Heterogeneous Quality Of Service Research Articles

A QoS-aware framework for spectrum characterization and switching decision in cognitive radio networks

The increasing demand for seamless wireless services coupled with underutilization of limited spectrum due to fixed channel allocation policy have posed numerous challenges to wireless communications. There is need to design appropriate channel allocation strategies for continuous communication by mobile users for higher spectral efficiency. Cognitive radio network (CRN) offers a solution to the spectrum scarcity problem inherent in 4G and other networks through dynamic spectrum access, by allowing unlicensed secondary users (SUs) with cognitive devices to opportunistically access the spectrum holes when the licensed primary users (PUs) are not occupying them. Often times, measurements taken by SUs during the sensing process are uncertain due to multipath fading, shadowing and varying channel conditions. This results in imprecise spectrum detection and selection by SUs during switching decisions causing incessant spectrum handoff and undesirable ping-pong effect. In this paper, a support vector machine (SVM) classifier is used to categorize spectrum into two classes of busy and idle. Then, based on heterogeneous quality of service (QoS) requirements of the SUs, dynamic activities of PUs, and the fluctuating channel state information, a QoS-aware Adaptive Neuro-Fuzzy Inference System (ANFIS) framework is developed for spectrum switching decision using underlay spectrum access model. SVM predicted spectrum holes with 98.8% accuracy. ANFIS model yielded a 91.62% accuracy in the task of allocating spectrum holes to SUs for coexistent with PUs. Results further indicate that the intelligent framework can ensure fairness among SUs, reduce interference, improve throughput, and spectral efficiency. It can be deployed in disaster relief and emergency, public safety, and battlefield environments.

UniPreCIS: A data preprocessing solution for collocated services on shared IoT

Next-generation smart city applications, attributed to the power of the Internet of Things (IoT) and Cyber–Physical Systems (CPS), significantly rely on sensing data quality. With an exponential increase in intelligent applications for urban development and enterprises offering sensing-as-a-service these days, it is imperative that a shared sensing infrastructure could thwart the better utilization of resources. However, a shared sensing infrastructure that leverages low-cost sensing devices for a cost-effective solution remains unexplored territory. A significant research effort is still needed to make edge-based data shaping solutions more reliable, feature-rich, and cost-effective while addressing the associated challenges in sharing the sensing infrastructure among multiple collocated services with diverse Quality of Service (QoS) requirements. Towards this, we propose UniPreCIS, a novel edge-based data preprocessing solution that accounts for the inherent characteristics of low-cost ambient sensors and their exhibited measurement dynamics concerning application-specific QoS. UniPreCISaims to identify and select quality data sources by performing sensor ranking and selection that dynamically adapts to the change in sensor attributes. Finally, multimodal data preprocessing is performed in a unified manner to meet heterogeneous application QoS and, at the same time, reduce the resource consumption footprint for the resource-constrained network edge. We study the effectiveness of UniPreCISon a real-world testbed deployed on our campus. As observed, the processing time and memory utilization of the stakeholder services have been reduced in the proposed approach while achieving up to 90% accuracy, which is arguably significant compared to state-of-the-art sensing techniques.

DeSlice: An Architecture for QoE-Aware and Isolated RAN Slicing.

Network slicing is considered a key feature of 5G and beyond cellular systems. It opens the door for new business models of mobile operators, enables new services, reduces costs with advanced infrastructure-sharing techniques, and improves heterogeneous traffic service. With slicing, the operators can tailor the network resources to the requirements of specific verticals, applications, and corresponding traffic types. To satisfy the heterogeneous quality of service (QoS) requirements of various slices, efficient virtualization and resource allocation algorithms are required. Such algorithms are especially crucial for the radio access network (RAN) because of the spectrum scarcity. This article develops DeSlice, a novel architecture for RAN slicing. DeSlice enables efficient real-time slicing algorithms that satisfy heterogeneous QoS requirements of the slices and improve the quality of experience for their end users. The article illustrates the advantages of DeSlice by considering the problem of the joint service of cloud VR, video, and web traffic. It develops the algorithms using DeSlice architecture and application-to-network communication. With simulations, it shows that, together, the architecture and the algorithms allow greatly improving the QoE for these traffics significantly.

Energy Efficiency Optimization for MISO-NOMA SWIPT System with Heterogeneous QoS Requirements

This letter solves the energy efficiency optimization problem for the simultaneous wireless information and power transfer (SWIPT) systems with non-orthogonal multiple access (NOMA), multiple input single output (MISO) and power-splitting structures, where each user may have different individual quality of service (QoS) requirements about information and energy. Nonlinear energy harvesting model is used. Alternate optimization approach is adopted to find the solution, which shows a fast convergence behavior. Simulation results show the proposed scheme has higher energy efficiency than existing dual-layer iteration and throughput maximization methods.

On the Ergodic Rate of Cognitive Radio Inspired Uplink Multiple Access

With the exponential increase of the number of devices in the communication ecosystem toward the upcoming sixth generation (6G) of wireless networks, more enabling technologies and potential wireless architectures are necessary to fulfill the networking requirements of high throughput, massive connectivity, ultra reliability, and heterogeneous quality of service (QoS). In this work, we consider an uplink network consisting of a primary user (PU) and a secondary user (SU) and, by integrating the concept of cognitive radio and multiple access, two protocols based on rate-splitting multiple access and non-orthogonal multiple access with successive interference cancellation are investigated in terms of ergodic rate. The considered protocols aim to serve the SU in a resource block which is originally allocated solely for the PU without negatively affecting the QoS of the PU. We extract the ergodic rate of the SU considering a specific QoS for the PU for the two protocols and perform a qualitative and quantitative comparison between the two protocols. In the numerical results, we validate the theoretical analysis and illustrate the superiority of the considered protocols over two benchmark schemes.

Q2A-NOMA: A Q-Learning-Based QoS-Aware NOMA System Design for Diverse Data Rate Requirements

Wireless use cases in the industrial Internet of Things networks often require guaranteed data rates ranging from a few kilobits per second to a few gigabits per second. Supporting such a requirement in a single radio access technique is difficult, especially when bandwidth is limited. Although nonorthogonal multiple access (NOMA) can improve the system capacity by simultaneously serving multiple devices, its performance suffers from strong device interference. In this article, we propose a Q-learning-based algorithm for handling many-to-many matching problems, such as bandwidth partitioning, device assignment to sub-bands, interference-aware access mode selection [orthogonal multiple access or NOMA], and power allocation to each device. The learning technique maximizes system throughput and spectral efficiency (SE) while maintaining quality-of-service (QoS) for a maximum number of devices. The simulation results show that the proposed technique can significantly increase overall system throughput and SE while meeting heterogeneous QoS criteria.

Service Provisioning in Sliced Cloud Radio Access Networks

Network slicing- (NS-) based cloud radio access networks (C-RANs) have emerged as a key paradigm to support various novel applications in 5G and beyond networks. However, it is still a challenge to allocate resources efficiently due to heterogeneous quality of service (QoS) requirements of diverse services as well as competition among different network slices. In this paper, we consider a service provisioning allocation framework to guarantee resource utilization while ensuring the QoS of users. Specifically, an inter/intraslice bandwidth optimization strategy is developed to maximize the revenue of the system with multiple network slices. The proposed strategy is hierarchically structured, which decomposes into network-level slicing and packet scheduling level slicing. At the network level, resources are allocated to each slice. At the packet scheduling level, each slice allocates physical resource blocks (PRBs) among users associated with the slice. Numerical results show that the proposed strategy can effectively improve the revenue of the system while guaranteeing heterogeneous QoS requirements. For example, the revenue of the proposed strategy is 21% higher than that of the average allocation strategy.

Multiagent Deep-Reinforcement-Learning-Based Resource Allocation for Heterogeneous QoS Guarantees for Vehicular Networks

Vehicle-to-vehicle communications can offer direct information interaction, including security-centered information and entertainment information. However, the rapid proliferation of vehicles and the diversity of communications services demand for a more intelligent and efficient resource allocation framework to enhance network performance. In this article, a multi-agent deep reinforcement learning-based resource allocation framework is developed to jointly optimize the channel allocation and power control to satisfy the heterogeneous Quality-of-Service (QoS) requirements in heterogeneous vehicular networks. In the proposed framework, the utility maximization problem is formulated by considering two types of traffics, i.e., the strict ultrareliable and low-latency requirements for safety-centric applications and the high-capacity requirements for entertainment applications. The utility of each vehicular users is formulated as a multicriterion objective function by taking into account the heterogeneous traffic requirements. To overcome the drawbacks of the traditional totally centralized and distributed deep reinforcement learning-based resource allocation approaches, we propose a multi-agent deep deterministic policy gradient algorithm with centralized learning and decentralized execution to solve the formulated optimization problem. The normalization of the input states and reward functions is introduced to speed up the training and learning progress of the proposed algorithm. Simulation results show the superiority of the proposed algorithm in terms of the convergence and system performance through the comparison with the other methods and schemes for the delay-sensitive applications and delay-tolerant applications.

Evolutionary Computing based Web Service Composition Technique for Scheduling of Workload under Cloud Environment

Objectives: The objective of this model is to design a technique to schedule the workload for the computation of web services in the heterogeneous cloud environment. As the Web Service Composition (WSC) for the execution of scientific workload in a heterogeneous cloud computing environment is a challenging task. Modern workload requires dynamic resource provisioning technique as there exist parallelization among sub-tasks and different task demands different Quality of Service (QoS) requirement. Methods: This study presents an Evolutionary Computing based Web Service Composition (EC-WSC) technique to execute a large-scale scientific workload in a heterogeneous cloud environment. A multi-objective metric for improving energy efficiency and resource utilization is modelled. Then, an improved searching mechanism for the dragonfly evolutionary computing algorithm is modelled. Findings: Experiment outcomes show EC-WSC model attains superior performance in execution time performance analysis and energy efficiency performance analysis when compared with existing resource provisioning models of workload service composition such as Deadline and Budget-Aware Workflow Scheduling (DBAWS)(1), Evolutionary Computing Multi-objective optimization for Hybrid Clouds (EC-MOH)(2), Web Service Composition (WSC)(3), and Evolutionary Multi-Objective Optimization for clouds (EMOC)(4) in terms of heterogeneous computing, workload size, multi-objective optimization, QoS metric, and optimization strategy. Our model EC-WSC has proved to be more efficient in terms of energy efficiency by a reduction of 52.13% and also reduction in execution time by 71% when compared with the WSC(3) existing Web Service Composition model. Novelty: Existing resource provisioning predominantly focused on reducing computation cost and time; however, induces task execution latency and energy overhead. However, EC-WSC is modelled to utilize resources more efficiently and meet task QoS requirements by assuring energy minimization constraints. Keywords: Cloud Computing; Heterogeneous Computing Environment; Multiobjective optimization problem; Resource Provisioning; Workload Scheduling

An Optimization-Based Orchestrator for Resource Access and Operation Management in Sliced 5G Core Networks.

Network slicing is a promising technology that network operators can deploy the services by slices with heterogeneous quality of service (QoS) requirements. However, an orchestrator for network operation with efficient slice resource provisioning algorithms is essential. This work stands on Internet service provider (ISP) to design an orchestrator analyzing the critical influencing factors, namely access control, scheduling, and resource migration, to systematically evolve a sustainable network. The scalability and flexibility of resources are jointly considered. The resource management problem is formulated as a mixed-integer programming (MIP) problem. A solution approach based on Lagrangian relaxation (LR) is proposed for the orchestrator to make decisions to satisfy the high QoS applications. It can investigate the resources required for access control within a cost-efficient resource pool and consider allocating or migrating resources efficiently in each network slice. For high system utilization, the proposed mechanisms are modeled in a pay-as-you-go manner. Furthermore, the experiment results show that the proposed strategies perform the near-optimal system revenue to meet the QoS requirement by making decisions.

Spectrum sensing challenges & their solutions in cognitive radio based vehicular networks

SummaryVehicular ad hoc network (VANET) has been established to mitigate road collisions and traffic congestion and provide infotainment facilities to users. Allocated channels to VANET, which is the DSRC (Dedicated Short‐Range Communication), are not adequate for the full implementation of VANET. Cognitive radio (CR) can be used to alleviate this issue. CR is a programmable and intelligent radio system capable of reaching various frequency ranges. CR needs to conduct spectrum sensing to get these bands. A vehicle fitted with a CR can sense the licensed spectrum to locate the vacant spectrum (which is not used by any licensed user) when the DSRC is wholly occupied. Compared to other CR networks, VANET faces specific additional difficulties related to spectrum sensing, such as periodic topological shifts due to high‐speed mobility, multipath fading and shadowing issues, and heterogeneous quality of service (QoS) specifications. This paper explains these problems in depth. All the problems and issues were explored from the perspective of the CR focused on VANET. Probable ideas and directions to overcome these problems have also been presented. We have provided a conceptual framework for spectrum sensing. The framework resolves a variety of issues and concerns that are discussed in the paper. It gives much better results than conventional sensing techniques.

Harvesting Devices' Heterogeneous Energy Profiles and QoS Requirements in IoT: WPT-NOMA vs BAC-NOMA

The next generation Internet of Things (IoT) exhibits a unique feature that IoT devices have different energy profiles and quality of service (QoS) requirements. In this paper, two energy and spectrally efficient transmission strategies, namely wireless power transfer assisted non-orthogonal multiple access (WPT-NOMA) and backscatter communication assisted NOMA (BAC-NOMA), are proposed by utilizing this feature of IoT and employing spectrum and energy cooperation among the devices. Furthermore, for the proposed WPT-NOMA scheme, the application of hybrid successive interference cancelation (SIC) is also considered, and analytical results are developed to demonstrate that WPT-NOMA can avoid outage probability error floors and realize the full diversity gain. Unlike WPT-NOMA, BAC-NOMA suffers from an outage probability error floor, and the asymptotic behaviour of this error floor is analyzed in the paper by applying the extreme value theory. In addition, the effect of a unique feature of BAC-NOMA, i.e., employing one device's signal as the carrier signal for another device, is studied, and its impact on the diversity gain is revealed. Simulation results are also provided to compare the performance of the proposed strategies and verify the developed analytical results.

Auto-Scaling Network Service Chains Using Machine Learning and Negotiation Game

Network Function Virtualization (NFV) enables Network Operators (NOs) to efficiently respond to the increasing dynamicity of network services. Virtual Network Functions (VNFs) running on commercial off-the-shelf servers are easy to deploy, update, monitor, and manage. Such virtualized services are often deployed as Service Chains (SCs), which require in-sequence placement of computing and memory resources as well as routing of traffic flows. Due to the ongoing migration towards cloudification of networks, the concept of auto-scaling which originated in Cloud Computing, is now receiving attention from networks professionals too. Prior studies on auto-scaling use measured load to dynamically react to traffic changes. Moreover, they often focus on only one of the resources (e.g., compute only, or network capacity only). In this study, we consider three different resource types: compute, memory, and network bandwidth. In prior studies, NO takes auto-scaling decisions, assuming tenants are always willing to auto-scale, and Quality of Service (QoS) requirements are homogeneous. Our study proposes a negotiation-game-based auto-scaling method where tenants and NO both engage in the auto-scaling decision, based on their willingness to participate, heterogeneous QoS requirements, and financial gain (e.g., cost savings). In addition, we propose a proactive Machine Learning (ML) based prediction method to perform SC auto-scaling in dynamic traffic scenario. Numerical examples show that our proposed SC auto-scaling methods powered by ML present a win-win situation for both NO and tenants (in terms of cost savings).

Leveraging knowledge-as-a-service (KaaS) for QoS-aware resource management in multi-user video transcoding

The coexistence of parallel applications in shared computing nodes, each one featuring different Quality of Service (QoS) requirements, carries out new challenges to improve resource occupation while keeping acceptable rates in terms of QoS. As more application-specific and system-wide metrics are included as QoS dimensions, or under situations in which resource-usage limits are strict, building and serving the most appropriate set of actions (application control knobs and system resource assignment) to concurrent applications in an automatic and optimal fashion become mandatory. In this paper, we propose strategies to build and serve this type of knowledge to concurrent applications by leveraging Reinforcement Learning techniques. Taking multi-user video transcoding as a driving example, our experimental results reveal an excellent adaptation of resource and knob management to heterogeneous QoS requests, and increases in the amount of concurrently served users up to 1.24 × compared with alternative approaches considering homogeneous QoS requests.

Service and Resource Aware Flow Management Scheme for an SDN-Based Smart Digital Campus Environment

Recently, campuses have been embracing smart digital technologies in order to boost the efficiency of education and creativity. Thus, massive heterogeneous flows are generated as a result of multitude simultaneous access from several heterogeneous devices. This is putting pressure on campuses to make better management of their constrained resources and to ensure the required Quality of Service (QoS). In this paper, we propose a multi-flow management scheme over a software-defined smart digital campus network, named Service and Resource Aware Flow Management (SRAFM). Our approach offers a unified fully-programmable architecture, a distributed end-host-based flow characterization plane, and a centralized software-defined optimization model to efficiently manage heterogeneous flows. Network functionalities, including QoS aware routing and resource allocation optimization, are formulated as a mixed-integer linear programming problem. Due to its NP-hard complexity, we propose an approximation algorithm in a decomposed fashion based on Lagrangian Dual Decomposition (LDD) and subgradient methods to find an optimal solution for flow management. We evaluate our scheme from different aspects, including the number of simultaneous heterogeneous flows, QoS provisioning, characterization impacts, and network scalability. Compared to the well-known benchmarks in QoS aware routing and optimization problem, SWAY and LARAC, our simulation results conducted with a large number of flows over a small-scale network show promising performance. The proposed scheme significantly improves the cost reduction by 51% as compared to LARAC, the end-to-end delay by 21% and 34%, the bandwidth availability by 27% and 36%, and the QoS violation by 11% and 29% as compared to SWAY and LARAC, respectively.

Self-organised resource assignment for on-demand services in the cloud platform

It is more popular for a multimedia service provider (MCSP) to deploy many data centres (DCs) in different geographic locations over cloud for delivering video-on-demand (VoD) services to a lot of users. One primary task of the MCSP is to maximise its profit while guarantee the user's quality-of-service (QoS) requirements. However, the stochastic arrival of user requests and the capacity restriction of individual DC make resource management in distributed cloud more challenging than in a general cloud. We present a resource assignment strategy that can accommodate heterogeneous network resources and QoS demands by converting the request distribution problem into the constrained function optimisation problem. An online algorithm is developed and certified approximating to the optimum solution. Compared with other alternatives, our algorithm can cut down more than 35% of operational cost without degrading the QoS of end users.

Joint Power and Resource Block Allocation for Mixed-Numerology-Based 5G Downlink Under Imperfect CSI

Fifth-generation (5G) of wireless networks are expected to accommodate different services with contrasting quality of service (QoS) requirements within a common physical infrastructure in an efficient way. In this article, we address the radio access network (RAN) slicing problem and focus on the three 5G primary services, namely, enhanced mobile broadband (eMBB), ultra-reliable and low-latency communications (URLLC) and massive machine-type communications (mMTC). In particular, we formulate the joint allocation of power and resource blocks to the heterogeneous users in the downlink targeting the transmit power minimization and by considering mixed numerology-based frame structures. Most importantly, the proposed scheme does not only consider the heterogeneous QoS requirements of each service, but also the queue status of each user during the scheduling of resource blocks. In addition, imperfect Channel State Information (CSI) is considered by including an outage probabilistic constraint into the formulation. The resulting non-convex problem is converted to a more tractable problem by exploiting Big-M formulation, probabilistic to non-probabilistic transformation, binary relaxation and successive convex approximation (SCA). The proposed solution is evaluated for different mixed-numerology resource grids within the context of strict slice-isolation and slice-aware radio resource management schemes via extensive numerical simulations.

Client-Centric Access Device Selection for Heterogeneous QoS Requirements in Beyond 5G IoT Networks

The expected manifolds increase in the traffic and the massive number of connected Internet of Things (IoT) devices would be a challenge to ensure a certain Quality of Service (QoS) in Beyond 5G (B5G) networks. Due to the increased availability of processing and energy resources and heterogeneous QoS requirements in modern IoT nodes, a client-centric access device selection approach for QoS provisioning in multiple Radio Access Technologies (RATs) scenario is proposed in this paper. The proposed algorithm provides the ability to specify node-specific QoS requirements at each node, a better access device selection, and improved network scalability. For experimental evaluation, a hybrid indoor network consisting of Wireless Fidelity (WiFi) and Light Fidelity (LiFi) RATs has been considered. Experimental results show that the proposed technique outperforms several conventional client-based access device selection approaches by up to 32.66% in network emulation experiments and up to 50% in hardware experimentation. The contribution of this paper includes the proposed algorithm, its complexity and game-theory based convergence analyses, evaluation of the proposed algorithm using network emulation and hardware-based setups, and LiFi channel analysis. The analyses and results imply that the proposed algorithm could be utilized in next-generation IoT networks as it performs better than conventionally used access device selection techniques for QoS provisioning.

5MART: A 5G SMART Scheduling Framework for Optimizing QoS Through Reinforcement Learning

The massive growth in mobile data traffic and the heterogeneity and stringency of Quality of Service (QoS) requirements of various applications have put significant pressure on the underlying network infrastructure and represent an important challenge even for the very anticipated 5G networks. In this context, the solution is to employ smart Radio Resource Management (RRM) in general and innovative packet scheduling in particular in order to offer high flexibility and cope with both current and upcoming QoS challenges. Given the increasing demand for bandwidth-hungry applications, conventional scheduling strategies face significant problems in meeting the heterogeneous QoS requirements of various application classes under dynamic network conditions. This paper proposes 5MART , a 5G smart scheduling framework that manages the QoS provisioning for heterogeneous traffic. Reinforcement learning and neural networks are jointly used to find the most suitable scheduling decisions based on current networking conditions. Simulation results show that the proposed 5MART framework can achieve up to 50% improvement in terms of time fraction (in sub-frames) when the heterogeneous QoS constraints are met with respect to other state-of-the-art scheduling solutions.

LoRaWAN Modeling and MCS Allocation to Satisfy Heterogeneous QoS Requirements.

LoRaWAN infrastructure has become widely deployed to provide wireless communications for various sensor applications. These applications generate different traffic volumes and require different quality of service (QoS). The paper presents an accurate mathematical model of low-power data transmission in a LoRaWAN sensor network, which allows accurate validation of key QoS indices, such as network capacity and packet loss ratio. Since LoRaWAN networks operate in the unlicensed spectrum, the model takes into account transmission attempt failures caused by random noise in the channel. Given QoS requirements, we can use the model to study how the performance of a LoRaWAN network depends on the traffic load and other scenario parameters. Since in LoRaWAN networks the transmissions at different modulation and coding schemes (MCSs) typically do not collide, we use the model to assign MCSs to the devices to satisfy their QoS requirements.