Adaptive 5G Network Slicing with Real-Time Traffic Classification and Experimental Validation

AFF3CT is an open source toolbox dedicated to Forward Error Correction (FEC or channel coding). It supports a broad range of codes: from widespread turbo codes and Low-Density Parity-Check (LDPC) codes to more recent polar codes. The toolbox is written in C++ and can be used either as a simulator to quickly evaluate algorithms characteristics, or as a library in Software Defined Radio (SDR) systems or for other specific needs. Most of the decoding algorithm implementations aim at low latency and high throughput, targeting multiple Gb/s on modern CPUs. This is crucial in both simulation and SDR use cases: Monte Carlo simulations require high performance implementation as they commonly target the estimation of approximately 1012 bits. On the other hand, the implementations in real systems have to be very efficient to be competitive against dedicated hardware ones. Finally, AFF3CT emphasizes the reproducibility of state-of-the-art results by providing public references and open, modular source code.

Network slicing is a technique for flexible resource provisioning in future wireless networks. With the powerful SDN and NFV technologies available, network slices can be quickly deployed and centrally managed, leading to simplified management, better resource utilization, and cost efficiency by commoditization of resources. Departing from the one-type-fits-all design philosophy, future wireless networks will employ the network slicing methodology in order to accommodate applications with widely diverse requirements over the same physical network. On the other hand, deciding how to efficiently allocate, manage, and control the slice resources in real time is very challenging. This article focuses on the algorithmic challenges that emerge in efficient network slicing, necessitating novel techniques from the communities of operation research, networking, and computer science.

In 5G networks, the deployment of network slices enabled by Software-Defined Networking (SDN) is becoming a critical component for delivering tailored services to meet diverse application needs. However, this introduces challenges in network management, particularly in efficiently allocating resources to ensure that each network slice meets its specific Quality of Service (QoS) and availability requirements. Simultaneously, it must optimize overall network performance and network operator’s profit, which is linked to the Quality of Experience (QoE) of the end-users. Existing works offer either an availability-based solution or a QoE-aware solution to this problem, but not both. This paper addresses the end-to-end network slice resource allocation problem by simultaneously considering QoS and availability requirements in slice placement, while employing a QoE-aware strategy for resource allocation. We propose a framework that optimizes the network operator profit i.e. the highest QoE with the least resource usage, and can be flexibly configured to model realistic scenarios. Arbitrary network slice requirements can be defined using slice-specific QoS/QoE mapping, resource requirements, end-to-end latency and availability. For solving the formulated problem a Mixed Integer Nonlinear Programming (MINLP) formulation and efficient heuristic methods are proposed. Our solution accounts for the non-linear QoS/QoE relationship, utilizes redundantly placed Service Function Chains (SFCs) to increase availability, and supports the sharing of Virtual Network Functions (VNFs) among SFCs to optimize resource usage. Through extensive simulations on realistic network topologies and slice requests, we demonstrate the framework’s effectiveness in offering flexible and efficient network slice placement and resource allocation, utilizing a baseline heuristic from related studies. The results indicate that while the exact method delivers an optimal solution, heuristic approaches are suitable for time-sensitive tasks, such as dynamic slice configuration.

There is an important class of applications in data centre called online dataintensive (OLDI) application, such as search engine, electronic shopping, and advertising, which has low latency, high throughput, and high burstiness. For better user experience, the query response delay in the data centre is controlled under 300 milliseconds. DCTCP can better meet the requirements of OLDI, but all the switches in the data centre are required to support RED. For data centre that does not fully support RED switch, TCP protocol can only be used. TCP Incast will appear in the application, which is far from the requirements of OLDI application. This paper proposes the TCP protocol of data centre based on window control. It is in existing switches and network devices,and centralized control makes the send window match with the switch queue cache. In the case of ensuring low latency, it keeps the switch queue in a small range of fluctuation, enabling the data centre to meet low latency, high throughput and high burstiness application requirements. Simulation experiments verify that WCTCP congestion control method can effectively solve the above problems. It is 5 times lower than TCPNewReno packet loss rate, 6 times higher than the network utilization rate, and the network performance is close to the DCTCP protocol.

Pipeline requires both low latency and high throughput. Synchronous pipeline achieves a near optimized throughput but suffers from the worst-case computation delay. Meanwhile, asynchronous pipeline keeps an optimized computation delay but suffers from the low throughput. In this paper, we present dual control path (DCP), a new structure of four-phase handshake asynchronous control path, to improve the throughput of asynchronous pipeline. We leverage the existing fully decoupled four-phase handshake elements and asymmetric delay elements to found DCP. Experimental results show the asynchronous pipeline with DCP could achieve high throughput as well as low computation delay, only with tiny area overhead.

Ensuring privacy of online messaging remains a challenge. While the contents or data of online communications are often protected by end-to-end encryption, the metadata of communications are not. Metadata such as who is communicating with whom, how much, and how often, are leaked by popular messaging systems today. In the last four decades we have witnessed a rich literature of designs towards metadata-protecting communications systems (MPCS). While recent MPCS works often target metadata-protected messaging systems, no existing construction simultaneously attains four desirable properties for messaging systems, namely (i) low latency, (ii) high throughput, (iii) horizontal scalability, and (iv) asynchronicity. Existing designs often capture disjoint subsets of these properties. For example, PIR-based approaches achieve low latency and asynchronicity but have low throughput and lack horizontal scalability, mixnet-based approaches achieve high throughput and horizontal scalability but lack asynchronicity, and approaches based on trusted execution environments (TEEs) achieve high throughput and asynchronicity but lack horizontal scalability. In this work, we present TEEMS, the first MPCS designed for metadata-protected messaging that simultaneously achieves all four desirable properties. Our distributed TEE-based system uses an oblivious mailbox design to provide metadata-protected messaging. TEEMS presents novel oblivious routing protocols that adapt prior work on oblivious distributed sorting. Moreover, we introduce the notion of ID and token channels to circumvent shortcomings of prior designs. We empirically demonstrate TEEMS' ability to support 2^20 clients engaged in metadata-protected conversations in under 1 s, with 205 cores, achieving an 18× improvement over prior work for latency and throughput, while supporting significantly better scalability and asynchronicity properties.

5G network is considered as a key enabler in meeting continuously increasing demands for the future Internet of Things (IoT) services, including high data rate, numerous devices connection, and low service latency. To satisfy these demands, network slicing and fog computing have been envisioned as the promising solutions in service-oriented 5G architecture. However, security paradigms enabling authentication and confidentiality of 5G communications for IoT services remain elusive, but indispensable. In this paper, we propose an efficient and secure service-oriented authentication framework supporting network slicing and fog computing for 5G-enabled IoT services. Specifically, users can efficiently establish connections with 5G core network and anonymously access IoT services under their delegation through proper network slices of 5G infrastructure selected by fog nodes based on the slice/service types of accessing services. The privacy-preserving slice selection mechanism is introduced to preserve both configured slice types and accessing service types of users. In addition, session keys are negotiated among users, local fogs and IoT servers to guarantee secure access of service data in fog cache and remote servers with low latency. We evaluate the performance of the proposed framework through simulations to demonstrate its efficiency and feasibility under 5G infrastructure.

Owing to their flexibility, fast deployment, increasing affordability, and numerous application opportunities, unmanned aerial vehicles (UAVs) became an undisputed target of studies and research to discover and exploit the immense opportunities they provide. Moreover, network slicing is a critical enabler for 5G and beyond. Network slices guarantee certain network user expectations such as data rate and latency thresh‐holds. There is no doubt that crowd‐sourced and incentive driven solutions are on the rise. Crowd‐sourcing allowed companies and organizations both small and large to gather data and perform other large‐scale tasks with minimum expenses and maintenance. Indeed, the major concerns for 5G and network slicing are guaranteeing high throughput, reliability, and latency. With data rates being the most important attribute for common regular users, that is, mobile devices, video streaming, navigation, and content consumption. As a result, the focus of this work is to utilize crowd‐sourced UAVs in an incentive driven network slice assistance system to extend coverage and provide mobile users with the high throughput they require. This work presents some basic 5G cellular radio access technologies and network slicing background and proposes a novel system, namely UAV slice assistance (UAVSA), and its hybrid alternative version: UAV hybrid slice assistance (UAVHSA). The hybrid mode combines benefits from the proposed UAVSA system and the traditional base station (BS) system. The proposed algorithms address the issue of coverage and guaranteeing high data rate slices for users. The proposed systems maximize satisfaction rate of network slice users. Moreover, all systems are extensively tested and compared in various scenarios to determine their performance and provide a coherent study. Finally, future works and opportunities are highlighted in conclusion.

Blockchain is a new technology for processing complex and disordered information with respect to business and other industrial applications. This work is aimed at studying the consensus algorithm of blockchain to improve the performance of blockchain. Despite their advantages, the proof of stake (POS) algorithm and the practical Byzantine fault tolerance (PBFT) algorithm have high latency, low throughput, and poor scalability. In this paper, a blockchain hybrid consensus algorithm which combines advantages of the POS and PBFT algorithms is proposed, and the algorithm is divided into two stages: sortition and witness. The proposed algorithm reduces the number of consensus nodes to a constant value by verifiable pseudorandom sortition and performs transaction witness between nodes. The algorithm is improved and optimized from three dimensions: throughput, latency, and scalability. The experimental results show that the improved hybrid consensus algorithm is significantly superior to the previous single algorithms for its excellent scalability, high throughput, and low latency.

Existing cellular communications and the upcoming 5G mobile network requires meeting high-reliability standards, very low latency, higher capacity, more security, and high-speed user connectivity. Mobile operators are looking for a programmable solution that will allow them to accommodate multiple independent tenants on the same physical infrastructure and 5G networks allow for end-to-end network resource allocation using the concept of Network Slicing (NS). Data-driven decision making will be vital in future communication networks due to the traffic explosion and Artificial Intelligence (AI) will accelerate the 5G network performance. In this paper, we have developed a ‘DeepSlice’ model by implementing Deep Learning (DL) Neural Network to manage network load efficiency and network availability, utilizing in-network deep learning and prediction. We use available network Key Performance Indicators (KPIs) to train our model to analyze incoming traffic and predict the network slice for an unknown device type. Intelligent resource allocation allows us to use the available resources on existing network slices efficiently and offer load balancing. Our proposed DeepSlice model will be able to make smart decisions and select the most appropriate network slice, even in case of a network failure.

Summary form only given. Nanoscale metal-semiconductor-metal (MSM) photodetectors and MESFETs are key elements in ultra-fast optical and wireless communication systems. However, devices with sub-0.25 /spl mu/m feature sizes are typically fabricated using electron-beam lithography (EEL), which has high cost and low throughput. Nanoimprint lithography (NIL), on the other hand, has both low cost and high throughput, as well as sub-l0 nm resolution. We report the fabrication of MSM photodetectors on semi-insulating (SI) GaAs using NIL, and the examination of NIL on the quality of the nanoscale Schottky contact. We found that if the imprint pressure is 600 psi or lower, nanoscale GaAs MSM photodetectors fabricated using NIL are comparable to those fabricated by conventional EEL and photolithography, indicating no degradation to the photodetector Schottky contact induced by the imprint process.

Circulating tumor cells (CTCs) are cancer cells shed from primary tumor and circulating in the peripheral blood. CTCs initiate secondary tumor colonies and result in more than 90% of cancer death. Isolation and enumeration of CTCs have exhibited promising clinical applications in cancer early diagnosis, prognosis and personalized therapy. However, owing to the extremely low rarity (as low as one CTC in one billion blood cells), CTCs isolation remains a great technical challenge. Current CTCs isolation approaches are based either on their biological properties, including immunoassay, magnetic-activated cell sorting, fluorescence-activated cell sorting, or physical properties, including filtering, inertial microfluidics, deterministic lateral displacement and etc. In general, biology-based techniques are characterized as high purity but low throughput; while physical property based techniques have advantages of low cost and high throughput. In this work, we reported the fabrication of a multilayer crossflow device and demonstrated the capability of isolating CTCs from whole blood based on lateral-flow filtration. The device was consisted of a filter membrane sandwiched between two microfluidic channel layers and fabricated with standard PDMS soft lithography and multilayer bonding technique. A 77.3% filtration efficiency with 4.5um polystyrene beads and a 97% cancer cells recovery rate have been achieved for a single-pass filtration. To further enhance the enrichment ratio(concentration of CTCs prior to filtration over that post-filtration) of CTCs over blood cells, a recirculating filtration system was built by combing the crossflow device with pressure pump and electromagnetic valves. In this system, 1 ml blood sample was treated for 10 times within 40 minutes, with an enrichment ratio of more than 104.

Due to the fact that the current variability of services is brought by the current networks and the new possibilities that will appear thanks to the near-future networks, Network Slicing has become one of the key elements to allow the co-existence of multiple computing and transportservices with different requirements (i.e., performance, security, isolation) over the same infrastructure in multi-tenant and multi-domain (i.e., edge, transport, core) scenarios. The use of this and other technologies allow to have only one generic infrastructure (e.g., an optical transport domain) despite the services differences, instead of needing specific resources (e.g., on single optical fiber) for each type of service. Multiple works have been published about Network Slicing, Network Function Virtualization and Software Defined Networks using multiple computing and transport domains but, based on our literature research, there is one important aspect with a low amount of attention: the security management around network slices and their enforcement. It is essential to ensure that the expected Quality of Security (QoSec) is accomplished based on the correct deployment and posterior monitoring of the security metrics defined in the agreed Security Service Level Agreement (SSLA) between the service requester and the provider.This article aims to present an architecture designed to manage and control the life-cycle of secured End-to-End (E2E) network slices involving multiple domains based on the SSLA requirements. The security management architecture is described with its components together with the deployment and monitoring processes and the data objects used. Finally, an experimental validation is described using the use case of a DoS attack scenario and its resolution.

5G networks will offer advanced functionalities and features such as high throughput, low latency, security and reliability by leveraging technologi-cal advances like network virtualization, edge computing and network slic-ing. The latter will facilitate the entrance of new players in the value chain. Moreover, they will offer a means for network operators to change their business models in order to address revenue losses. The concept of a neutral host supported by the H2020 5GCity project seems to be a strong candidate for future business models helping network operators thrive in the new telecom era.

Batch processing is an essential technique to improve packet rates under high-speed network, which has been widely used in high-performance packet IO frameworks. Conventionally, we need to make a tradeoff between high latency at low input loads and high throughput at high loads. In this work, we intend to leverage the smart batching mechanism to dynamically balance the input traffic rate and the available processing ability, so as to achieve both low latency and high throughput.%We conduct experimental investigations on the impact of different batching sizes on throughput, latency and CPU costs on our testbed which utilizesThe impact of different batching sizes on throughput, latency and CPU costs is investigated on our testbed which utilizes MoonGen and DPDK platform. We build a model for the batch processing procedure to explore various factors influencing latency and throughput. We find that batching size is a controllable variable linking the input rate and the available system processing rate. In light of this understanding, we design the smart batching mechanism to dynamically tune batching size according to the input traffic rate and the processing capability available. Smart batching mechanism is implemented and evaluated on DPDK platform under different traffic patterns and workload. The results show that it is sensitive and adaptive to the instantaneous states of network and server. Besides, it is simple enough and portable to most packet IO frameworks.