Network slicing is one key enabler to provide the required flexibility and to realize the service-oriented 5G vision. Unlike the core network slicing, radio access network (RAN) slicing is still at its infancy and several works just start to investigate the challenges and potentials to enable the multi-service RAN, toward a serviced-oriented RAN (SO-RAN) architecture. One of the major concerns in RAN slicing is to provide different levels of isolation and sharing as per slice requirement. Moreover, both control and user plane processing may be customized allowing a slice owner to flexibly control its service. Enabling dynamic RAN composition with flexible functional split for disaggregated RAN deployments is another challenge. In this paper, we propose a RAN runtime slicing system through which the operation and behavior of the underlying RAN could be customized and controlled to meet slice requirements. We present a proof-of-concept prototype of the proposed RAN runtime slicing system for LTE, assess its feasibility and potentials, and demonstrate the isolation, sharing, and customization capabilities with three representative use cases.
... RadioSaber [16] further refines this approach by incorporating channel awareness into the slice-level scheduler, optimizing RB allocation based on users' Channel Quality Indicators (CQIs). Ensuring slice-level service quality is a primary concern across various RAN slicing proposals [4,10,32]. Recent efforts, such as Zipper [5], a ML-based algorithm to compute SLA-compliant schedules in real-time, albeit with a focus on application-level service assurance. Despite these advancements, existing RAN slicing frameworks predominantly focus on single-antenna systems and commonly employ slot-based and RB-based slicing methodologies [16,19,30]. ...
... To make it more realistic, unlike the previous even distribution of users among slices, we generate a random set of numbers (i.e. [10,12,18,20,25,33,45,37] with a mean of 25) to determine the number of users in each slice. With = 16, our experiments include both sparsely populated (|K | < ) and densely populated (|K | > ) slices. ...
An important aspect of 5G networks is the development of Radio Access Network (RAN) slicing, a concept wherein the virtualized infrastructure of wireless networks is subdivided into slices (or enterprises), tailored to fulfill specific use-cases. A key focus in this context is the efficient radio resource allocation to meet various enterprises' service-level agreements (SLAs). In this work, we introduce a channel-aware and SLA-aware RAN slicing framework for massive multiple input multiple output (MIMO) networks where resource allocation extends to incorporate the spatial dimension available through beamforming. Essentially, the same time-frequency resource block (RB) can be shared across multiple users through multiple antennas. Notably, certain enterprises, particularly those operating critical infrastructure, necessitate dedicated RB allocation, denoted as private networks, to ensure security. Conversely, some enterprises would allow resource sharing with others in the public network to maintain network performance while minimizing capital expenditure. Building upon this understanding, the proposed scheduler comprises scheduling schemes under both scenarios: where different slices share the same set of RBs, and where they require exclusivity of allocated RBs. We validate the efficacy of our proposed schedulers through simulation by utilizing a channel data set collected from a real-world massive MIMO testbed. Our assessments demonstrate that resource sharing across slices using our approach can lead up to 60.9% reduction in RB usage compared to other approaches. Moreover, our proposed schedulers exhibit significantly enhanced operational efficiency, with significantly faster running time compared to exhaustive greedy approaches while meeting the stringent 5G sub-millisecond-level latency requirement.
... Most research on network slicing in the 5G era has been on either radio access network (RAN) slicing or core network (CN) slicing [3]. Research has been carried out on virtualizing and softwarizing the radio resources for RAN slicing [4], while studies known as end-to-end (E2E) network slicing investigates where Virtual Network Functions (VNFs) should be located with respect to the underlying actual infrastructure so that individual slices can operate autonomously [5]. A study was carried out on the creation of interfaces and protocols for inter-slice communication between RAN and CN [6]. ...
... The (3) constraint guarantees that the requested link capacity of a virtual node m ∈ N V can be met by the link capacity of a PI node i ∈ N P . Security level of the virtual node m ∈ N V can be satisfied by the security measures of the PI node i ∈ N P , according to constraint (4). The bandwidth of the PI node i ∈ N P must meet the bandwidth requirements of the virtual node m ∈ N V according to constraint (5). ...
Network slicing is crucial to the 5G architecture because it enables the virtual-ization of network resources into a logical network. Network slices are created, isolated, and managed using software-defined networking (SDN) and network function virtualization (NFV). The virtual network function (VNF) manager must devise strategies for all stages of network slicing to ensure optimal allocation of physical infrastructure (PI) resources to high-acceptance virtual service requests (VSRs). This paper investigates two independent network slicing frameworks named as dual-slice isolation and management strategy (D-SIMS) and recommends the best of the two based on performance measurements. D-SIMS places VNFs for network slicing using self-sustained resource reservation (SSRR) and master-sliced resource reservation (MSRR), with some flexibility for the VNF manager to choose between them based on the degree to which the underlying physical infrastructure has been sliced. The present research work consists of two 1 phases: the first deals with the creation of slices, and the second with determining the most efficient way to distribute resources among them. A deep neural network (DNN) technique is used in the first stage to generate slices for both PI and VSR. Then, in the second stage, we propose D-SIMS for resource allocation, which uses both the fuzzy-PROMETHEE method for node mapping and Dijk-stra’s algorithm for link mapping. During the slice creation phase, the proposed DNN training method’s classification performance is evaluated using accuracy, precision, recall, and F1 score measures. To assess the success of resource allocation , metrics such as acceptance rate and resource effectiveness are used. The performance benefit is investigated under various network conditions and VSRs. Finally, to demonstrate the importance of the proposed work, we compare the simulation results to those in the academic literature.
... The (3) constraint guarantees that the requested link capacity of a virtual node m ∈ N V can be met by the link capacity of a PI node i ∈ N P . Security level of the virtual node m ∈ N V can be satisfied by the security measures of the PI node i ∈ N P , according to constraint (4). The bandwidth of the PI node i ∈ N P must meet the bandwidth requirements of the virtual node m ∈ N V according to constraint (5). ...
Network slicing is crucial to the 5G architecture because it enables the virtualization of network resources into a logical network. Network slices are created, isolated, and managed using software-defined networking (SDN) and network function virtualization (NFV). The virtual network function (VNF) manager must devise strategies for all stages of network slicing to ensure optimal allocation of physical infrastructure (PI) resources to high-acceptance virtual service requests (VSRs). This paper investigates two independent network slicing frameworks named as dual-slice isolation and management strategy (D-SIMS) and recommends the best of the two based on performance measurements. D-SIMS places VNFs for network slicing using self-sustained resource reservation (SSRR) and master-sliced resource reservation (MSRR), with some flexibility for the VNF manager to choose between them based on the degree to which the underlying physical infrastructure has been sliced. The present research work consists of two phases: the first deals with the creation of slices, and the second with determining the most efficient way to distribute resources among them. A deep neural network (DNN) technique is used in the first stage to generate slices for both PI and VSR. Then, in the second stage, we propose D-SIMS for resource allocation, which uses both the fuzzy-PROMETHEE method for node mapping and Dijkstra’s algorithm for link mapping. During the slice creation phase, the proposed DNN training method’s classification performance is evaluated using accuracy, precision, recall, and F1 score measures. To assess the success of resource allocation, metrics such as acceptance rate and resource effectiveness are used. The performance benefit is investigated under various network conditions and VSRs. Finally, to demonstrate the importance of the proposed work, we compare the simulation results to those in the academic literature.
... The slice shape identifies the slots over which the K RBs allocated to the slice must be located. We define an allocation window of duration W (expressed in number of slots) that establishes the time period during which the allocation of RBs to slices must be maintained [17]. We denote L t as the number of RBs allocated to the RAN slice in slot t. ...
5G and beyond networks will support the digitalization of smart manufacturing thanks to their capacity to simultaneously serve different types of traffic with distinct QoS requirements. This can be achieved using Network Slicing that creates different logical network partitions (or slices) over a common infrastructure, and each can be tailored to support a particular type of traffic. The configuration of the Radio Access Network (RAN) slices strongly impacts the capacity of 5G and beyond to support critical services with stringent QoS requirements, and in particular deterministic requirements. Existing RAN Slicing solutions only consider the transmission rate (or bandwidth) requirements of the different services to partition the radio resources. This study demonstrates that this approach is not suitable to guarantee the stringent latency requirements of deterministic aperiodic traffic that is characteristic of industrial critical applications. We then propose designing RAN slices using descriptors that consider both the services' transmission rate and latency requirements, and demonstrate that this approach can support critical services that generate deterministic aperiodic traffic.
... The life cycle of a working slice instance and the proposed RAN subslicing are illustrated in Figure 2. Similar subslicing has been carried out in [23], where dynamic inter-slice radio resource partitioning in the time-frequency plane is proposed. The optimization goal is to find the largest unallocated space. ...
In 5G and beyond, the network slicing is a crucial feature that ensures the fulfillment of service requirements. Nevertheless, the impact of the number of slices and slice size on the radio access network (RAN) slice performance has not yet been studied. This research is needed to understand the effects of creating subslices on slice resources to serve slice users and how the performance of RAN slices is affected by the number and size of these subslices. A slice is divided into numbers of subslices of different sizes, and the slice performance is evaluated based on the slice bandwidth utilization and slice goodput. A proposed subslicing algorithm is compared with k-means UE clustering and equal UE grouping. The MATLAB simulation results show that subslicing can improve slice performance. If the slice contains all UEs with a good block error ratio (BLER), then a slice performance improvement of up to 37% can be achieved, and it comes more from the decrease in bandwidth utilization than the increase in goodput. If a slice contains UEs with a poor BLER, then the slice performance can be improved by up to 84%, and it comes only from the goodput increase. The most important criterion in subslicing is the minimum subslice size in terms of resource blocks (RB), which is 73 for a slice that contains all good-BLER UEs. If a slice contains UEs with poor BLER, then the subslice can be smaller.
The effects of transport development on people’s lives are diverse, ranging from economy to tourism, health care, etc. Great progress has been made in this area, which has led to the emergence of the Internet of Vehicles (IoV) concept. The main objective of this concept is to offer a safer and more comfortable travel experience through making available a vast array of applications, by relying on a range of communication technologies including the fifth-generation mobile networks. The proposed applications have personalized Quality of Service (QoS) requirements, which raise new challenging issues for the management and allocation of resources. Currently, this interest has been doubled with the start of the discussion of the sixth-generation mobile networks. In this context, Network Slicing (NS) was presented as one of the key technologies in the 5G architecture to address these challenges. In this article, we try to bring together the effects of NS implications in the Internet of Vehicles field and show the impact on transport development. We begin by reviewing the state of the art of NS in IoV in terms of architecture, types, life cycle, enabling technologies, network parts, and evolution within cellular networks. Then, we discuss the benefits brought by the use of NS in such a dynamic environment, along with the technical challenges. Moreover, we provide a comprehensive review of NS deploying various aspects of Learning Techniques for the Internet of Vehicles. Afterwards, we present Network Slicing utilization in different IoV application scenarios through different domains; terrestrial, aerial, and marine. In addition, we review Vehicle-to-Everything (V2X) datasets as well as existing implementation tools; besides presenting a concise summary of the Network Slicing-related projects that have an impact on IoV. Finally, in order to promote the deployment of Network Slicing in IoV, we provide some directions for future research work. We believe that the survey will be useful for researchers from academia and industry. First, to acquire a holistic vision regarding IoV-based NS realization and identify the challenges hindering it. Second, to understand the progression of IoV powered NS applications in the different fields (terrestrial, aerial, and marine). Finally, to determine the opportunities for using Machine Learning Techniques (MLT), in order to propose their own solutions to foster NS-IoV integration.
Access to the Internet is growing exponentially due to its ease of usability, flexibility, and lowering data plans. The diverse network service requirements encourage mobile operators to look for mechanisms that facilitate efficient use of network infrastructure, so that it can reduce the operational and expenditure costs. Use cases like the video streaming services requires high bandwidth, autonomous driving and remote medical surgery requires low latency, and various IoT applications work with low bandwidth to cater to the users needs. We simulate the RAN slicing using an emulator called eXP-RAN which effectively manages the allocation of different network resources to the created slices. The infrastructure, slicing, and service layers are the three distinct layers in the proposed system architecture. The isolation and abstraction of the network resources is also applied to the created slices by this emulator.
Network slicing is a key enabler in the Next Generation 5G Radio Access Network (RAN) to build the RAN-as-a-Service concept. Cloud-RAN, Network Function Virtualization, Software Defined Network and RAN functional splits are the main pillars expected to be integrated to provide the required flexibility. One of the major concerns is to efficiently allocate RAN resources for slices, while supporting multiple use-cases with heterogeneous Quality-of-Service (QoS) requirements. Current related work is adopting radio resource allocation scheme by considering a cell-centric deployment approach for slice embedding. However, to achieve greater flexibility and fine-grained tunable resource utilization, we believe that the deployment scheme should be integrated in the slice design. In this paper, we go a step further and propose a RAN slicing approach with customized deployment scheme on user basis. As the corresponding optimization problem is NP-Hard, we propose a low-cost and efficient heuristic algorithm for RAN Slice allocation based on the Particle Swarm Optimization approach. Our proposal jointly harnesses radio, processing and link resources at user level tailored to the QoS requirements, while customizing efficiently the underlying physical RAN resource usage.
The fifth generation (5G) mobile cellular network relies on network slicing (NS) to satisfy the diverse quality of service (QoS) requirements of various service providers operating on a standard shared infrastructure. However, the synchronization of radio access network (RAN) and core network (CN) slicing has not been well-studied as an interdependent resource allocation problem. This work proposes a novel slice-to-node access factor (SNAF)-based end-to-end (E2E) slice resource provisioning scheme and deep reinforcement learning (DRL)-based real-time resource allocation algorithm for E2E interdependent resource slicing and allocation, respectively, specifically for RAN and CN. To ensure effective resource slicing and allocation, we consider the versatile user equipment (UEs) QoS requirements on transmission delay and data rate. Notably, the SNAF-based scheme provides proper resource provisioning and traffic synchronization, while the DRL-based algorithm allocates radio resources based on affordable traffic and backhaul resources. Based on the 5G air interface, we conduct system-level simulations to evaluate the performance of our proposed methods from various perspectives. Simulation results confirm that our proposed SNAF and DRL-based interdependent E2E resource slicing and allocation techniques achieve better E2E traffic-resource synchronization, and improve the QoS satisfaction with minimal resource utilization compared to other existing benchmark schemes.
Network slicing is a fundamental capability for future Fifth Generation (5G) networks to facilitate the cost-effective deployment and operation of multiple logical networks over a common physical network infrastructure in a way that each logical network (i.e. network slice) can be customized and dimensioned to best serve the needs of specific applications (e.g. mobile broadband, smart city, connected car, public safety, fixed wireless access) and users (e.g. general public, enterprise customers, virtual operators, content providers). The practical realization of such capability still raises numerous technical challenges, both in the Core and RAN parts of the 5G system. Through a comprehensive analysis of the impact that the realization of RAN slicing has on the different layers of the radio interface protocol architecture, this article proposes a framework for the support and specification of RAN slices based on the definition of a set of configuration descriptors that characterize the features, policies and resources to be put in place across the radio protocol layers of a next-generation RAN node.
Emerging 5G mobile networks are envisioned to become multi-service environments, enabling the dynamic deployment of services with a diverse set of performance requirements, accommodating the needs of mobile network operators, verticals and over-the-top (OTT) service providers. Virtualizing the mobile network in a flexible way is of paramount importance for a cost-effective realization of this vision. While virtualization has been extensively studied in the case of the mobile core, virtualizing the radio access network (RAN) is still at its infancy. In this paper, we present Orion, a novel RAN slicing system that enables the dynamic on-the-fly virtualization of base stations, the flexible customization of slices to meet their respective service needs and which can be used in an end-to-end network slicing setting. Orion guarantees the functional and performance isolation of slices, while allowing for the efficient use of RAN resources among them. We present a concrete prototype implementation of Orion for LTE, with experimental results, considering alternative RAN slicing approaches, indicating its efficiency and highlighting its isolation capabilities. We also present an extension to Orion for accommodating the needs of OTT providers.
5G networks will support very diverse and challenging requirements. Network slicing offers an effective way to unlock the full potential of 5G networks and meet those requirements using a common network infrastructure. This article presents a cloud-native approach to network slicing that advances a fundamental rethinking of the mobile network to shift its architectural vision from a network of entities to a network of capabilities, and its driving purpose from a network for connectivity to a network for services. The approach covers the entire life cycle of network slices, encompassing their design, creation, deployment, customization, and optimization. We provide an overview of our cloud-native approach to network slicing and describe a proof-of-concept implementation that demonstrates its key principles.
Cellular traffic continues to grow rapidly making the scalability of the cellular infrastructure a critical issue. However, there is mounting evidence that the current Evolved Packet Core (EPC) is ill-suited to meet these scaling demands: EPC solutions based on specialized appliances are expensive to scale and recent software EPCs perform poorly, particularly with increasing numbers of devices or signaling traffic.
In this paper, we design and evaluate a new system architecture for a software EPC that achieves high and scalable performance. We postulate that the poor scaling of existing EPC systems stems from the manner in which the system is decomposed which leads to device state being duplicated across multiple components which in turn results in frequent interactions between the different components. We propose an alternate approach in which state for a single device is consolidated in one location and EPC functions are (re)organized for efficient access to this consolidated state. In effect, our design "slices" the EPC by user.
We prototype and evaluate PEPC, a software EPC that implements the key components of our design. We show that PEPC achieves 3-7x higher throughput than comparable software EPCs that have been implemented in industry and over 10x higher throughput than a popular open-source implementation (OpenAirInterface). Compared to the industrial EPC implementations, PEPC sustains high data throughput for 10-100x more users devices per core, and a 10x higher ratio of signaling-to-data traffic. In addition to high performance, PEPC's by-user organization enables efficient state migration and customization of processing pipelines. We implement user migration in PEPC and show that state can be migrated with little disruption, e.g., migration adds only up to 4μs of latency to median per packet latencies.
Knowing the variety of services and applications to be supported in the upcoming 5G systems, the current "one size fits all" network architecture is no more efficient. Indeed, each 5G service may have different needs in terms of latency, bandwidth, and reliability, which cannot be sustained by the same physical network infrastructure. In this context, network virtualization represents a viable way to provide a network slice tailored to each service. Several 5G initiatives (from industry and academia) have been pushing for solutions to enable network slicing in mobile networks, mainly based on SDN, NFV, and cloud computing as key enablers. The proposed architectures focus principally on the process of instantiating and deploying network slices, while ignoring how they are enforced in the mobile network. While several techniques of slicing the network infrastructure exist, slicing the RAN is still challenging. In this article, we propose a new framework to enforce network slices, featuring radio resources abstraction. The proposed framework is complementary to the ongoing solutions of network slicing, and fully compliant with the 3GPP vision. Indeed, our contributions are twofold: a fully programmable network slicing architecture based on the 3GPP DCN and a flexible RAN (i.e., programmable RAN) to enforce network slicing; a two-level MAC scheduler to abstract and share the physical resources among slices. Finally, a proof of concept on RAN slicing has been developed on top of OAI to derive key performance results, focusing on the flexibility and dynamicity of the proposed architecture to share the RAN resources among slices.
The upcoming 5G ecosystem is envisioned to build business-driven Network Slices to accommodate the different needs of divergent service types, applications and services in support of vertical industries. In this paper, we describe the Network Slicing concept, by unveiling a novel Network Slicing architecture for integrated 5G communications. Further, we demonstrate its realization, for the case of evolved LTE, using state of the art
technologies. Finally, we elaborate on the LTE specific requirements towards 5G and point out existing challenges and open issues.
With the continued exponential growth of mobile traffic and the rise of diverse applications, the current LTE radio access network (RAN) architecture of cellular operators face mounting challenges. Current RAN suffers from insufficient radio resource coordination, inefficient infrastructure utilization, and inflexible data paths. We present the high level design of PRAN, which centralizes base stations' L1/L2 processing into a cluster of commodity servers. PRAN uses a flexible data path model to support new protocols; multiple base stations' L1/L2 processing tasks are scheduled on servers with performance guarantees; and a RAN scheduler coordinates the allocation of shared radio resources between operators and base stations. Our evaluation shows the feasibility of fast data path control and efficiency of resource pooling (a potential for a 30× reduction on resources).