Dynamic 5G Network Slicing (original) (raw)

Network Slicing in 5G Systems: Challenges, Opportunities and Implementation Approaches

IJIRIS:: AM Publications,India, 2024

The introduction of the fifth-generation (5G) of network technology has radically transformed the telecommunications landscape by providing high-speed, low-latency communication suitable for a range of innovative applications. However, this transformation also introduces novel network complexity and resource management challenges. An emerging solution to these formidable challenges is 'Network Slicing,' a powerful technology that plays a crux role in the efficient management of 5G network systems. Network slicing, enabled by key technologies such as Software-Defined Networking (SDN) and Network Function Virtualization (NFV), allows the creation of multiple virtual and independent networks operating on a shared physical infrastructure. This ability contributes to a more flexible, scalable, and efficient network system, making it aptly suited for diverse 5G applications. In this paper, we conduct an indepth examination of network slicing in 5G systems, its implementation strategies, associated challenges, and potential solutions. Two real-world case studies underline its practical applications, while a discussion on the future outlook anticipates advances in AI and ML to refine network slicing management. The paper posits that while network slicing brings its own set of complexities, its continuous evolution and relentless innovations gear towards overcoming such challenges, paving the way to a future of 5G networking marked by versatility, reliability, and efficiency of unprecedented levels.

Dynamic Network Slice Scaling Assisted by Attention-Based Prediction in 5G Core Network

IEEE Access, 2022

Network slicing is a key technology in fifth-generation (5G) networks that allows network operators to create multiple logical networks over a shared physical infrastructure to meet the requirements of diverse use cases. Among core functions to implement network slicing, resource management and scaling are difficult challenges. Network operators must ensure the Service Level Agreement (SLA) requirements for latency, bandwidth, resources, etc for each network slice while utilizing the limited resources efficiently, i.e., optimal resource assignment and dynamic resource scaling for each network slice. Existing resource scaling approaches can be classified into reactive and proactive types. The former makes a resource scaling decision when the resource usage of virtual network functions (VNFs) exceeds a predefined threshold, and the latter forecasts the future resource usage of VNFs in network slices by utilizing classical statistical models or deep learning models. However, both have a trade-off between assurance and efficiency. For instance, the lower threshold in the reactive approach or more marginal prediction in the proactive approach can meet the requirements more certainly, but it may cause unnecessary resource wastage. To overcome the tradeoff, we first propose a novel and efficient proactive resource forecasting algorithm. The proposed algorithm introduces an attention-based encoder-decoder model for multivariate time series forecasting to achieve high short-term and long-term prediction accuracies. It helps network slices be scaled up and down effectively and reduces the costs of SLA violations and resource overprovisioning. Using the attention mechanism, the model attends to every hidden state of the sequential input at every time step to select the most important time steps affecting the prediction results. We also designed an automated resource configuration mechanism responsible for monitoring resources and automatically adding or removing VNF instances of network slices, which helps network operators satisfy service requirements even when the traffic of end-user requests changes dynamically. Comprehensive experiments demonstrate that our proposed solution outperforms other solutions in terms of short-term and long-term predictions while reducing the cost of SLA violations and resource overprovisioning and enhancing the delay quality of network slices.

An Efficient Resource Management Mechanism for Network Slicing in a LTE Network

IEEE Access

The proliferation of mobile devices and user applications has continued to contribute to the humongous volume of data traffic in cellular networks. To surmount this challenge, service and resource providers are looking for alternative mechanisms that can successfully facilitate managing network resources in a more dynamic, predictive and distributed manner. New concepts of network architectures such as Software Defined Network (SDN) and Network Function Virtualization (NFV) have paved the way to move from static to flexible networks. They make networks more flexible (i.e. network providers capable of on-demand provisioning), easily customizable and cost effective. In this regard, network slicing is emerging as a new technology built on the concepts of SDN and NFV. It splits a network infrastructure into isolated virtual networks and allows them to manage resources allocation individually based on their requirements and characteristics. Most of the existing solutions for network slicing are computationally expensive because of the length of time they require to estimate the resources required for each isolated slice. In addition, there is no guarantee that the resource allocation is fairly shared among users in a slice. In this paper, we propose a Network Slicing Resource Management (NSRM) mechanism to assign the required resources for each slice in an LTE network, taking into consideration isolation of resources among different slices. In addition, NSRM aims to ensure isolation and fair sharing of distributed bandwidths between users belonging to the same slice. In NSRM, depending on requirements, each slice can be customized (e.g. each can have a different scheduling policy).

A Comprehensive View of State-of-Art of 5G Network Slicing

Journal of network and information security, 2020

Network Slicing is a concept that creates multiple virtual networks that serve the purpose of various service requirements. These logical networks created on top of the same physical network infrastructure are called "network slices". Each slice of the network acts as an isolated network that is end-to-end and customized to achieve the requirements as expected by the application. This network slicing is one of the driving aspects in the 5G networks, which promises to provide various services as per the user requirement. A study is made on Network Function Virtualization (NFV) and Software Defined Networks (SDN) which forms the driving aspects for network Slicing in 5G networks. Also, the state of art developments in the field of network slicing has been studied and explained. The paper presents the benefits of 5G network slicing from the technical point of view and later describes different vertical segments that make use of slicing of 5G networks. It can be stated that network slicing in 5G networks offers to improve the efficiency of the 5G networks and also helps to achieve the expected and promised performance of the 5G in the coming future.

Predictive Resource Allocation in 5G Network Slicing: Leveraging SARIMAX Time Series Models with AI

Doctoral Symposium on Electronics, Telecommunications, & Information Technology, 2023

This paper addresses the need for enhanced Network Slice (NS) Management, within the Cloud Network Functions of 5G Core (5GC). Given that the Network Repository Function (NRF) acts like a central hub for registration and discovery services, some level of slicing information is processed, but the slicing management is rather traditional, not being entirely able to ensure dynamic resource adaptation. Therefore, the solution proposed leverages the power of artificial intelligence (AI) and Machine Learning Techniques to support better resource allocation, to improve the quality of service (QoS) and to reduce the CAPEX/OPEX for Mobile Network Operators (MNOs). The solution involves adding an additional component which acts like an integrated slicing manager, taking decisions based on AI predictions, more specifically, based on an AI model which processes and filters the training data. Results demonstrated majorly improved network responsiveness, decreased latency end enhanced packet loss percentage. Emergent technologies, such as AI, Machine Learning, Neuronal Networks, Edge and Cloud Computing, DevOps instruments, Continuous Integration, Continuous Development (CI/CD) algorithms or other solutions, could be the start of unlocking new opportunities for mobile network communications, especially for 5G/6G development, which governs over a self-healing and self-optimising environment.

Optimization of the implementation of network slicing in 5G RAN

2018 IEEE Middle East and North Africa Communications Conference (MENACOMM)

Network slicing enables an infrastructure provider (InP) to support heterogeneous 5G services over a common platform (i.e., by creating a customized slice for each service). Once in operation, slices can be dynamically scaled up/down to match the variation of their service requirements. An InP generates revenue by accepting a slice request. If a slice cannot be scaled up when required, an InP has to also pay a penalty (proportional to the level of service degradation). It becomes then crucial for an InP to decide which slice requests should be accepted/rejected in order to increase its net profit. This paper presents a slice admission strategy based on reinforcement learning (RL) in the presence of services with different priorities. The use case considered is a 5G flexible radio access network (RAN), where slices of different mobile service providers are virtualized over the same RAN infrastructure. The proposed policy learns which are the services with the potential to bring high profit (i.e., high revenue with low degradation penalty), and hence should be accepted. The performance of the RL-based admission policy is compared against two deterministic heuristics. Results show that in the considered scenario, the proposed strategy outperforms the benchmark heuristics by at least 55%. Moreover, this paper shows how the policy is able to adapt to different conditions in terms of: (i) slice degradation penalty vs. slice revenue factors, and (ii) proportion of high vs. low priority services.

Enhancing 5G network performance through effective resource management with network slicing

International Journal of Electrical and Computer Engineering (IJECE), 2024

The immense growth of mobile networks leads to versatile applications and new demands. The improved concert, transferability, flexibility, and performance of innovative network services are applied in diversified fields. More unique networking concepts are incorporated into state-of-the-art mobile technologies to expand these dynamic features further. This paper presents a novel system architecture of slicing and pairing networks with intra-layer and inter-layer functionalities in 5th generation (5G) mobile networks. The radio access network layer slices and the core network layer slices are paired up using the network slicing pairing functionalities. The physical network elements of such network slices will be logically assigned entities called softwarization of the network. Such a novel system architecture called network sliced softwarization of 5G mobile networks (NSS-5G) has shown better performances in terms of end-to-end delay, total throughput, and resource utilization when compared to traditional mobile networks. Thus, effective resource management is achieved using NSS-5G. This study will pave the way for future softwarization of heterogeneous mobile applications.

Algorithmics and Modeling Aspects of Network Slicing in 5G and Beyonds Network: Survey

IEEE Access

One of the key goals of future 5G networks is to incorporate many different services into a single physical network, where each service has its logical network isolated from other networks. Besides, Network Slicing (NS) is considered as the key technology for meeting the service requirements of diverse application domains. Recently, NS faces several algorithmic challenges for 5G networks. This paper provides a review related to NS architecture with a focus on relevant Management and Orchestration (MANO) architecture across multiple domains. In addition, this survey paper delivers a deep analysis and a taxonomy of NS algorithmic aspects. Finally, this paper highlights some of the open issues and future directions.

Real-Time Dynamic Network Slicing for the 5G Radio Access Network

2019 IEEE Global Communications Conference (GLOBECOM), 2019

The 5G networks are expected to satisfy diverse use cases and business models with significant advancements in terms of capacity, reliability, and latency. The allocation and provisioning of network resources pose a challenge for this novel architecture to guarantee higher flexibility and quality of service. As a potential enabler, network slicing was proposed as an innovative approach for the control of the network resources. Although a static slicing approach can be suitable for the transport and core network, the stochastic behavior of the wireless channel requires fast and secure slicing techniques for resource allocation. In this paper, we propose a dynamic slicing approach for the radio access network, where the network resources are carefully assigned to guarantee the service level agreements and increase the number of served users. To prove the performance of our approach, we implemented a fronthaul testbed to emphasize the strength of our method in terms of throughput and resource utilization, compared to static slicing.