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Enable Advanced QoS-Aware Network Slicing in 5G Networks for Slice-Based Media Use Cases
Abstract
Media use cases for emergency services require mission-critical levels of reliability for the delivery of media-rich services, such as video streaming. With the upcoming deployment of the fifth generation (5G) networks, a wide variety of applications and services with heterogeneous performance requirements are expected to be supported, and any migration of mission-critical services to 5G networks presents significant challenges in the quality of service (QoS), for emergency service operators. This paper presents a novel SliceNet framework, based on advanced and customizable network slicing to address some of the highlighted challenges in migrating eHealth telemedicine services to 5G networks. An overview of the framework outlines the technical approaches in beyond the state-of-the-art network slicing. Subsequently, this paper emphasizes the design and prototyping of a media-centric eHealth use case, focusing on a set of innovative enablers toward achieving end-to-end QoS-aware network slicing capabilities, required by this demanding use case. Experimental results empirically validate the prototyped enablers and demonstrate the applicability of the proposed framework in such media-rich use cases.
... So far, only a few research works have focused on 5G applied to mobile hospital systems. A 5G network slicingbased framework has been proposed by Wang et al. to address some challenges faced by eHealth telemedicine services [17]. ...
... The main differences between our proposed method and the related works in terms of slicing are as follows. First, except [17] and [18], none of the related works have used the network slicing technique like our work. Second, traditional cellular architectures with baseband processing decentralized to the base stations have been used in all the related works, whereas our proposed slicing method uses the C-RANs, where baseband resources are centralized to a single baseband unit (BBU) pool using optical fronthaul links to connect several remote radio heads (RRHs) [9], [24]. ...
... But we should choose a larger ε q u without violating the OPLP bound target ε u . According to (17), ε u can still be retained with a larger tolerated ε q u if the decoding error probability ε e u is reduced. ...
As one of the healthcare development trends, mobile hospital systems nowadays need more advanced means of treatment and diagnosis and require higher data transmission speed and capacity with lower latency. With its ability to simultaneously support a variety of services in a wide range of application scenarios using network slicing technique, the emerging fifth generation communication system (5G) is expected to offer significant communication performances, including higher throughput, higher mobility, higher connection density, higher transmission reliability with lower latency than the current fourth generation (4G). Hence, 5G can serve as an effective transmission means to meet the modern mobile hospital systems requirements. This paper proposes a 5G network slicing-based mobile hospital system where two types of slices, namely enhanced mobile broadband (eMBB) slice and ultra-reliable and low-latency communications (uRLLC) slice, are dedicated to the medical data. We propose an optimization method to maximize the throughput of the medical data assigned to the eMBB slice. We also propose a resource allocation algorithm for very high transmission reliability with very low latency of the medical data assigned to the uRLLC slice. Simulation results indicate that our proposed approach can effectively meet mobile hospital systems requirements for data transmission throughput, reliability, and latency from remote sites to hospital centers.
... Where stages address critical issues such as mapping from high-level service requirements to network function (NF) and infrastructure requirements were provided. Advanced QoS-aware NS was discussed by Wang et al. (2020), where design and prototyping of a media centric e-health use case was emphasised. ...
... Thantharate et al. (2019) developed a deep-slice model for managing network load efficiency, network availability, and prediction, where key performance indicators (KPIs) are used for training purposes. A deep reinforcement learning approach was used by Wang et al. (2020) to achieve automatic-efficient optimisation with E2E service reliability. Li et al. (2018) designed deep learning model for the prediction of VNF service chain requests and a novel deep learning-based two-phase algorithm was proposed by Pei et al. (2020), where VNF selection and chaining networks were designed for achieving intelligent NS. ...
... The NS mathematical model adopted in this paper is analogous to the one used in previous literature (Gligoroski and Kralevska, 2019;Guan et al., 2019;Wang et al., 2020), which requires topological information such as core nodes (CNs), optical switches (OSs) and base stations (BSs). The infrastructure network is represented by an un-directed weighted graph as G I = (N I , E I , C I , B I ). ...
Over the last few years, network slicing has been presented as one of the key ingredients in 5G for efficiently specifying network services as per the heterogeneous quality and functional requirements over common shared resources. Network slices are multiple networks having their own management, requirements, and characteristics, positioned over the same physical network with distinct network functions present in each slice. Thus, multiple independent end-to-end networks are supposed to be deployed in 5G, using parallel network slicing. However, it is not easy to guarantee that the traffic on one slice will not affect the traffic on another slice. To implement independent and intelligent network slicing management, this paper proposes a data-driven machine learning-based slicing and allocation model which provides greater flexibility with quality of service (QoS) and traffic-aware reliable dynamic slicing, where resources can be intelligently assigned and redistributed among network slices according to temporal variation of the virtual resource requirements.
... Where stages address critical issues such as mapping from high-level service requirements to network function (NF) and infrastructure requirements were provided. Advanced QoS-aware NS was discussed by Wang et al. (2020), where design and prototyping of a media centric e-health use case was emphasised. ...
... Thantharate et al. (2019) developed a deep-slice model for managing network load efficiency, network availability, and prediction, where key performance indicators (KPIs) are used for training purposes. A deep reinforcement learning approach was used by Wang et al. (2020) to achieve automatic-efficient optimisation with E2E service reliability. Li et al. (2018) designed deep learning model for the prediction of VNF service chain requests and a novel deep learning-based two-phase algorithm was proposed by Pei et al. (2020), where VNF selection and chaining networks were designed for achieving intelligent NS. ...
... The NS mathematical model adopted in this paper is analogous to the one used in previous literature (Gligoroski and Kralevska, 2019;Guan et al., 2019;Wang et al., 2020), which requires topological information such as core nodes (CNs), optical switches (OSs) and base stations (BSs). The infrastructure network is represented by an un-directed weighted graph as G I = (N I , E I , C I , B I ). ...
... In these scenarios, the total number of slice requests allocated in DSAF is greater or equal than the FCFSFA scheme. [59]. This testbed (Figure 16) proposes a QoS-aware network slicing for multiple services with distinct QoS requirements. ...
... (ii) The second category encompasses the implementations which satisfy all of the primary and the majority of secondary attributes from the design criteria explained in Section 3. The testbeds such as those in [43,48,49,52,53,59,64,67] deliver E2E network slicing with MANO privilege in their architectures along with multi-tenancy and multi-RAT support. The testbeds in [59,64] also incorporate MLenabled capability in their architectures, and the testbed in [64] is open-source. ...
... (ii) The second category encompasses the implementations which satisfy all of the primary and the majority of secondary attributes from the design criteria explained in Section 3. The testbeds such as those in [43,48,49,52,53,59,64,67] deliver E2E network slicing with MANO privilege in their architectures along with multi-tenancy and multi-RAT support. The testbeds in [59,64] also incorporate MLenabled capability in their architectures, and the testbed in [64] is open-source. ...
Developing specialized cloud-based and open-source testbeds is a practical approach to investigate network slicing functionalities in the fifth-generation (5G) mobile networks. This paper provides a comprehensive review of most of the existing cost-efficient and small-scale testbeds that partially or fully deploy network slicing. First, we present relevant software packages for the three main functional blocks of the ETSI NFV MANO framework and for emulating the access and core network domains. Second, we define primary and secondary design criteria for deploying network slicing testbeds. These design criteria are later used for comparison between the testbeds. Third, we present the state-of-the-art testbeds, including their design objectives, key technologies, network slicing deployment, and experiments. Next, we evaluate the testbeds according to the defined design criteria and present an in-depth summary table. This assessment concludes with the superiority of some of them over the rest and the most dominant software packages satisfying the ETSI NFV MANO framework. Finally, challenges, potential solutions, and future works of network slicing testbeds are discussed.
... Figure 15: Implementing a topology with physical servers (brown blocks) and DSAF components (blue blocks with solid lines representing logical communication paths) [58]. [59]. This testbed ( Figure 16) proposes a QoS-aware network slicing for multiple services with distinct QoS requirements. ...
... With the help of PHY abstraction mode of oaisim in OAI RAN, emulating practical network scenarios with numerous UEs and eNBs is conceivable. In particular, the Figure 16: E2E network slicing approach in SliceNet platform [59]. ...
... (ii) The second category encompasses the implementations which satisfy all of the primary and the majority of secondary attributes from the design criteria explained in Section 3. The testbeds such as those in [43], [48], [49], [52], [53], [59], [64], [67] deliver E2E network slicing with MANO privilege in their architectures along with multi-tenancy and multi-RATs support. The testbeds in [59], [64] also incorporate ML-enabled capability in their architectures, and the testbed in [64] is open-source. ...
Developing specialized cloud-based and open-source testbeds is a practical approach to investigate network slicing functionalities in the fifth-generation (5G) mobile networks. This paper provides a comprehensive review of most of the existing cost-efficient and small-scale testbeds that partially or fully deploy network slicing. First, we present relevant software packages for the three main functional blocks of the ETSI NFV MANO framework and for emulating the access and core network domains. Second, we define primary and secondary design criteria for deploying network slicing testbeds. These design criteria are later used for comparison between the testbeds. Third, we present the state-of-the-art testbeds, including their design objectives, key technologies, network slicing deployment, and experiments. Next, we evaluate the testbeds according to the defined design criteria and present an in-depth summary table. This assessment concludes with the superiority of some of them over the rest and the most dominant software packages satisfying the ETSI NFV MANO framework. Finally, challenges, potential solutions, and future works of network slicing testbeds are discussed.
... Wang et al. [79] propose a network slicing framework for an eHealth use case in the 5G context. The framework employs P4 FPGA boards to perform traffic parsing and classification, as well as QoS control, for video transmission. ...
... In [76,77], where another industrial application is presented, experiments show delays of 15 ms for digital twin app data, 30 ms for telemetry packets, and 50 ms for remote support, all of which use virtual queues with priority queuing. The study in [79] presents an e-health video transmission use case in which slices with a high QoS level have an average delay of less than 0.05 ms. Moreover, in [52], the authors include the committed information rate (CIR) ratio as an adjustment parameter in the hardware switches for the throughput of each supported vertical for three IoT scenarios. ...
Applications in 5G and Beyond Architectures: A Systematic Review. Sensors 2023, 23, 6955. https:// Abstract: The rapid evolution of 5G and beyond technologies has sparked an unprecedented surge in the need for networking infrastructure that can deliver high speed, minimal latency, and remarkable flexibility. The programmable data plane, which enables the dynamic reconfiguration of network functions and protocols, is becoming increasingly important in meeting these requirements. This paper provides an overview of the current state of the art in programmable data planes implemented in 5G and beyond architectures. It proposes a classification of the reviewed studies based on system architecture and specific use cases. Furthermore, the article surveys the primary applications of programmable devices in emerging telecommunication networks, such as tunneling and forwarding, network slicing, cybersecurity, and in-band telemetry. Finally, this publication summarizes the open research challenges and future directions. In addition to offering a comprehensive review of programmable data plane applications in telecommunication networks, this article aims to guide further research in this promising field for network operators and researchers alike.
... Network slicing has now become a powerful approach to ensuring certain levels of well-performing services. Nowadays, there is a lot of research and developments in the state of the art that leverage network slicing to guarantee QoS levels among heterogeneous use cases such as the Internet of things (IoT) [17]- [19], smart grids [20], [21], smart cities [22], eHealth (e.g., telemedicine, critical services) [23], [24], or intelligent transport, education and media and entertainment [25], among others. ...
... All such metrics are directly stored in the inventory (21), shared with metric aggregator engine (22) and they are then indexed (23). Moreover, FCA makes the subscription to actions and intents (24); all intents messages received are directly stored in the inventory (25), shared with the provisioning engine (26) and then indexed (27). At this stage, slice nanager is bootstrapped and fully functional. ...
Network slicing is one of the cornerstone features of the fifth generation (5G) networks to enable the multiplexing of virtualised logical networks over the same physical network infrastructure for various vertical business services. Architectures based on network slicing enable network operators to offer end-to-end (E2E) vertical services whilst assuring fulfilling tailored service level agreement (SLA) requirements. Despite the numerous benefits that this concept offers, the need for efficient, scalable and holistic E2E management of network slicing significantly complicates network operators' network management and imposes significant challenges in the quality of service (QoS). This paper proposes a novel intent-based slice manager framework over a service base 5G architecture that allows flexible definitions to customise network slices, automates all essential management tasks for 5G network slice providers, manages the life cycles of all sorts of network slices, and guarantees their QoS in a unified network slice management framework. The proposed framework is empirically validated in a realistic large-scale 5G multi-tenant infrastructure. The high scalability of the framework is based on the empirical results supporting more than 512 physical machines, 534288 virtual machines and more than two million network interfaces.
... Multimedia data not only give detailed information but offer clear and highly visible insights for a critical scene analysis. For example, remote surgery needs high speeds, short delays, and better visualization at both the expert's and the patient's side [67]. An overview of the role of slice networks in transforming critical healthcare applications on a 5G platform has been presented [67]. ...
... For example, remote surgery needs high speeds, short delays, and better visualization at both the expert's and the patient's side [67]. An overview of the role of slice networks in transforming critical healthcare applications on a 5G platform has been presented [67]. Also discussed are media-centric healthcare use cases, which are key components for enhancing QoS. ...
The dynamic nature of wireless links and the mobility of devices connected to the Internet of Things (IoT) over fifth-generation (5G) networks (IoT-5G), on the one hand, empowers pervasive healthcare applications. On the other hand, it allows eavesdroppers and other illegitimate actors to access secret information. Due to the poor time efficiency and high computational complexity of conventional cryptographic methods and the heterogeneous technologies used, it is easy to compromise the authentication of lightweight wearable and healthcare devices. Therefore, intelligent authentication, which relies on artificial intelligence (AI), and sufficient network resources are extremely important for securing healthcare devices connected to IoT-5G. This survey considers intelligent authentication and includes a comprehensive overview of intelligent authentication mechanisms for securing IoT-5G devices deployed in the healthcare domain. First, it presents a detailed, thoughtful, and state-of-the-art review of IoT-5G, healthcare technologies, tools, applications, research trends, challenges, opportunities, and solutions. We selected 20 technical articles from those surveyed based on their strong overlaps with IoT, 5G, healthcare, device authentication, and AI. Second, IoT-5G device authentication, radio-frequency fingerprinting, and mutual authentication are reviewed, characterized, clustered, and classified. Third, the review envisions that AI can be used to integrate the attributes of the physical layer and 5G networks to empower intelligent healthcare devices. Moreover, methods for developing intelligent authentication models using AI are presented. Finally, the future outlook and recommendations are introduced for IoT-5G healthcare applications, and recommendations for further research are presented as well. The remarkable contributions and relevance of this survey may assist the research community in understanding the research gaps and the research opportunities relating to the intelligent authentication of IoT-5G healthcare devices.
... In addition, the SDN can be incorporated into the network function virtualization (NFV) [3,4], by which virtual network functions (VNFs) are interconnected into different delivery operations. For applications based on the 5G network and beyond [5] such as eHealth, smart poles, and smart cities [6][7][8], SDN and NFV for QoS could play important roles for efficient allocations of network resources for communication services. ...
... The OpenFlow protocol [3] is used for the de- ing the ERAB to service quality y by (6). For the search space B for each service, we set the step size ∆ = 0.25 Mbps in (7). Furthermore, both Link 1 and link 2 have the same maximum bandwidth B 1 = B 2 = 40 Mbps. ...
An effective System-on-Chip (SoC) for smart Quality-of-Service (QoS) management over a virtual local area network (LAN) is presented in this study. The SoC is implemented by field programmable gate array (FPGA) for accelerating the delivery quality prediction for a service. The quality prediction is carried out by the general regression neural network (GRNN) algorithm based on a time-varying profile consisting of the past delivery records of the service. A novel record replacement algorithm is presented to update the profile, so that the bandwidth usage of the service can be effectively tracked by GRNN. Experimental results show that the SoC provides self-aware QoS management with low computation costs for applications over virtual LAN.
... Researchers [12][13][14] utilize data plane programmability to develop Quality of Service (QoS) management solutions using Programming Protocol-Independent Packet Processors (P4) switches. The study [12] proposes a customized P4 pipeline for strict bandwidth limitation and UDP stream guarantees. ...
... As such, they proposed TCP-friendly meters that can achieve up to 85% in target rate. The work in [14] utilizes the P4 NetFPGA reference implementation to develop a QoS-aware data plane for slicing that classifies traffic flows and schedules them to multi-level priority queues, but the study only considers the effects of network slicing on media-rich applications. ...
Network slicing is considered a key technology in enabling the underlying 5G mobile network infrastructure to meet diverse service requirements. In this article, we demonstrate how transport network slicing accommodates the various network service requirements of Massive IoT (MIoT), Critical IoT (CIoT), and Mobile Broadband (MBB) applications. Given that most of the research conducted previously to measure 5G network slicing is done through simulations, we utilized SimTalk, an IoT application traffic emulator, to emulate large amounts of realistic traffic patterns in order to study the effects of transport network slicing on IoT and MBB applications. Furthermore, we developed several MIoT, CIoT, and MBB applications that operate sustainably on several campuses and directed both real and emulated traffic into a Programming Protocol-Independent Packet Processors (P4)-based 5G testbed. We then examined the performance in terms of throughput, packet loss, and latency. Our study indicates that applications with different traffic characteristics need different corresponding Committed Information Rate (CIR) ratios. The CIR ratio is the CIR setting for a P4 meter in physical switch hardware over the aggregated data rate of applications of the same type. A low CIR ratio adversely affects the application’s performance because P4 switches will dispatch application packets to the low-priority queue if the packet arrival rate exceeds the CIR setting for the same type of applications. In our testbed, both exemplar MBB applications required a CIR ratio of 140% to achieve, respectively, a near 100% throughput percentage with a 0.0035% loss rate and an approximate 100% throughput percentage with a 0.0017% loss rate. However, the exemplar CIoT and MIoT applications required a CIR ratio of 120% and 100%, respectively, to reach a 100% throughput percentage without any packet loss. With the proper CIR settings for the P4 meters, the proposed transport network slicing mechanism can enforce the committed rates and fulfill the latency and reliability requirements for 5G MIoT, CIoT, and MBB applications in both TCP and UDP.
... where η 1 , η 2 and η 3 are constant coefficients, N represents the number of time slots divided in one day, M represents the number of SFC requests, e(q, i) represents the energy consumption of SFC request i in time slot q, n mig (q, i) represents the migrated nodes of SFC request i in time slot q, and x(q, i) represents whether SFC request i is mapped successfully or not, where the value is 1 in case of success and the value is 0 otherwise. The constraints align with those from previous literature, such as [1,24,25]. The constraints include the following: each VNF can only be mapped to one physical node; each virtual link can only be mapped to one physical path; when the virtual link is remapped, the VNFs on it must be mapped to the corresponding physical node of the physical link where the virtual link is located; the resource (e.g., CPU, storage, or bandwidth) constraints can not be exceeded; the end-to-end delay limit of the SFC cannot be exceeded; and so on. ...
Network Function Virtualization (NFV) allows for the dynamic provisioning of Virtual Network Functions (VNFs), adapting services to the complex and dynamic network environment to enhance network performance. However, VNF migration and energy consumption pose significant challenges due to the dynamic nature of the physical network. In order to maximize the acceptance rate of Service Function Chain Requests (SFCR), and reduce VNF migration and energy consumption as much as possible, we summarize several related factors such as the node hosting state, link hosting state, energy consumption, migrated nodes, and whether the mapping is successful. We define the Markov decision process by considering the factors mentioned above. Next, we design the VNF migration algorithm utilizing actor–critic models, graph convolution networks, and LSTM networks. In order to reduce the risk of trial and error during training and prediction in deep reinforcement learning scenarios, we designed a network architecture based on a digital twin (DT). In simulation experiments, compared with the FF algorithm that greedily selects the first available node, our AC_GCN algorithm significantly improves the acceptance rate of SFC requests by 2.9 times more than the FF algorithm in small topology experiments, and 27 times more than the FF algorithm in large topology experiments. Compared with the deep reinforcement learning (DRL) algorithm, which does not consider all the above factors together, for the small topology experiment, our AC_GCN algorithm outperforms the DRL algorithm in terms of request acceptance rate by 13%, underperforms compared to the DRL algorithm in terms of energy consumption by 3.8%, and underperforms compared to the DRL algorithm in terms of the number of migrated nodes for 22%; for the large topology experiment, our AC_GCN algorithm outperforms the DRL algorithm in terms of the request acceptance rate by 7.7%, outperforms the DRL algorithm in terms of energy consumption by 0.4%, and outperforms the DRL algorithm in terms of the number of migrated nodes by 1.6%.
... The deployment of robots can benefit from 5G's SDN and NFV to scale up robot learning and knowledge sharing that was impossible in the past. The robotic application we propose and discuss in this paper requires intensive data transmission and processing, which is different from those currently demonstrated by other projects as initial proofs-of-concept of robotic vertical applications, such as: the H2020 SliceNet framework [1] 5G-Tours [2] and 5G-HEART project [3] demonstrated robotic services in smart/ connected ambulance, museum tour and healthcare with virtual network slices. ...
Cloud and edge computing, distributed AI, and most recently 5G/6G communications are coming together and changing the way we collaborate, connect and interact. A new generation of AI-powered robots are also expected to be facilitated by these digital technological breakthroughs. Robots are supposed to tackle unknown situations and adapt in the long term by collaborating, connecting and interacting with the digital world. Such applications generate versatile, perpetuated and rapidly changing transmission demands to the network. Traditional network resource management is insufficient in supporting such traffic to meet the QoS. In this paper, we go a step further, in addition to the effort on the network side for traffic engineering; we also work on the application side to shape the traffic within non-public networks. We present an initial development for the proposed intent-based deployment for robotic applications.
... Therefore, to offer the service-level requirements of emergency responders, it is pertinent to ensure that network slices operate dynamically and autonomously. Efforts are being made to enable an automatic orchestration of network resources across different domains with a high Quality of Service (QoS), leveraging Zero-touch network and Service Management (ZSM) and Network Slicing (NS) techniques [3][4][5][6]. ...
The telecommunication system being a critical pillar of emergency management, intelligent deployment and management of slices in an affected area will help emergency responders. Techniques such as automated management of Machine Learning (ML) pipelines across the edge and emergency responder devices, usage of hierarchical closed-loops, and offloading inference tasks closer to the edge can minimize latencies for first responders in case of emergencies. This study describes the major results from building a Proof of Concept (PoC) for network resource allocation for emergency management using a hierarchical autonomous Artificial Intelligence (AI)/ML-based closed-loops in the mobile network, organized by the Internal Telecommunication Union Focus Group on Autonomous Networks (ITU FG-AN). The background scenario for this PoC included the interaction between a higher closed-loop in the Operations Support System (OSS) and a lower closed-loop in Radio Access Network (RAN) to intelligently share RAN resources between the public and the emergency responder slice. Representation of closed-loop "controllers" in a declarative fashion (intent), triggering "imperative actions" in the "underlay" based on the intent, setup of a data pipeline between various components, and methods of "influencing" lower layer loops using specific logic/models, were some of the essential aspects investigated by various teams. The main conclusions are summarised in this paper, including the significant observations and limitations from the PoC as well as future directions.
... Slicing can be used to simultaneously support different service providers (tenants) over the same physical network, and to enable service differentiation within the same tenant. In both cases, with proper network slice management, it is possible to guarantee the required QoS to provide professional multimedia services over 5G [40]. ...
5G is a key tool for the cloud-based wireless production of audio-visual contents. By providing higher throughput and lower latency than previous mobile network technologies, as well as flexible allocation of network resources, it enables that some content production pipelines, which are currently implemented using wired connectivity, are provided on top of a 5G network. This brings down production costs, reduces environmental impact and increases operational efficiency. In particular, live immersive production services, such as free-viewpoint video (FVV), can be provided in a much more efficient way. In this paper, we address the challenge of producing and streaming FVV services in real time through 5G networks. We have adapted a state-of-the-art FVV system to integrate it within a 5G architecture, using three key technological enablers: mmWave radio access network to support the uplink traffic requirements from FVV cameras, multi-access edge computing to run video processing algorithms with minimum latency, and end-to-end slicing to guarantee a sufficient quality of service (QoS) within the production pipeline. We have built a field trial over the production network of a telecommunication operator, including a mmWave pilot deployment, edge cloud processing, and remote content production, involving three different locations across Spain. We have measured the key performance indicators at the relevant parts of our trial deployment, showing that, with existing 5G technology, it is possible to achieve live FVV production, although with some limitations. We have also analyzed such limitations, obtaining some insights on how the next generation of 5G networks can overcome them to achieve higher quality of experience (QoE).
... Table 1 summarizes the main articles published in the last four years and their goal and QoS strategy. Among different QoS methods, studies focus mainly on four common strategies: (i) scheduling protocols [15,16]; (ii) data prioritization [15][16][17][18][19][20][21][22]; (iii) routing protocols [22][23][24][25][26][27]; (iv) resource management [16,21]. Studies have examined the use of data prioritization to reduce transmission time. ...
Background: the increasing adoption of smart and wearable sensors in the healthcare domain empowers the development of cutting-edge medical applications. Smart hospitals can employ sensors and applications for critical decision-making based on real-time monitoring of patients and equipment. In this context, quality of service (QoS) is essential to ensure the reliability of application data. Methods: we developed a QoS-aware sensor middleware for healthcare 4.0 that orchestrates data from several sensors in a hybrid operating room. We deployed depth imaging sensors and real-time location tags to monitor surgeries in real-time, providing data to medical applications. Results: an experimental evaluation in an actual hybrid operating room demonstrates that the solution can reduce the jitter of sensor samples up to 90.3%. Conclusions: the main contribution of this article relies on the QoS Service Elasticity strategy that aims to provide QoS for applications. The development and installation were demonstrated to be complex, but possible to achieve.
... With the continuous integration of artificial intelligence technology and big data analysis technology with human life, as an important part of cloud intelligence algorithm, deep learning technology has gradually played more and more roles in many fields [1]. At present, most We-media data protection systems are mainly based on traditional data storage and protection strategies [2]. If the deep learning algorithm is introduced into the self-media privacy data protection system, the self-media data protection system will hopefully obtain more protection strategies [3]. ...
In recent years, with the in-depth research on the privacy protection methods of We-media network, various types of big data analysis technologies gradually protect the network privacy data, but there are still problems of low intelligence and poor protection in the existing research. Based on this, this paper first uses big data technology and artificial intelligence deep learning algorithm to complete the construction of different types of self-media control databases. Then, it analyzes the common privacy data types of We-media network, constructs an optimization protection model based on secondary identification and verification strategy, and forms a data query system. Finally, simulation experiments are conducted to verify whether the constructed network privacy protection model can realize the intelligent protection of network privacy algorithms from different dimensions. In the process of privacy protection of experimental data at different stages, the internal correlation differences of different types of protection algorithm strategies are obvious in the multidimensional analysis of specific databases. For different types of factor data in different types of We-media networks, the protection rate of the We-media network privacy protection model designed in this study has reached more than 95%. The research results show that the self-media network privacy protection model based on big data and artificial intelligence deep learning technology can realize protection from the aspects of gateway verification and data encryption and has high accuracy and reliability.
... To treat the security attacks intelligently and effectively, AI based security solutions perform by following the smart slice security cycle: identify, protect, proceed, and recover. Examples of AI based solutions include pattern matching or anomaly detection for attacks detection and identification, source code morphology for protection, and open-source intelligence (OS-INT) for awareness [75]. ...
Network slicing is one of the emerging technologies allowing resource sharing among different network entities in 5G networks. It enables delivering smart, critical, and multi-services with distinctive requirements transiting from network as an infrastructure to network as a service setup. Although its advantages, it is facing several challenges raised from isolation and resource sharing among services leading to security issues. Security is a critical problem for network slicing as slices serving customized services with different requirements may also have different security levels and policies. Thus, considering the impact of these security issues on network slices is required when defining and designing security protocols. Addressing these challenges is necessary to protect users’ security and privacy while maintaining the required performance and QoS. Most of the existing works covered only one or more aspects of the network slicing including, architecture, taxonomy, challenges, security issues, attacks classification, possible solutions, and future scope. In this paper, we extensively investigated all these aspects and others, we analyzed how the security can be ensured inside and outside of the network slices with resource isolation, machine learning, and cryptography with an E2E security. We presented a deep review of the security issues threatening the network slicing and how to mitigate them over a multi-domain infrastructure in 5G networks. we evaluated the performance of some of these solutions in preventing malicious attacks through experiments using Open Air Interface.
... A study conducted in network slicing environment using facilities at the 5G Prototyping Lab at Dell EMC facilities Ireland and SliceNet reported an average round trip latency of 296.91 ms from client to core, an average round trip time of 50.68 ms from client to edge, and an average packet loss of 7.2% for the core and 0.1% at the edge [65]. Another study was carried out in [64] using the same experimental tools with the added features like QoS control based on the data plane programmability and low-latency cloud-based mobile edge computing (MEC) platform. Throughput was evaluated for the coordinated and uncoordinated network slicing strategies and ranged from 0 to 18 Mbps. ...
Fifth generation (5G) mobile communication technology can enable novel healthcare applications and augment existing ones. However, 5G-enabled healthcare applications demand diverse technical requirements for radio communication. Knowledge of these requirements is important for developers, network providers, and regulatory authorities in the healthcare sector to facilitate safe and effective healthcare. In this paper, we review, identify, describe, and compare the requirements for communication key performance indicators in relevant healthcare use cases, including remote robotic-assisted surgery, connected ambulance, wearable and implantable devices, and service robotics for assisted living, with a focus on quantitative requirements. We also compare 5G-healthcare requirements with the current state of 5G capabilities. Finally, we identify gaps in the existing literature and highlight considerations for this space.
... Based on these streams, three network slice service types (SST) are defined in 5G standards [8] as shown in Table 1. Several advancements have already been made where the slicing technique is implemented at the transmission and core part of the network [9,10]. Slice implementation at the radio access network (RAN) is still an active area of research. ...
The vision of the 5G network is to provide wireless connectivity to different market verticals with a diverse quality of service requirements. To meet the requirements of these verticals, network resources at each layer (core, transmission, and radio access) of 5G architecture need efficient resource management. Network slicing is one of the key features of 5G networks where network resources form virtual sub-networks to handle diverse resource requirements from verticals. In this paper, we propose a framework using multi-objective Markov decision process that models radio resource management (RRM) for 5G radio access network slices. In particular, we present a multi-objective scheduler for 5G radio that allocates inter-slice radio resources efficiently for enhanced mobile broadband (eMBB) and ultra-reliable low latency communication (uRLLC) slices. Probabilistic model checking is used to analyze the performance of the scheduler and to perform quantitative verification. The proposed scheduler takes into account key design parameters such as mmWave radio channel condition and network load condition to optimize the performance of bandwidth greedy eMBB and latency sensitive uRLLC slices through appropriate joint resource allocation. Results show that the proposed scheduler provides optimal strategy synthesis for joint resource management of shared radio bandwidth in eMBB and uRLLC slices .
... Maintaining the reliability in the slice for mission-critical sevices is a challenging task which is addressed in [8] with media use-case applications and proposed a SliceNet model for customized and advanced network slicing framework to resolve highlighted challenges in migrating e-Health telemedicine application services in 5G networks. ...
Flexibility is one of the primary aims of 5G Networks to support various use-case applications in mobile networks. In this work we have given the concept of fine-grain network slices which means giving a slice for network traffic of specific application. The fine-grain network slices make network more flexible to analyze, update and detect application failures. In this work we proposed a 2-tier approach for allocating a secure slice using Random Forest method. The datasets used in this work are application traffic dataset which has 50 plus different application labels like google, youtube, facebook etc and DDoS attack dataset. Random Forest model is showing promising results to allocate the secure slice for the network traffic. Index Terms: software-defined networking, network function virtualization, 5G, network slicing, DDoS.
... For example, network slices for time-sensitive applications require the deployment of 5G network functions close to the users that require ultralow latency and high availability [14]. Similarly, network slices for multimedia streaming require high bandwidth capabilities, mobility support, and the deployment of content delivery network (CDN) resources [15]. ...
Network slicing is a key element in 5G networks. It allows a mobile network operator (MNO) to offer multiple logical networks tailored to the requirements of different industry verticals over a shared infrastructure. From the point of view of an MNO, a mobile virtual network operator (MVNO) is a particular type of vertical network requiring not only the provision of logical networks with specific infrastructure resources but also customized management capabilities to allow the MVNO to offer network slice-based services to its clients over the infrastructure of multiple MNOs. However, the lack of flexibility to provide a customized and efficient network slice management system per tenant under the current proposals of the 3rd Generation Partnership Project (3GPP) and European Telecommunications Standards Institute (ETSI) limits the possibilities of MVNOs. This paper addresses this requirement by proposing a novel virtualized multidomain and multiclient orchestration system aligned with the functionalities of the network slicing management framework proposed by the 3GPP and ETSI. In particular, there are three contributions of this paper: (i) an extended service-based architecture is designed to provide an isolated management system to different MVNOs; (ii) a novel high-level slice instance template is defined to specify management and isolation requirements supporting network slicing as a service model; and (iii) finally, a teleoperated driving use case is described to showcase how our proposal provides an MVNO with an independent management system to orchestrate several network slices in cross-domain environments.
... Also, the EU project SliceNet had the objective of provide a E2E cognitive network slicing and slice management framework in virtualised multi-domain, multi-tenant 5G networks. An overview of the SliceNet framework based on flexible and customise network slicing is presented in [5]. This contribution emphasises on the design and prototyping of an eHealth use case, focusing on the achievement of the E2E QoS-aware network slicing capabilities required, such as low latency and high reliability. ...
... A Tabela 1 apresenta as principais características dos trabalhos analisados. Considerando as estratégias de QoS empregadas pelos autores, podemos destacar quatro em comum, independentemente do foco da pesquisa: (i) protocolos de escalonamento [Samanta and Misra 2018, Iranmanesh and Rizi 2018, Venkatesh et al. 2019]; (ii) priorização de dados , Guezguez et al. 2018, Iranmanesh and Rizi 2018, Wang, Q. et al. 2019, Al-Tarawneh 2019, Venkatesh et al. 2019, Khalil et al. 2019, Goyal et al. 2020]; ...
Com a utilização de sensores inteligentes em hospitais, tomadas de decisões críticas podem ser realizadas baseadas no monitoramento em tempo real de pacientes e equipamentos. Porém, erros na geração e transmissão de dados podem causar falhas no resultado de aplicações que processam esses dados. Neste contexto, esse artigo propõem HealthStack, um middleware para salas cirúrgicas com estratégias de qualidade do serviço (QoS) e suporte a transmissão de dados em tempo real. O artigo propõe uma estratégia de QoS baseada em neurônios artificiais para seleção dos componentes do middleware com baixa performance. Foi desenvolvido e testado um protótipo do modelo em uma sala de cirurgia real possibilitando redução de jitter médio em até 90,3%.
... The priorities of the slice and user per service within the slice will be changed based on the state of the network in an unexpected situation such as disasters time that needs an emergency contact [117]. Furthermore, most of the published work in literature identifies three types of slices and many types of services for each slice [17], [40], [42], [82]. ...
In-network softwarization, Network Slicing provides scalability and flexibility through various services such as Quality of Service (QoS) and Quality of Experience (QoE) to cover the network demands. For the QoS, a set of policies must be considered in real-time, accompanied by a group of functions and services to guarantee the end-user needs based on network demand. On the other hand, for the QoE, the service’s performance needs to be improved to bring an efficient service to cover the demands of the end-user. The 3G Partnership Project (3GPP) defined the slice as a component of resources used to process a set of packets. These resources need to be flexible, which means the resources can be scaled up or down based on the demand. This survey discusses softwarization and virtualization techniques, considering how to implement the slices for future networks. Specifically, we discuss current advances concerning the functionality and architecture of the 5G network. Therefore, the paper critically evaluates recent research and systems related to mobility management as a service in real-time inter/intra slice control by considering the strengths and limitations of these contributions to identify the research gaps and possible research directions for emerging research and development opportunities. Moreover, we extend our review by considering the slice types and their numbers based on the 3GPP Technical Specification (3GPP TS). The study presented in this paper identifies open issues and research directions that reveal that mobility management at a service level with inter/intra slice management techniques has strong potential in future networks and requires further investigation from the research community to exploit its benefits fully.
Internet of things and smart medical applications are deeply changing the way healthcare is delivered worldwide. A typical Internet of Things (IoT)‐based eHealth system includes medical sensors for data collection, access network to transmit data, and Cloud servers for data processing and storage. Machine learning/deep learning (ML/DL) have proven to be a powerful tool for data classification, disease prognosis, and diagnosis and even medication prescription. ML/DL models need a large amount of data and significant computational and storage capacities especially for the training phase. Deploying ML/DL algorithms in the Cloud can be effectively done due to the processing and storage power of Cloud data centers. However, it raises many issues related to the availability, latency, energy consumption, bandwidth, security, and privacy. Recently, there is a growing interest to run as much processing as possible nearer the data sources, in the Edge, to compensate the Cloud‐based solutions limitations. In this paper, we propose to investigate the benefits of using IoT, ML/DL, and Edge Computing to enhance healthcare applications. Then, we are going to review the main approaches and trends for executing ML/DL in the Edge, to give their benefits and limitations, and draw finally conclusions about existing research issues.
5G networks are progressing to offer services with high Quality of Service (QoS) features like high user mobility skills, higher data rates, high reliable communiqué, and lower latency. For this, numerous novel settings like integration of Software-Defined Networking (SDN) and Network Function Virtualization (NFV) should be implemented in the network system. Here, this paper plans to design an optimization-based power allocation model to increase the Effective Power Efficiency (EPE) that offers guaranteed QoS over “Single-Input Single-Output (SISO) and Multiple-Input Multiple-Output (MIMO)-channel oriented 5 G networks”. Moreover, the statistical QoS-based green power allotment system was also analyzed to increase the EPE over other MIMO networks. Particularly, this paper makes an attempt to make the power allocation optimal, which directly ensures the 5 G network energy efficiency when guaranteeing QoS. This is considered to be the optimization issue, which is solved by MS-DA. Our suggested approach can be applied to hybrid energy supply networks with various network topologies, such as 5 G networks, which are now the most extensively used, and is capable of achieving a good resolution through iterative convergence.
In the current era of technology, the utilization of tablets and smart phones plays a major role in every situation. As the numbers of mobile users increase, the quality of service (QoS) and quality of experience (QoE) are facing the greater challenges. Thus, this can significantly reduce the latency and optimize the power consumed by the tasks executed locally. Most of the previous works are focused only on quality optimization in the dynamic service layouts. However, they ignored the significant impact of accurate access network selection and perfect service placement. This article performs the detailed survey of various MEC approaches with service provision and adoption. The survey also provides the analysis of various approaches for optimizing the QoS parameters and MEC resources. In this regarding, the survey classifies the approaches based on service placement, network selection, QoS, and QoE parameters, and resources such as latency, energy, bandwidth, memory, storage, and processing.
Network slicing is an essential technology for 5G mobile networks. It partitions network resource logically into multiple isolated slices, each of which can satisfy a suite of network requirements for one specific service. However, it cannot be fulfilled by the current SDN (Software-Defined Networks), since the conventional SDN data-plane technology, OpenFlow, is not flexible enough to offer fine-grained network resource control or queue/packet scheduling. It leads to many research studies developing corresponding solutions on programmable switches. In this work, we focus on the support of the bandwidth guarantee and management for network slices. Although several studies with the similar goal have been proposed, they do not consider interference among different flow types or use the built-in meter for easy deployment on COTS (Commercial Off-The-Shelf) P4 switches. To this end, we first conduct a case study to examine the interference cases. We then propose a solution, designated as P4-TINS (P4-driven Traffic Isolation for Network Slicing), to resolve the interference by isolating different types of traffic flows in priority queues and set the P4 switch’s bucket size based on the time granularity of its bandwidth management operation. It cannot only ensure the guaranteed bandwidth for each slice but also enable coexistent slices to fairly share residual bandwidth. We have confirmed its effectiveness experimentally based on our prototype over an ONOS (Open Network Operating System) controller and a COTS P4 switch.
Media services must ensure an enhanced user’s perceived quality during content playback to attract and retain audiences, especially while the streams are distributed remotely via networks. Thus, media streaming services rely heavily on good and predictable network performance when delivered to a large number of people. Furthermore, as the quality of media content gets high, the network performance demands are also increasing, and meeting them is challenging. Network functions devoted to improving media streaming services become essential to cope with the high dynamics of network performance and user mobility. Furthermore, new networking paradigms and architectures under the 5G networks umbrella are bringing new possibilities to deploy smart network functions, which monitor the media streaming services through live and objective metrics and boost them in real-time. This survey overviews the state-of-the-art technologies and solutions proposed to apply new network functions for enhancing media streaming.
This paper focuses on hybrid pipeline designs for User Plane Function and next-generation NodeB leveraging target-specific features and an insightful discussion of P4 and target challenges and limitations. The entire or disaggregated UPF runs on P4 targets and allocates packet processing data paths in P4 hardware or DPDK/x86 software based on flow characteristics (e.g., heavy hitters) and QoS requirements (e.g., low-latency slices). For the hybrid gNodeB, most packet processing is executed in commodity Tofino hardware, while unsupported functions such as Automatic Repeat Request and cryptography are performed in DPDK/x86. We show that our hybrid UPF improves the scalability by 18× and reduces latency up to 50%. The results also suggest that careful traffic allocation to pipeline targets is required to optimize each target's strength and avoid processing delays. Finally, we demonstrate a QoS-oriented application of the hybrid UPF and present gNodeB buffer service benchmarks.
Fifth-generation networks efficiently support and fulfill the demands of mobile broadband and communication services. There has been a continuing advancement from 4G to 5G networks, with 5G mainly providing the three services of enhanced mobile broadband (eMBB), massive machine type communication (eMTC), and ultra-reliable low-latency services (URLLC). Since it is difficult to provide all of these services on a physical network, the 5G network is partitioned into multiple virtual networks called “slices”. These slices customize these unique services and enable the network to be reliable and fulfill the needs of its users. This phenomenon is called network slicing. Security is a critical concern in network slicing as adversaries have evolved to become more competent and often employ new attack strategies. This study focused on the security issues that arise during the network slice lifecycle. Machine learning and deep learning algorithm solutions were applied in the planning and design, construction and deployment, monitoring, fault detection, and security phases of the slices. This paper outlines the 5G network slicing concept, its layers and architectural framework, and the prevention of attacks, threats, and issues that represent how network slicing influences the 5G network. This paper also provides a comparison of existing surveys and maps out taxonomies to illustrate various machine learning solutions for different application parameters and network functions, along with significant contributions to the field.
Healthcare 4.0 is a new concept that originates from the evolution of hospitals due to technological advances in medical activities. Nowadays, more and more doctors and healthcare administrators require real-time data analysis obtained from sensors and surgery monitoring. Using real-time information could make the difference between death and life in such settings. Therefore, quality of service (QoS) is essential in this context because, without it, the results of the applications become unreliable. Given the background, this article proposes HealthStack, a sensor middleware model for operating room facilities. HealthStack aims at improving delay and jitter from applications and, at the same time, reducing resource consumption. Our scientific contributions are twofold: a middleware for operating rooms with automatic QoS support for real-time data transmission; and a QoS strategy based on artificial neurons to select middleware components with critical performance. We developed a prototype that uses depth cameras and ultra-wideband (UWB) real-time location systems (RTLS) to monitor workflow during surgery. The evaluation demonstrates that the strategy improves the average jitter experienced by application by 92.3%. The results also reveal a reduction of network and CPU consumption by up to 61.8% and 8.3%.
Artificial intelligence (AI) algorithms are experiencing growing research interest due to their ability to improve decision making capabilities for promising applications in different economic sectors. The growing shift toward the Internet of Everything environments brought by devices embedded with sensors that can share information brings immense opportunity for new applications (apps). While these new apps thrive in resource-rich areas (i.e., capitals), neighboring cities that lack the resources and infrastructure to support them may be left behind. It is vital that new technologies can reach those who need them the most, especially healthcare-based. This article proposes an app-based approach for long-distance patient monitoring and care. The app would serve as a platform of communication between patients and healthcare staff, where the latter can send standardized video footage or pictures to the former (e.g., their primary care doctor). This feature is enhanced with a recurrent neural network algorithm as a validation tool for healthcare-related videos exchanged between patients and staff. Thus, the healthcare team does not need to check each video for validity, freeing their time for other activities.
Content Delivery Networks (CDNs) are becoming more critical due to the tremendous growth of video traffic. This paper proposes a complete framework targeting the creation of Information Centric Network (ICN) and IP slices for content delivery. Leveraging ICNs in-network caching advantages, our solution is tailored to a VNF placement con- text where ICN and IP slices are dynamically created over the physical infrastructure. Embedding QoS-constrained slices with high profits requires adjustments of the available resources based on the traffic and slice demands, which leads to reassigning the slices. However, the slice reassignment may lead to additional costs, content disruption in service delivery, and QoS violation. We formulate the problem as Integer Linear Programming (ILP) and propose a cost-efficient dynamic slicing heuristic. The objective is to maximize the profit of infrastructure providers while meeting the different QoS requirements of the slices. In our evaluation, in addition to validating the effectiveness of profit maximization, the impact of QoS violation on the infrastructure provider is analyzed. The results are compared to a work from the literature and an optimal solution. The evaluation shows that our proposed heuristic is promising and offers a near-optimal solution while improving profitability and ensuring QoS.
Through the concept of network slicing, a single physical network infrastructure can be split into multiple logically-independent Network Slices (NS), each of which is customized for the needs of its respective individual user or industrial vertical. In the beyond 5G (B5G) system, this customization can be done for many targeted services, including, but not limited to, 5G use cases and beyond 5G. The network slices should be optimized and customized to stitch a suitable environment for targeted industrial services and verticals. This paper proposes a novel Quality of Service (QoS) framework that optimizes and customizes the network slices to ensure the service level agreement (SLA) in terms of end-to-end reliability, delay, and bandwidth communication. The proposed framework makes use of network softwarization technologies, including software-defined networking (SDN) and network function virtualization (NFV), to preserve the SLA and ensure elasticity in managing the NS. This paper also mathematically models the end-to-end network by considering three parts: radio access network (RAN), transport network (TN), and core network (CN). The network is modeled in an abstract manner based on these three parts. Finally, we develop a prototype system to implement these algorithms using the open network operating system (ONOS) as a SDN controller. Simulations are conducted using the Mininet simulator. The results show that our QoS framework and the proposed resource allocation algorithms can effectively schedule network resources for various NS types and provide reliable E2E QoS services to end-users.
The exponential growth of mobile applications and services during the last years has challenged the existing network infrastructures. Consequently, the arrival of multiple management solutions to cope with this explosion along the end-to-end network chain has increased the complexity in the coordinated orchestration of different segments composing the whole infrastructure. The Zero-touch Network and Service Management (ZSM) concept has recently emerged to automatically orchestrate and manage network resources while assuring the Quality of Experience (QoE) demanded by users. Machine Learning (ML) is one of the key enabling technologies that many ZSM frameworks are adopting to bring intelligent decision making to the network management system. This paper presents a comprehensive survey of the state-of-the-art in the application of ML-based techniques to improve the ZSM performance. To this end, the main related standardization activities and the aligned international projects and research efforts are deeply examined. From this dissection, the skyrocketing growth of the ZSM paradigm can be observed. Concretely, different standardization bodies have already designed reference architectures to set the foundations of novel automatic network management functions and resource orchestration. Aligned with these advances, diverse ML techniques are being currently exploited to build further ZSM developments in different aspects, including multi-tenancy management, traffic monitoring, and architecture coordination, among others. However, different challenges, such as the complexity, scalability, and security of ML mechanisms are also identified, and future research guidelines are provided to accomplish a firm development of the ZSM ecosystem.
Remote patient monitoring, pre-hospital emergency care, tele-medicine, and tele-surgery are being under the limelight recently. Specially, in-ambulance tele-medicine is considered as a promising approach for improving health related emergency care. This emerging approach requires continuous monitoring of ambulance location and status of patient during the critical hour of patient transportation.
A centralized monitoring system is required to understand the physical and physiological condition of the patient. Hence, this urges the need for establishing communication between the staff of the ambulance and a monitoring station located in the hospital, in order to exchange high-definition ultrasound images of patients, reliable real time audio-video communication and texts for control commands and medical care advices.
The communication between the ambulance and monitoring station is achieved through a vehicular network, and requires low latency, stringent connectivity criteria and high reliability, that can be fulfilled by the Fifth Generation (5G) promising potential. In fact, 5G can provide high data rates, massive connectivity, ultra-reliability, high spectral efficiency and very low latencies.
5G presents an innovative solution called network slicing. The latter will enable network operators to provide highly secure dedicated virtual networks, to specific vertical customers over the same physical infrastructure.
The first objective of this chapter focuses on achieving high level of Quality of Service (QoS) guarantees for in-ambulance telemedicine communications. To this end, we design a specific 5G network slice, dedicated to monitoring connected ambulance, in order to achieve reliable real time communication between high-speed moving ambulance and monitoring station.
The proposed design of 5G slice for monitoring connected ambulance, is based on a vehicular network that allows users to roam across heterogeneous technologies (mainly IEEE 802.11p and LTE-V. In this divergent environment, with the high mobility of moving ambulance, exchanged messages should be delivered within a tight time window and with a reduced packet loss ratio.
Therefore, the second objective of the chapter is to present a handover management scheme dedicated for ambulance monitoring slice. First, the proposed solution is based on a handover algorithm that differentiates between two main types of handover: intra-slice handover and inter-slice handover. Second, a slice selection
algorithm is presented based on a sigmoid utility function.
The performance assessment of the proposed research solutions is evaluated through conducting a batch of simulations. Obtained results show the efficiency of our proposed scheme in terms of QoS guarantees.
Next-generation wireless networks are expected to enable new applications with strict latency constraints. However, existing transport layer protocols are unable to meet the stringent Quality of Service (QoS) requirements on throughput and maximum latency: excessive queuing due to capacity-oriented congestion control inflates end-to-end latency well beyond interactivity deadlines. In this work, we propose a novel framework that evolves best-effort communications into reliability- and latency-aware communications for QoS-sensitive applications. The new protocol, named High-reliability latency-bounded Overlay Protocol (HOP), provides a novel combination of packet-level Forward Error Correction (FEC) and multipath scheduling to compensate for capacity drops and meet pre-defined QoS requirements. More specifically, the sender splits the data and the associated redundancy between the paths by using a stochastic forecast of their future capacity and decides the amount of redundancy necessary to meet the application's requirements without clogging the connections. We compare HOP's performance with state-of-the-art multipath protocols in ns-3 simulations using both synthetic and live network traces, and confirm that our scheme can reliably deliver high-throughput data, reducing the number of late blocks by 2 to 5 times with respect to optimized Multipath TCP (MPTCP).
Driven by the quality of experience (QoE) requirement of video streaming applications in the smart city, smart education, immersive service, and connected vehicle scenarios, the existing network poses significant challenges, including ultra-high bandwidth, ultra-large storage, and ultra-low latency requirements, etc. Multi-access edge computing (MEC) is a potential technology, which can provide computation-intensive and caching-intensive services for video streaming applications to satisfy the requirement of QoE. Thus, focusing on video streaming schemes, a comprehensive summary of the state of the art applying MEC to video streaming is surveyed. Firstly, the related overview and background knowledge are reviewed. Secondly, resource allocation issues have been discussed. Thirdly, the enabling technologies for video streaming are summarized by taking account of caching, computing, and networking. Then, a taxonomy of MEC enabled video streaming applications is classified. Finally, challenges and future research directions are given.
Network slicing is one of the key enablers to provide the required flexibility and to realize the service-oriented vision toward fifth generation (5G) mobile networks. In that sense, virtualization, softwarization, and disaggregation are core concepts to accommodate the requirements of an end-to-end (E2E) service to be either isolated, shared, or customized. They lay the foundation for a multi-service and multi-tenant architecture, and are realized by applying the principles of software-defined networking (SDN), network function virtualization (NFV), and cloud computing to the mobile networks. Research on these principles requires agile and flexible platforms that offer a wide range of real-world experimentations over different domains to open up innovations in 5G. To this end, we present Mosaic5G, a community-led consortium for sharing platforms, providing a number of software components, namely FlexRAN, LL-MEC, JOX and Store, spanning application, management, control and user plane on top of OpenAirInterface (OAI) platform. Finally, we show several use cases of Mosaic5G corresponding to widely-mentioned 5G research directions.
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.
This whitepaper proposes OpenFlow: a way for researchers to run experimental protocols in the networks they use ev- ery day. OpenFlow is based on an Ethernet switch, with an internal flow-table, and a standardized interface to add and remove flow entries. Our goal is to encourage network- ing vendors to add OpenFlow to their switch products for deployment in college campus backbones and wiring closets. We believe that OpenFlow is a pragmatic compromise: on one hand, it allows researchers to run experiments on hetero- geneous switches in a uniform way at line-rate and with high port-density; while on the other hand, vendors do not need to expose the internal workings of their switches. In addition to allowing researchers to evaluate their ideas in real-world traffic settings, OpenFlow could serve as a useful campus component in proposed large-scale testbeds like GENI. Two buildings at Stanford University will soon run OpenFlow networks, using commercial Ethernet switches and routers. We will work to encourage deployment at other schools; and We encourage you to consider deploying OpenFlow in your university network too.
The Fifth-Generation (5G) networks, as the emerging next generation mobile networks, are adopting softwarization and virtualization technologies as the cornerstones for the network operators to gain significant competitive advantages by reducing both capital and operational expenditure, enabling agile and flexible service creation and deployment, among others. Meanwhile, a virtualized and softwarized 5G network would suffer from downgraded system performance due to this unprecedented paradigm shift towards software-based networking. Addressing one of the top challenges in this context, this paper focuses on improving the performance of the data plane from the edge to the core network segment (backhaul) in a 5G multi-tenant network by leveraging and exploring the programmability introduced by software-based networking. A fully functional prototype has been designed and implemented utilizing a Field Programmable Gate Arrays (FPGAs) acceleration-based platform, and the prototyped system has been empirically tested and evaluated to demonstrate the superior performance enhancements. The proposed solution can effectively support 5G networks in delivering mission-critical or time-sensitive applications such as ultra-high definition video use cases as experimentally validated and shown in this paper, by fulfilling the strict Quality of Service (QoS) requirements imposed to the data plane.
Network slicing has emerged as a major new networking paradigm for meeting the diverse requirements of various vertical businesses in virtualised and softwarised 5G networks. SliceNet is a project of the EU 5G Infrastructure Public Private Partnership (5G PPP) and focuses on network slicing as a cornerstone technology in 5G networks, and addresses the associated challenges in managing, controlling and orchestrating the new services for users especially vertical sectors, thereby maximising the potential of 5G infrastructures and their services by leveraging advanced software networking and cognitive network management. This paper presents the vision of the SliceNet project, highlighting the gaps in existing work and challenges, the proposed overall architecture, proposed technical approaches, and use cases.
Although the radio access network (RAN) part of mobile networks offers a significant opportunity for benefiting from the use of SDN ideas, this opportunity is largely untapped due to the lack of a software-defined RAN (SD-RAN) platform. We fill this void with FlexRAN, a flexible and programmable SD-RAN platform that separates the RAN control and data planes through a new, custom-tailored southbound API. Aided by virtualized control functions and control delegation features, FlexRAN provides a flexible control plane designed with support for real-time RAN control applications, flexibility to realize various degrees of coordination among RAN infrastructure entities, and programmability to adapt control over time and easier evolution to the future following SDN/NFV principles. We implement FlexRAN as an extension to a modified version of the OpenAirInterface LTE platform, with evaluation results indicating the feasibility of using FlexRAN under the stringent time constraints posed by the RAN. To demonstrate the effectiveness of FlexRAN as an SD-RAN platform and highlight its applicability for a diverse set of use cases, we present three network services deployed over FlexRAN focusing on interference management, mobile edge computing and RAN sharing.
The demand-led growth of datacenter networks has meant that many constituent technologies are beyond the budget of the wider community. In order to make and validate timely and relevant new contributions, the wider community requires accessible evaluation, experimentation and demonstration environments with specification comparable to the subsystems of the most massive datacenter networks. We demonstrate NetFPGA, an open-source platform for rapid prototyping of networking devices with I/O capabilities up to 100Gbps. NetFPGA offers an integrated environment that enables networking research by users from a wide range of disciplines: from hardware-centric research to formal methods.
Driven by the need to cope with exponentially growing mobile data traffic and to support new traffic types from massive numbers of machine-type devices, academia and industry are thinking beyond the current generation of mobile cellular networks to chalk a path towards fifth generation (5G) mobile networks. Several new approaches and technologies are being considered as potential elements making up such a future mobile network, including cloud RANs, application of SDN principles, exploiting new and unused portions of spectrum, use of massive MIMO and full-duplex communications. Research on these technologies requires realistic and flexible experimentation platforms that offer a wide range of experimentation modes from real-world experimentation to controlled and scalable evaluations while at the same time retaining backward compatibility with current generation systems. Towards this end, we present OpenAirInterface (OAI) as a suitably flexible platform. In addition, we discuss the use of OAI in the context of several widely mentioned 5G research directions.
The demand-led growth of datacenter networks has meant that many constituent technologies are beyond the budget of the wider community. In order to make and validate timely and relevant new contributions, the wider community requires accessible evaluation, experimentation and demonstration environments with specification comparable to the subsystems of the most massive datacenter networks. We demonstrate NetFPGA, an open-source platform for rapid prototyping of networking devices with I/O capabilities up to 100Gbps. NetFPGA offers an integrated environment that enables networking research by users from a wide range of disciplines: from hardware-centric research to formal methods.
Government ‘Unlikely’ to Scrap Delayed 4G ESN
- S Mccaskill
S. McCaskill. (2018) Government 'unlikely' to scrap delayed
4g esn. [Online]. Available: https://www.techradar.com/news/
government-unlikely-to-scrap-delayed-4g-esn
Four business models for mobile broadband public safety communications
- Nokia
NOKIA. (2016) Four business models for mobile broadband
public safety communications. [Online]. Available: https://
onestore.nokia.com/asset/182917