G architecture, Reference architecture, patterns. Network slicing, Network Function Virtualization, Virtual Network Function.

The appearance of Network Functions Virtualization (NFV) has provided a revolution in various network-based applications owing to its different advantages like manageability, flexibility, security, and scalability. The users of NFV are provided with a framework that supplies different flexible network services in a dynamic way via the software-based virtualization of network functions in a single infrastructure. Nevertheless, NFV confront various challenges of security which make it vulnerable to several cybersecurity threats. In this paper, a review of NFV has been provided by introducing many related works, discussing serious and potential security attacks on the NFV, and presenting the efficient countermeasures for mitigating these attacks. Finally, several practical solutions are suggested for providing a reliable platform for NFV

The Network Function Virtualization paradigm enables the possibility to dynamically instantiate Virtual Network Functions (VNFs) in Commercial-Off-The-Shelf (COTS) hardware. Such VNFs are then concatenated together in Service Chains (SCs) to provide specific Internet services to the users. Depending on latency requirements for such services and considering the aim of maximally consolidating the VNFs (i.e., of minimizing the COTS hardware), the VNFs can be centralized in few datacenters in the core network or they can be distributed closer to the edge of the network. In this paper we evaluate the impact of latency requirements of SCs on VNF distribution towards the edge of the network, by also showing the benefits of a Fixed and Mobile Convergent (FMC) metro/access network, with respect to a non-convergent network, in terms of consolidation.

The core challenges in the actual SESAME EU-funded project is to develop an ecosystem to sustain network infrastructure openness, built on the pillars of network functions virtualization (NFV), mobile-edge computing (MEC) capabilities and cognitive network management that will provide multi-tenancy and flexible cloud-network interaction with highly-predictable and flexible end-to-end performance characteristics. Based on this aspect, we discuss the potential benefits of including NFV and MEC in a modern mobile communications infrastructure, through Small Cells coordination and virtualization, also focused upon realistic 5G-oriented considerations. Within the proposed SESAME architecture, we also assess the various advantages coming from a more enhanced network operation and management of resources, as it appears with the incorporation of cognitive capabilities embracing knowledge and intelligence.

Wireless networks in next generation will provide users ubiquitous computing services with low delay by devices at the network edge, namely mobile edge computing (MEC). The intensive computation tasks can be partially offloaded to the MEC server via the wireless link and then processed through the MEC computation resources to cater for the delay demand. A parallel computation process is formed in the MEC network consists of local computation at MEC users (MUs) and MEC computation at MEC servers. However, the fluctuating wireless channel environment, changeable spatial distribution of MUs and the randomness of MEC servers’ locations make it hard to characterize and guarantee the end-to-end quality of service requirements. In this work, we are devoted to analyze and optimize the overall delay bound for MEC networks under two orthogonal frequency division multiple access (OFDMA) strategies via stochastic network calculus (SNC). Specifically, Poisson cluster process is utilized to capture the randomness of MEC servers’ and users’ spatial locations and to derive the Laplace transform of interference suffered by an MU of interest. The upper bounds for the delay violation probability of two OFDMA strategies are established by exploiting SNC with the Mellin transform of signal-to-interference ratio. Furthermore, we propose an optimal task offloading scheme by minimizing the overall delay, which balances the local computation delay and MEC delay.

Mobile edge computing (MEC) effectively integrates wireless network and Internet technologies and adds computing, storage, and processing functions to the edge of cellular networks. This new network architecture model can deliver services directly from the cloud to the very edge of the network while providing the best efficiency in mobile networks. However, due to the dynamic, open, and collaborative nature of MEC network environments, network security issues have become increasingly complex. Devices cannot easily ensure obtaining satisfactory and safe services because of the numerous, dynamic, and collaborative character of MEC devices and the lack of trust between devices. The trusted cooperative mechanism can help solve this problem. In this paper, we analyze the MEC network structure and device-to-device (D2D) trusted cooperative mechanism and their challenging issues and then discuss and compare different ways to establish the D2D trusted cooperative relationship in MEC, such as social trust, reputation, authentication techniques, and intrusion detection. All these ways focus on enhancing the efficiency, stability, and security of MEC services in presenting trustworthy services.

Various real-time, latency-sensitive, and high-speed mobile applications are evolving as 5G applications. These applications are realized using Multi-access Edge Computing (MEC) and Network Function Virtualization (NFV) technologies in the 5G system. MEC platform, MEC service, and MEC application are the main components of an MEC Framework. NFV Orchestrator, Virtualized Infrastructure Manager (VIM), and virtualization technologies such as Virtual Machines (VM) and Containers are the main pillars of the NFV technology. In this paper, we study the impact of the virtualization technologies in the deployment of the MEC framework and its components while. We also study the impact of virtualization technologies on NFV and MEC KPIs such as onboarding time, instantiation time, MEC service and application response times. The experiments and its analysis show that containers perform better than VM to instantiate/terminate MEC components in the NFV framework. The observed MEC service KPIs show that the edge application’s performance will be improved to meet the QoE of the applications irrespective of the virtualization technology used. These results can be used as a reference while deploying the MEC components based on their granular functionalities.