Quality Of Service Requirements Of Services Research Articles

A highly efficient resource slicing and scheduling optimization algorithm for power heterogeneous communication networks based on hypergraph and congruence entropy

New services, such as distributed photovoltaic regulation and control, pose new service requirements for communication networks in the new power system. These requirements include low latency, high reliability, and large bandwidth. Consequently, power heterogeneous communication networks face the challenge of maintaining quality of service (QoS) while enhancing network resource utilization. Therefore, this paper puts forward a highly efficient optimization algorithm for resource slicing and scheduling in power heterogeneous communication networks. Our first step involves establishing an architectural description model of heterogeneous wireless networks for electric power based on hypergraph. This model characterizes complex dynamic relationships among service requirements, virtual networks, and physical networks. The system congruence entropy characterizes the degree of matching between the service demand and resource supply. Then an optimization problem is formed to maximize the system congruence entropy through dynamic resource allocation. To solve this problem, a joint resource allocation and routing method based on Lagrangian dual decomposition is proposed. These methods provide the optimal solutions of the nodes and link mappings of service function chains. The simulation results demonstrate that the proposed algorithm in this paper can greatly enhance resource utilization and also meet the QoS requirements of various services.

Optimized QoS Routing in Software-Defined In-Vehicle Networks

To address the problems of low network centralized management, weak interaction, limited hardware scalability, compatibility issues and challenges in expansion in the static deployment of traditional in-vehicle networks (IVNs), a new IVN architecture is designed. At the same time, to better meet the IVN Quality of Service (QoS) requirements and improve the real-time guarantee of data transmission, the QoS routing optimization mechanism under the new architecture is established. First, a new IVN architecture including a forwarding plane, control plane and application plane is designed by introducing software defined network (SDN) technology and combining it with the IVN itself. Second, the end-to-end delay optimization model of IVN is established by introducing network calculus theory, defining system parameters, creating a network model, calculating latency, considering queuing and congestion. the traditional routing algorithm is improved, and the DBROA algorithm has been proposed, which enhances the performance of QoS routing by introducing features such as distributed routing decisions, beacon mechanisms, optimization algorithms, and adaptability. This improvement allows it to better meet the QoS requirements of various applications and services, thereby enhancing existing QoS routing algorithms. Finally, an IVN routing optimization system is built and implemented, and the performance of different algorithms is compared and analyzed. The experimental results show that compared with the traditional Dijkstra and ECMP algorithms, the DBROA algorithm can effectively reduce the data forwarding delay and packet loss rate, improve the overall performance of IVN, and provide better QoS guarantees for IVN real-time data transmission.

Automatic Spike Neural Technique for Slicing Bandwidth Estimated Virtual Buffer-Size in Network Environment

The Next-generation networks, such as 5G and 6G, need capacity and requirements for low latency, and high dependability. According to experts, one of the most important features of (5 and 6) G networks is network slicing. To enhance the Quality of Service (QoS), network operators may now operate many instances on the same infrastructure due to configuring able slicing QoS. Each virtualized network resource, such as connection bandwidth, buffer size, and computing functions, may have a varied number of virtualized network resources. Because network resources are limited, virtual resources of the slices must be carefully coordinated to meet the different QoS requirements of users and services. These networks may be modified to achieve QoS using Artificial Intelligence (AI) and machine learning (ML). Developing an intelligent decision-making system for network management and reducing network slice failures requires reconfigurable wireless network solutions with machine learning capabilities. Using Spiking Neural Network (SNN) and prediction, we have developed a 'Buffer-Size Management' model for controlling network load efficiency by managing the slice's buffer size. To analyze incoming traffic and predict the network slice buffer size; our proposed Buffer-Size Management model can intelligently choose the best amount of buffer size for each slice to reduce packet loss ratio, increase throughput to 95% and reduce network failure by about 97%.

QoS‐based routing scheme in software‐defined networks using fuzzy analytic hierarchy process

SummaryRecently, with the rapid development of the Internet of Things (IoT) and its real‐time applications, the network is required to guarantee the differential quality of service (QoS) requirements of the various data flows (telemedicine, video, and live streaming) of various IoT services. Software‐defined network (SDN) as emerging network technology separates the control logic from data planes of networks and is a promising technique to guarantee the QoS requirements of different services. In SDN, the controller has global visibility of the whole network devices such as switches and routers. Hence, controllers can dynamically optimize QoS requirements and resources of the network. On the other hand, providing a QoS‐aware routing scheme is an important challenge. Hence, in this paper by capitalizing on the fuzzy analytic hierarchy process (AHP), a new QoS‐aware routing method is proposed in SDNs. The proposed scheme considers the combination of different QoS criteria such as bandwidth, route length, reliability, and bit error rate for selecting an appropriate route between sender and receiver nodes. The simulation results indicated that the proposed method improves throughput and end‐to‐end delay. Comparison with other routing algorithms shows that the proposed method performs better load balancing under different network topologies.

A Cost-Aware Framework for QoS-Based and Energy-Efficient Scheduling in Cloud–Fog Computing

Cloud–fog computing is a large-scale service environment developed to deliver fast, scalable services to clients. The fog nodes of such environments are distributed in diverse places and operate independently by deciding on which data to process locally and which data to send remotely to the cloud for further analysis, in which a Service-Level Agreement (SLA) is employed to govern Quality of Service (QoS) requirements of the cloud provider to such nodes. The provider experiences varying incoming workloads that come from heterogeneous fog and Internet of Things (IoT) devices, each of which submits jobs that entail various service characteristics and QoS requirements. To execute fog workloads and meet their SLA obligations, the provider allocates appropriate resources and utilizes load scheduling strategies that effectively manage the executions of fog jobs on cloud resources. Failing to fulfill such demands causes extra network bottlenecks, service delays, and energy constraints that are difficult to maintain at run-time. This paper proposes a joint energy- and QoS-optimized performance framework that tolerates delay and energy risks on the cost performance of the cloud provider. The framework employs scheduling mechanisms that consider the SLA penalty and energy impacts of data communication, service, and waiting performance metrics on cost reduction. The findings prove the framework’s effectiveness in mitigating energy consumption due to QoS penalties and therefore reducing the gross scheduling cost.

Priority-based load-adaptive preamble separation random access for QoS-differentiated services in 5G networks

Fifth generation (5G) networks provide connectivity for several services including enhanced mobile broadband (eMBB), ultra-reliable low latency communication (URLLC) and massive Internet of things (mIoT). With the accelerated increase in the number of devices connected to the networks, and the diversity in quality-of-service (QoS) requirements, an appropriate modification in protocol design is required. Initial access in 5G networks is carried out based on the random access (RA) procedure through the random access channel (RACH). However, the limited resources, i.e. preambles, of the RACH causes intensive collisions among the massive number of contending devices, which result in low success probability and high access delay, causing failure to meet QoS requirements for different services. This paper introduces a priority-based load-adaptive preamble separation (PLPS) RA scheme in which RACH resources are separated between devices according to the service class priority and load estimation. The number of preambles assigned for each class of device is updated before each random access opportunity (RAO), based on the arrival load estimation, aimed at increasing the RACH throughput and therefore reducing the access delay for eMBB and URLLC devices. The proposed scheme is evaluated through a mathematical analysis model and extensive simulations. The results show that the proposed PLPS RA scheme succeeds in achieving the different QoS requirements of the three considered services, while increasing the RACH throughput by approximately 140%, compared to the benchmark which indicates the efficiency of the proposed scheme.

A cost-efficient IoT service placement approach using whale optimization algorithm in fog computing environment

The rapid development of Internet of Things (IoT)-based applications and the era of 5G networks has led to an exponential increase in the amount of data required for processing the IoT services. The fog computing paradigm has emerged as a distributed computing solution for serving these applications using available fog nodes near the IoT devices. Since the IoT applications are developed in the form of several IoT services with various quality of service (QoS) requirements that can be deployed on the fog nodes with different resource capabilities in the fog ecosystem, finding an efficient service placement plan is one of the challenging issues to be considered. In this paper, we propose an efficient IoT service placement solution based on the autonomic methodology for deploying IoT applications on the fog infrastructure. Our proposed solution monitors the QoS requirements of IoT services and capabilities of available fog nodes to determine an efficient service placement plan using the whale optimization algorithm (WOA) meta-heuristic technique. Besides, our evolutionary-based mechanism utilized the throughput and the energy consumption as objective functions for finding desirable IoT service placement plan while meeting the QoS requirements of each IoT service. Also, we develop an autonomous service placement framework according to a three-tier architecture of the fog ecosystem to show the interaction between the main components of the IoT device and fog layers for deploying IoT applications. The simulation results demonstrate that the proposed solution increases the resource usage and service acceptance ratio and reduces the service delay and the energy consumption compared with the other metaheuristic-based mechanisms.

Pr-CAI: Priority based-Context Aware Information scheduling for SDN-based vehicular network

The Intelligent Transport System (ITS) services recon upon reliable information dissemination among the heterogeneous vehicular network components along the roads. The information sharing among vehicles, pedestrian units and RoadSide Units (RSUs) is paramount in dynamic vehicular network to provide real-time data services in vehicular networks. Due to distinctive vehicular network characteristics, it is extremely challenging to provide efficient data services in heterogeneous traffic scenario. Firstly, the ITS services have different Quality of Service (QoS) constraints which varies in line with application specification. Secondly, there is a need of coordination among network resources in vehicular network. In this work, a Software Defined Network (SDN) based vehicular network model has been presented to provide reliable data services in heterogeneous traffic in urban environment. The Context Aware Information Scheduling (CAIS) problem has been formulated by analysing the characteristics and QoS requirements of ITS services. A Priority based Context Aware Information (Pr-CAI) scheduling algorithm has been proposed to ensure real time data service scheduling, which assigns priority to requests based on service deadline, request selection priority and traffic dynamics. The Pr-CAI has been simulated for different heterogeneous traffic scenarios to analyse its performance. The comprehensive simulation results prove the ascendancy of proposed algorithm with respect to the existing compared algorithms. The Pr-CAI scheduling algorithm ensures real time data service scheduling for different ITS services that maximizes the service ratio and provides fairness amongst users.

A QoS-guaranteed intelligent routing mechanism in software-defined networks

With the development of the Internet of Things (IoT), the network is required to guarantee the differential Quality of Service (QoS) requirements of the various data flows of various IoT services. Software-defined network (SDN) is envisioned as a promising technique to guarantee the QoS requirements of different services, through separating the control logic from data planes of networks. In order to guarantee the QoS requirements of data flows in SDNs, in this paper we investigate the problem of intelligent routing in SDNs, by leveraging a novel data flow classification method. Combining a variety of machine learning algorithms, a data flow classification method called MACCA2-RF&RF is proposed, in order to identify the data flow category and obtain the QoS requirements. The link parameter is newly designed considering multiple QoS requirements. According to the link parameter, the QoS-guaranteed path selection algorithm is then proposed, which can select QoS guaranteed routing path for different data flows with different QoS requirements. Aiming at the situation that the link is congested, local routing change algorithm is then proposed which only adjusts the links before and after the congested link instead of the entire path. Based on the above, a QoS-guaranteed intelligent routing mechanism called QI-RM in SDN is finally proposed in this paper to achieve QoS guarantee for data flows. The simulation results show that the MACCA2-RF&RF can classify data flows efficiently with an identification accuracy of 99.73%, and the QI-RM can guarantee the QoS requirements of data flows before and after link congestion.

QoS-Aware Flexible Handover Management in Software-Defined Mobile Networks

Handover support is one of the important issues in mobile networks to guarantee the quality of service (QoS) requirements for mobile users. Alongside the development of network technologies, handover management to provide service continuity has been researched and applied for the Internet or cellular networks such as 3G/4G/5G. However, each network paradigm provides its own individual handover management system, even though there are different kinds of QoS requirements for various mobile services. This causes inefficient network resource utilization from the network operators’ perspectives. Therefore, this paper proposes a QoS-aware flexible mobility management scheme for software-defined networking (SDN)-based mobile networks. The proposed scheme classifies flows into four classes based on the QoS requirements of services in terms of delay and loss tolerance. According to the classified service characteristics, it provides a differential handover method for each flow class to support efficient network operation without any service degradation by interacting between the forwarding plane nodes and SDN controller. The performance analysis shows that the proposed scheme enables flexible network resource utilization, satisfying the QoS requirements for each class well compared to the conventional schemes that only consider their own individual handover procedure.

Load-Balanced and QoS-Aware Software-Defined Internet of Things

Internet of Things (IoT) offers a variety of solutions to control industrial environments. The new generation of IoT consists of millions of machines generating huge traffic volumes; this challenges the network in achieving the Quality-of-Service (QoS) and avoiding overload. Diverse classes of applications in IoT are subject to specific QoS treatments. In addition, traffic should be distributed among IoT servers based on their available capacity. In this article, we propose a novel framework based on software-defined networking (SDN) to fulfill the QoS requirements of various IoT services and to balance traffic between IoT servers simultaneously. At first, the problem is formulated as an integer linear programming (ILP) model that is NP-hard. Then, a predictive and proactive heuristic mechanism based on time-series analysis and fuzzy logic is proposed. Afterward, the proposed framework is implemented in a real testbed, which consists of the Open vSwitch, Floodlight controller, and Kaa servers. To evaluate the performance, various experiments are conducted under different scenarios. The results indicate the improved IoT QoS parameters, including throughput and delay, and illustrate the nonoccurrence of overload on IoT servers in heavy traffic. Furthermore, the results show improved performance compared to similar methods.

Intelligent Quality of Service Aware Traffic Forwarding for Software-Defined Networking/Open Shortest Path First Hybrid Industrial Internet

Driven by the emerging advanced information and communication technologies, e.g., artificial intelligence, 5G wireless communications, big data analytics, etc., industrial Internet serves as a key enabling technology to realize intelligent manufacturing, and has been attracting considerable attentions from academia and industry. However, the traditional industrial networks can hardly satisfy the quality of service (QoS) requirements for some mission-critical industrial applications (e.g., fault detection, advanced control, remote monitoring, predictive maintenance, etc.) due to network heterogeneity, traffic congestion, dynamic end-to-end latency, reliability issues, and so on. The emerging software-defined networking (SDN) has been considered as a promising architecture to improve the QoS of industrial applications by flexibly decoupling the control and data planes to control the network behaviours centrally. Owing to economy and policy considerations, a realistic solution is to incrementally deploy SDN in industrial networks instead of fully replacing traditional industrial routers with SDN-enabled switches. In this article, we consider a hybrid Industrial network consisting of conventional routers (e.g., running OSPF protocol) and SDN-enabled switches (e.g., running OpenFlow protocol), and propose an intelligent QoS-aware forwarding strategy to improve the QoS of industrial applications, by utilizing a single path minimum cost forwarding scheme and a K-path partition algorithm for multipath forwarding. Simulation results demonstrate that the proposed scheme not only guarantees the QoS requirements of industrial services, but also efficiently utilizes bandwidth resources by balancing traffic load in the SDN/OSPF hybrid industrial Internet.

Resource Management in Cloud and Tactile-Capable Next-Generation Optical Access Networks

This paper introduces OCLDN, an agile, programmable, and scalable optical cloud distribution network, which exploits content distribution networks to deliver killer cloud and tactile services to end users at very high speeds, ultra-low latency, and low cost. With OCLDN, a software-defined-networking-based mini-cloud data center is installed at the central office of a next-generation passive optical network, which includes a cloud-based tactile steering server, and supports all types of cloud applications. These applications have stringent quality-of-service (QoS) requirements and, thus, require a fast, adaptive, and effective bandwidth allocation mechanism. Therefore, in this paper, we introduce a new dynamic wavelength and bandwidth allocation (DWBA) scheme that meets the QoS requirements of tactile services via dedicating it upstream wavelength(s), and also enables inter-channel statistical multiplexing so as to maximize the network throughput without impairing the QoS requirements for all types of services. Extensive simulations demonstrate the ability of the proposed DWBA to outperform existing schemes, and highlight its effectiveness in meeting the QoS demands for tactile and non-tactile services.

Heterogeneous Statistical QoS Provisioning Over Airborne Mobile Wireless Networks

Airborne mobile wireless networks (AMWNs), which use spacecrafts and aircrafts such as satellites, airships, airplanes, unmanned aerial vehicles, and other high/medium/low-altitude platforms (HAPs/MAPs/LAPs) can efficiently support high dynamic network topologies and weakly connected communication links. Due to the dramatic dynamics of the AMWNs, it is very difficult to provide the deterministic delay-bounded quality of service (QoS) provisioning for time-sensitive real-time traffics (such as video and audio) over the AMWNs. Alternatively, the statistical delay-bounded QoS provisioning provides an efficient way for guaranteeing delay-bounded QoS requirements for real-time traffics over the AMWNs. On the other hand, because of the diversity of real-time traffics, it is highly demanded to consider the heterogeneity of delay-bounded QoS requirements for distinct real-time services under different HAPs/MAPs/LAPs in the AMWNs. In this paper, we establish the heterogeneous statistical QoS provisioning framework to support the diverse real-time services over the AMWNs. In particular, we formulate the optimization problem to maximize the aggregate effective capacity subject to heterogeneous statistical delay-bounded QoS requirements for both downlink and uplink transmissions-based AMWNs groups (AMWNGs) in the AMWNs. We solve the aggregate effective capacity maximization problems and derive the optimal heterogeneous statistical QoS-driven power allocation schemes for the AMWNs. The numerical analyses we obtained verify that our developed optimal heterogeneous statistical QoS-driven power allocation schemes can significantly increase the aggregate effective capacity for the AMWNs than the other existing schemes.

Ring Optical Packet switched (OPS) network: Quality of Service (QoS) and traffic model

Optical Packet Switched (OPS) technology is under study as a promising solution for addressing the rapid growth of network-intensive applications having different Quality of Service (QoS) requirements. By employing Optical Circuit Switched (OCS) networks, an optimized Packet Loss Ratio (PLR), delay and Packet Delay Variation (PDV) performance can be obtained. It is, however, very demanding to offer a QoS comparable to OCS network through the OPS network in order to support a significant and growing number of applications. Thus, moving from OCS to OPS network, QoS is a critical issue. Hence, this paper analyzes the QoS requirements of network-intensive applications and services. Moreover, the paper derived an expression for performance metrics of a single OPS switch and ring OPS network. It is found that the International Telecommunication Union - Telecommunication (ITU-T) classification is extended with the new requirements of applications and services. For example, the upper bound delay, PDV and PLR of video streaming requires (100 ms, 400 ms), 50 ms and 1E−05 respectively. In this work, these values are improved as delay within 10 ms, PDV in micro seconds and PLR of 1E−07 at full load. It is also shown that a Fixed Delay Line (FDL) has to be considered when low PLR is required and when PDV and delay are not critical issues.

A traffic-differentiated routing algorithm in Flying Ad Hoc Sensor Networks with SDN cluster controllers

Recently, Flying Ad-hoc Sensor Networks (FASNETs), which are basically ad hoc networks between Unmanned Aerial Vehicles (UAVs), have been playing a great role in many sensing fields. To further improve the utilization of network resources, researchers offer the possibility of applying Software Defined Networking (SDN) technology to FASNETs, enabling various applications to be supported over the same platform. Since these concurrently executed applications might have diverse Quality of Service (QoS) requirements, it brings new challenges to network management and resource scheduling. In this paper, we propose a novel clustering FASNET architecture with SDN cluster controllers and also a collaborative controller, in order to realize hierarchical management and unified dispatch. Based on our designed architecture, we also propose a centralized traffic-differentiated routing (TDR) executed in each cluster, which aims to guarantee the specific QoS requirements of delay-sensitive and reliability-requisite services. Different weights are assigned to various flows according to their sensitivity to delay and also levels of importance. In particular, we introduce a transmission reliability prediction model to TDR, in which we consider both link availability and node’s forwarding ability. Simulation results show our proposed TDR has good performances in terms of end-to-end delay, packet dropping ratio and network throughput.

Toward efficient smartification of the Internet of Things (IoT) services

The Internet of Things (IoT) comprises several communication network technology standards and most of them are currently operating in silos. However, to achieve the IoT paradigm’s main goal, which is to deliver efficient and high-quality smart services, interoperation among various IoT standards is necessary. Therefore, interoperability and quality of service (QoS) provisioning are two of the main requirements for current and future standards operating within the IoT ecosystem. To understand how current standards can or cannot meet these requirements, first, we analyze high-level technical standards for some application domains within the IoT ecosystem. Second, with a focus on some common communication network standards, we present the QoS requirements of smart services in the IoT. Finally, we highlight the mechanisms employed by these standards in enabling interoperability and QoS in the IoT environment.

Software-Defined Vehicular Networks: Architecture, Algorithms, and Applications: Part 1

The papers in this special section focus on software defined vehicular networks.With the ever more rapid development of wireless communications and the explosive usage of mobile electronics, vehicular networks have become an attainable technology to meet the imminent demands for improving traffic safety and efficiency. In addition, there is an increasing demand from traveling users to access the Internet through IP-enabled smart devices, which enables infotainment applications to have rapidly taken on an important role in the past few years. Even though several future architectures have been proposed and investigated, it has been very challenging to coordinate vehicular networks to efficiently facilitate applications with diverse quality of service (QoS) demands. Recently, software-defined networking (SDN) has been emerging as a promising paradigm to control the network in a systematic way. The flexibility and programmability of SDN, which are lacking in today’s distributed wireless substrate, not only make it attractive to satisfy the QoS requirements of vehicular multimedia services, but also simplify the resource management in vehicular networks. Consequently, there is a need to conduct research to further investigate the standardization efforts and address challenging issues in the SDN enabled vehicular networks.

A Distributed Comprehensive-QoS Multicast Routing Algorithm on WSNs

This paper proposes a distributed integrated quality of service (QoS) multicast routing algorithm, which based on genetic simulated annealing algorithm, for the wireless sensor networks (WSNs).The basic idea is to grade the huge and complicated network, and decentralize resource requirements and the computational overhead of source routing algorithm to the various nodes. In this way, each node just needs to know partial information and make local decisions. In addition, simulated annealing algorithm is introduced into genetic algorithm (GA), whose cooling schedules will be more sophisticated through adjustment of temperature attenuation function, combined with improved adaptive crossover and mutation probability though and heuristic crossover and mutation strategy, which can overcome premature convergence and low searching efficiency in late evolution of the genetic algorithm, also improve evolution speed of the simulated annealing algorithm. The algorithm can meet different QoS requirements of services, and have better performance than the original algorithm.

Efficient Virtual Machine Resource Management for Media Cloud Computing

Virtual Machine (VM) resource management is crucial to satisfy the Quality of Service (QoS) demands of various multimedia services in a media cloud platform. To this end, this paper presents a VM resource allocation model that dynamically and optimally utilizes VM resources to satisfy QoS requirements of media-rich cloud services or applications. It additionally maintains high system utilization by avoiding the over-provisioning of VM resources to services or applications. The objective is to 1) minimize the number of physical machines for cost reduction and energy saving; 2) control the processing delay of media services to improve response time; and 3) achieve load balancing or overall utilization of physical resources. The proposed VM allocation is mapped into the multidimensional bin-packing problem, which is NP-complete. To solve this problem, we have designed a Mixed Integer Linear Programming (MILP) model, as well as heuristics for quantitatively optimizing the VM allocation. The simulation results show that our scheme outperforms the existing VM allocation schemes in a media cloud environment, in terms of cost reduction, response time reduction and QoS guarantee.