Real-time Quality Of Service Research Articles

Optimizing 6G Network Slicing with the EvoNetSlice Model for Dynamic Resource Allocation and Real-Time QoS Management

This research paper focuses on thoroughly examining the challenges in 6G network slicing. To develop, evaluate performance characteristics for on-demand reallocation and instantaneously changeable QoS EvoNetSlice model. The study employs integrated evolutionary algorithms with artificial intelligence-enabled data analytics and multi-objective optimization to optimize network resources usage under minimum end-to-end delay, high transmission rates and optimal background data management. Firstly, the network resource allocation individuals should be based on the network traffic data, QoD (quality of demand) value for some applications and users’ behaviors. The performance degradation detection and quality of service (QoS) adaptation mechanism combined with a multi-layer objective fitness function for achieving good balance in conflict between conflicting objectives. Results indicate that EvoNetSlice improves the general efficiency of a particular network, adapts according to ever shifting requirements for QoS at any time and provides crucial statistics-focused data on network management. The importance of this work lies in developing the future 6G network’s technology. W the key issues, including resource optimization and real-time adaptation required to support modern 6G services, are considered by EvoNetSlice. Such an exploration is an essential element in developing flexible 6G systems that will define next-generation wireless communication.

Data aggregation scheme for IOT based wireless sensor network through optimal clustering method

Wireless sensor network (WSN)based Internet of Things (IoT) has gained much popularity, in offering solutions to several real-time applications by interaction between sensor nodes and physical world entities through interconnected technology. WSN based IoT has become key technology in achieving real-time quality of service (QoS), low-cost operation and long-term reliability. IoT based WSN nodes, permits interconnection of various objects wirelessly and these nodes are tiny, equipped with irreplaceable battery and are resource constrain. Due to continuous sensing and gathering, IoT produce huge volume of data which may results in high computation overhead, data redundancy, packet collision and high energy consumption. To address resource constrains, significant research has been conducted to optimize energy consumption of nodes to extend network lifetime. Most of the existing methods focus on extending network lifetime by scheduling method through duty cycles, however these methods fails to handle data redundancy and has lower throughput. Cluster based data aggregation schemes have offered solution to eliminate data redundancy and harvest energy. In this paper we propose an efficient data aggregation scheme (EDAS) for IoT based WSN, this scheme considers improved low energy adaptive clustering algorithm (I-LEACH) to form optimal number of cluster head (CH), by considering node residual energy and average network energy. Data redundancy is eliminated using network coding, this technique integrates liner XOR operation and ensures non-replicated data transmissions. Finally, we performed simulations of proposed EDAS scheme to evaluate network parameters and performance of EDAS is compared with existing schemes.

Sustainability Model for the Internet of Health Things (IoHT) Using Reinforcement Learning with Mobile Edge Secured Services

In wireless multimedia networks, the Internet of Things (IoT) and visual sensors are used to interpret and exchange vast data in the form of images. The digital images are subsequently delivered to cloud systems via a sink node, where they are interacted with by smart communication systems using physical devices. Visual sensors are becoming a more significant part of digital systems and can help us live in a more intelligent world. However, for IoT-based data analytics, optimizing communications overhead by balancing the usage of energy and bandwidth resources is a new research challenge. Furthermore, protecting the IoT network’s data from anonymous attackers is critical. As a result, utilizing machine learning, this study proposes a mobile edge computing model with a secured cloud (MEC-Seccloud) for a sustainable Internet of Health Things (IoHT), providing real-time quality of service (QoS) for big data analytics while maintaining the integrity of green technologies. We investigate a reinforcement learning optimization technique to enable sensor interaction by examining metaheuristic methods and optimally transferring health-related information with the interaction of mobile edges. Furthermore, two-phase encryptions are used to guarantee data concealment and to provide secured wireless connectivity with cloud networks. The proposed model has shown considerable performance for various network metrics compared with earlier studies.

Dynamic QoS Prediction Algorithm Based on Kalman Filter Modification.

With the widespread adoption of service-oriented architectures (SOA), services with the same functionality but the different Quality of Service (QoS) are proliferating, which is challenging the ability of users to build high-quality services. It is often costly for users to evaluate the QoS of all feasible services; therefore, it is necessary to investigate QoS prediction algorithms to help users find services that meet their needs. In this paper, we propose a QoS prediction algorithm called the MFDK model, which is able to fill in historical sparse QoS values by a non-negative matrix decomposition algorithm and predict future QoS values by a deep neural network. In addition, this model uses a Kalman filter algorithm to correct the model prediction values with real-time QoS observations to reduce its prediction error. Through extensive simulation experiments on the WS-DREAM dataset, we analytically validate that the MFDK model has better prediction accuracy compared to the baseline model, and it can maintain good prediction results under different tensor densities and observation densities. We further demonstrate the rationality of our proposed model and its prediction performance through model ablation experiments and parameter tuning experiments.

Enabling Real-Time Quality-of-Service and Fine-Grained Aggregation for Wireless TSN.

Wireless Time-Sensitive Networking (WTSN) has emerged as a promising technology for Industrial Internet of Things (IIoT) applications. To meet the latency requirements of WTSN, wireless local area network (WLAN) such as IEEE 802.11 protocol with the time division multiple access (TDMA) mechanism is shown to be a practical solution. In this paper, we propose the RT-WiFiQA protocol with two novel schemes to improve the latency and reliability performance: real-time quality of service (RT-QoS) and fine-grained aggregation (FGA) for TDMA-based 802.11 systems. The RT-QoS is designed to guarantee the quality-of-service requirements of different traffic and to support the FGA mechanism. The FGA mechanism aggregates frames for different stations to reduce the physical layer transmission overhead. The trade-off between the reliability and FGA packet size is analyzed with numerical results. Specifically, we derive a critical threshold such that the FGA can achieve higher reliability when the aggregated packet size is smaller than the critical threshold. Otherwise, the non-aggregation scheme outperforms the FGA scheme. Extensive experiments are conducted on the commercial off-the-shelf 802.11 interface. The experiment results show that compared with the existing TDMA-based 802.11 system, the developed RT-WiFiQA protocol can achieve deterministic bounded real-time latency and greatly improves the reliability performance.

A Unified 3D Beam Training and Tracking Procedure for Terahertz Communication

Terahertz (THz) communication is considered as an attractive way to overcome the bandwidth bottleneck and satisfy the ever-increasing capacity demand in the future. Due to the high directivity and propagation loss of THz waves, a massive MIMO system using beamforming is envisioned as a promising technology in THz communication to realize high-gain and directional transmission. However, pilots, which are the fundamentals for many beamforming schemes, are challenging to be accurately detected in the THz band owing to the severe propagation loss. In this paper, a unified 3D beam training and tracking procedure is proposed to effectively realize the beamforming in THz communications, by considering the line-of-sight (LoS) propagation. In particular, a novel quadruple-uniform planar array (QUPA) architecture is analyzed to enlarge the signal coverage, increase the beam gain, and reduce the beam squint loss. Then, a new 3D grid-based (GB) beam training is developed with low complexity, including the design of the 3D codebook and training protocol. Finally, a simple yet effective grid-based hybrid (GBH) beam tracking is investigated to support THz beamforming in an efficient manner. The communication framework based on this procedure can dynamically trigger beam training/tracking depending on the real-time quality of service. Numerical results are presented to demonstrate the superiority of our proposed beam training and tracking over the benchmark methods.

A QoS Based Formal Model for Software Defined Network

Background & Objective: Software-Defined Networks (SDN) decouple the responsibility of data plane, control plane and aggregates responsibilities at the controller. The controller manages all the requests generated from distributed switches to get the optimal path for sending data from source to destination using load balancing algorithms. The guarantee of packet reachability is a major challenge in real time scenario of a SDN which depends on components of network infrastructure as switches, a central controller, channel capacity and server load. The success of this aggregation and packet reachability demand is a high Quality of Service (QoS) requirement in terms of throughput, delay and packet loss due to high traffic volume and network size. This QoS has two perspectives one is required other is a computation of real QoS value. Methods: In this paper, we have presented the QoS based formal model of SDN to compute and to investigate the role of the real QoS value. This formal model includes QoS on the basis of packet movement hop by hop which is a real-time QoS. The hop by hop packet movement reliability has been computed using channel capacity and server load which is an abstraction of throughput, delay, and packet loss. The effect of channel capacity and server load can be varying using different values of the weight factor. We have also considered an equal role of channel capacity and server load to compute reliability. This QoS helps to the controller to match with required QoS to decide the better path. Conclusion: Our results finds the reliable path based on channel capacity and server load of the network. Also, results showed that the reliability of the network and controller which are based on the reliability of the packet delivery between two nodes.

A Multi-Dimensional Intelligent Multiple Access Technique for 5G Beyond and 6G Wireless Networks

The ever-growing wireless applications and their diverse Quality of Service (QoS) requirements bring the challenge of tailored QoS provisioning with limited radio resources in future cellular networks. While resource constraint is ubiquitous, different communication equipment in cellular networks could experience very different constraints in the multi-dimensional resource domains. To achieve stringent yet diverse QoS with limited resources, a novel multi-dimensional intelligent multiple access (MD-IMA) scheme is proposed in this paper to exploit disparate resource constraints among heterogeneous equipment for 5G beyond and 6G networks. With the assist of real-time data analysis, real-time QoS requirements, and resource availability of the related equipment are first determined in the proposed MD-IMA. Based on this, multiple access (MA) scheme is then intelligently adapted accordingly for each equipment in multi-dimensional resource domain to maximize the overall system requirement with operational constraints. The resource allocation in the MD-IMA system is further formulated as an optimization problem. To solve this non-convexity optimization of high computational complexity, the overall optimization is divided into several sub-problems and a joint optimization algorithm is adopted. Simulation results demonstrate the system energy efficiency performance gain of proposed MD-IMA over traditional MA is around 15% - 18%.

A Reward Based Formal Model for Distributed Software Defined Networks

Software defined network (SDN) increases control on network infrastructures. It aggregates responsibilities of different hardware devices at higher level abstraction which is called as controller. The controller may face problem of serious failures. The guarantee of packet delivery in secure environment during data transmission is a major challenge using appropriate network policies. The mathematical model investigates the hidden issues in any system. The quality of service (QoS) has two views. One perspective is QoS requirement and other is real QoS during the data transmission. This QoS helps to find the accuracy of the controller decision. In this paper, we have proposed reward based formal model of distributed software defined networks using real time data (SDNR) to compute real time QoS during data transmission. We have separated time based QoS reliability from the other QoS like security and consistency level of network policy and these QoS have been incorporated collectively. We have considered all above three parameters as a reward for distributed SDN. The reliability is an abstraction of QoS parameters like latency, throughput, delay and link utilization which is computed at packet delivery time and captures the motive of other finer QoS parameters. In distributed SDN several controllers updates their data parallel in real time and they share Network Information Base of each other. The synchronization and the time is an important factor in the case of distributed SDN. Extended Time Automata is used to investigate role of reward in data transmission in distributed SDN in real time.

A New Methodology for Implementing the Data Distribution Service on Top of Gigabit Ethernet for Automotive Applications

Today’s vehicles have become increasingly complex, as consumers demand more features and better quality in their cars. Most of these new features require additional electronic control units (ECU) and software control, constantly pushing back the limits of existing architectures and design methodologies. Indeed, modern automobiles have a larger number of critical time functions distributed and running simultaneously on each ECU. Data Distribution Service (DDS) is a publish/subscribe middleware specified by the international consortium Object Management Group (OMG), which makes the information available in real time, while offering a rich range of quality of service (QoS) policies. In this paper, we propose a new methodology to integrate DDS in automotive application. We evaluate the performance of our new design by testing the fulfillment of real time QoS requirements. We also compare the performance of the vehicle application when using FlexRay and Ethernet networks. Computations prove that the use of DDS over Gigabit Ethernet (GBE) is promising in the automotive field.

Wireless Surveillance With Interference Exploitation in Unauthorized Underlaid D2D Networks

Interference exploitation has been considered an effective method to improve secrecy in underlaid device-to-device (D2D) communication networks. This paper studied interference exploitation in unauthorized underlaid D2D communication networks, where a cellular user (CU) transmits uplink information to a base station (BS) and a pair of suspicious D2D users (DUs) transmit the suspicious information at the same time. We used the beamforming of the full-duplex (FD) CU to simultaneously achieve the legitimate uplink transmission of the CU and wireless surveillance on the suspicious DUs. Considering the different priority levels of the tasks, we divided the real-time quality of service (QoS) requirements of the CU into three cases and formulated the problem in each case. We derived beamforming expressions for each problem in the perfect self-interference cancellation (PSIC) and then proposed targeted algorithms in the imperfect self-interference cancellation (ISIC). Finally, simulation results are provided to validate the effectiveness and the advantages of our proposed schemes in situations with different QoS requirements.

Onboard Spectral Analysis for Low-Complexity IoT Devices

The lack of coordinated spectrum access for IoT wireless technologies in unlicensed bands creates inefficient spectrum usage and poses growing concerns in several IoT applications. Spectrum awareness becomes then crucial, especially in the presence of strict quality-of-service (QoS) requirements and mission-critical communication. In this work, we propose a lightweight spectral analysis framework designed for strongly resource-constrained devices, which are the norm in IoT deployments. The proposed solution enables model-based reconstruction of the spectrum of single radio-bursts entirely onboard without DFT processing. The spectrum sampling exploits pattern-based frequency sweeping, which enables the spectral analysis of short radio-bursts while minimizing the sampling error induced by non-ideal sensing hardware. We carry out an analysis of the properties of such sweeping patterns, derive useful theoretical error bounds, and explain how to design optimal patterns for radio front-ends with different characteristics. The experimental campaign shows that the proposed solution enables the estimation of central frequency, bandwidth, and spectral shape of signals at runtime by using a strongly hardware-limited radio platform. Finally, we test the potential of the proposed solution in combination with a proactive blacklisting scheme, allowing a substantial improvement in real-time QoS of a radio link under interference.

SDN replaced deployment and real-time QoS provisioning based on network models

From industrial communications to real-time internet applications, end-to-end delay guarantee is one of the most important aspects of quality of service (QoS). As a new networking paradigm, software-defined networking (SDN) exposes an opportunity to provide this guarantee. In exploiting SDN, We can replace the legacy network devices to SDN forwarding units (SDN-FUs gradually. This paper focuses on the SDN replaced deployment problem, striving for providing real-time QoS provisioning in a network meanwhile. Utilising previous works originated from network calculus, we first propose the candidate nodes selection methods for upgrading legacy nodes to SDN-FUs and then present a routing algorithm located on SDN controller to direct the running of SDN-FUs. Via a simulation about a small network, we show that the delay of a flow passing through a network can reduce significantly with the usage of SDN.

A novel energy-aware utility maximization for efficient device-to-device communication in LTE-WiFi networks under mixed traffic scenarios

To meet the ever increasing data traffic demand from Device-to-Device (D2D) users, more mobile broadband bandwidth will be needed. Given the large amount of spectrum available in the unlicensed band, it is now being considered by the cellular operators as a complementary tool to augment their service offering. However, due to the uncontrollable interference from collocated WiFi networks, the available bandwidth for the D2D users cannot be guaranteed. In this paper, we introduce an approach for resource allocation to D2D users by aggregating spectrum resources from both licensed and unlicensed bands. An optimization framework that maximizes the utility functions of all the D2D pairs while minimizing the overall energy consumption when transmitting on licensed and unlicensed bands is proposed. Since the aforementioned spectrum and power allocation problem is NP-hard in nature, we therefore, propose a two-step framework for resource allocation to D2D pairs. In the first step, an efficient resource (LTE Resource Blocks (RBs) and unlicensed subchannels) assignment scheme is obtained by formulating a convex optimization problem, which maximizes the aggregate utility of D2D pairs while maintaining fixed transmission power on all the RBs/unlicensed subchannels. In the next step, a non-linear programming problem that maximizes the aggregate utility over sum energy consumption is formulated based on the spectrum resource allocation results obtained from the previous step. The optimization problem determines the transmission power on all the allocated RBs/subchannels by using primal-dual decomposition theory and the gradient update method. As a result of this, we observe that a user and the resource preferences can be considered while performing resource allocation. We further observe that in a network comprising of two category of users (viz., one who have subscribed to real time Quality of Service (QoS) services and second who have subscribed to Best Effort (BE) services), the proposed resource allocation scheme gives priority to QoS services when allocating resources. Extensive simulations and comparison with various benchmark schemes show that aggregating both licensed and unlicensed resources indeed improves the utility of the D2D pairs.

Context-Aware and Adaptive QoS Prediction for Mobile Edge Computing Services

Mobile edge computing (MEC) allows the use of its services with low latency, location awareness and mobility support to make up for the disadvantages of cloud computing, and has gained a considerable momentum recently. However, the dynamically changing quality of service (QoS) may result in failures of QoS-aware recommendation and composition of MEC services, which significantly degrades users’ satisfaction and negates the advantages of MEC. To address this issue, by considering user-related and service-related contextual factors and various MEC services scheduling scenarios, we propose two context-aware QoS prediction schemes for MEC services. The first scheme is designed for the situations when MEC services are scheduled in real-time, which contains two context-aware real-time QoS estimation methods. One method can estimate the real-time multi-QoS of MEC services and the other method can estimate the real-time fitted QoS of MEC services. The second scheme is designed for the situations when MEC services are scheduled in the future. This scheme includes two context-aware QoS prediction methods. One method can predict the multi-QoS of MEC services and the other method can predict the fitted QoS of MEC services. Finally, adaptive QoS prediction strategies are developed in the light of characteristics of the proposed QoS prediction methods. According to these strategies, the most appropriate QoS prediction method can be scheduled. Extensive experiments are conducted to validate our proposed approaches and to demonstrate their performance.

An autonomous resource provisioning framework for massively multiplayer online games in cloud environment

Massively Multiplayer Online Games (MMOGs) applications are a class of computationally intensive client-server multi-tier applications with real-time quality of service (QoS) requirements. In these online games, the players all over the world can interact with each other through a virtual environment. To guarantee the QoS requirements to a highly variable number of concurrent players, the game operators statically over-provision a large infrastructure capable of sustaining the game peak load, even though a large portion of the resources is unused most of the time and subsequently leads to incur high resource provisioning costs on MMOG providers. To address this problem, we introduce an autonomous resource-provisioning framework for MMOGs in a Cloud environment. Firstly, a load prediction service anticipates the future game-entity distribution from historical trace data using an adaptive neuro-fuzzy inference system (ANFIS) prediction model. Then, we proposed a fuzzy decision tree algorithm to estimate the proper number of resources to be allocated to each tier in the MMOG application using the predicted workload and user SLA. The experimental results under real and synthetic workloads indicate that the proposed solution outperforms in terms of accuracy and performance metrics compared with the other approaches.

Time-slotted software-defined Industrial Ethernet for real-time Quality of Service in Industry 4.0

Abstract Reliable and timely data delivery, namely, real-time communication, is a key aspect for achieving the agile manufacturing goals of Industry 4.0. Moreover, reducing the maintenance, infrastructure and reconfiguration costs, and increasing the flexibility are general goals for communication systems in Industry 4.0. Although some works have extended the Ethernet standard IEEE 802.3 to satisfy the real-time requirements of industrial automation systems, the existing works have one or more of the following drawbacks: poor scalability, insufficient self-configuration capabilities, or increased costs due to their use of proprietary hardware. In this paper, we propose a Time-slotted Software-defined Industrial Ethernet (TS-SDIE) for real-time Quality of Service (QoS) in Industry 4.0. The paper addresses two key technologies of TS-SDIE, time synchronization and time slot-based packet switching. First, we develop a precision time protocol-based link delay compensation mechanism to handle the delay variance in networks. Precise synchronization is achieved at the microsecond level. Second, we design a system architecture for time slot-based industrial switches with four modules responsible for time synchronization, flow classification, time slot assignment, and packet forwarding, respectively. The experimental results show that the switch delay and its jitter can be reduced to 12 . 2 μ s and 0 . 12 μ s , respectively, for packets with a size of 100 B, which satisfies the requirements of time-sensitive networking for industrial automation systems.

Toy-IoT-Oriented data-driven CDN performance evaluation model with deep learning

Abstract Content Delivery Network(CDN) is geographically distributed network of cache servers. It can deliver the Internet content based on the users’ geographic position and real-time quality of service(QoS). Now with the rapid development of the Internet of things, IoT also needs CDN acceleration. Therefore, CDN not only needs to serve people, but also needs to serve IoT, such as sensors,toys. Especially for toy computing, CDN need to sink further to support the uplink data transmission. Because the performance of CDN is crucial to the resource management of existing platform and the acceleration of data transmission, CDN providers use different models to exactly describe the performance of CDN. Traditional models are linear models or need to be adjusted manually. These normally existing drawbacks make the methods hardly to be applied stably. Recently, deep learning(DL) has made great breakthroughs in solving many problems. We use the RNN in deep learning to model the CDN performance. Our design can exactly capture the nonlinear relationship between high-dimensional machine data and the CDN performance. And it can also realize the correct prediction of the reach rate which is the CDN’ main performance evaluation index in our design.The experimental results have shown that our model is able to outperform state-of-the-arts models.

Energy aware frame based fair scheduling

Energy has now-a-days become a critical resource in all battery operated devices. Reduction of energy consumption is essential to prolong the battery life in these systems. A significant class of such systems often execute a mix of independent real-time applications with guaranteed Quality of Service (QoS) requirements. Proportional fair schedulers form a very effective mechanism for handling such application sets, with high resource utilisation on multiprocessor systems. In this paper, we propose a DVFS enabled proportional fair strategy called Energy Aware Frame Based Fair Scheduling (EAFBFS) for executing a set of real-time QoS aware tasks on multicores. In addition to providing appreciable energy savings, EAFBFS is able to deliver high and tunable fairness accuracy, low scheduling related overheads along with resource utilisation optimality, by combining and leveraging the benefits of two prominent state-of-the-art schedulers ERFair and DPFair. While the proposed scheduler ensures atmost m−1 task migrations over stipulated intervals called time frames similar to DPFair, EAFBFS exhibits far improved fairness properties. Experimental results show that EAFBFS performs almost at par with energy aware DPFair in terms of energy savings and migrations incurred while achieving much higher proportional fairness accuracy (10–15 times on average over DPFair).

Facilitating Dynamic RT-QoS for Massive-Scale Autonomous Cyber-Physical Systems

With the evolution of autonomous distributed systems such as smart cities, autonomous vehicles, smart control and scheduling systems there is an increased need for approaches to manage the execution of services to deliver real-time performance. As Cloud-hosted services are increasingly used to provide intelligence and analytic functionality to Internet of Things (IoT) systems, Quality of Service (QoS) techniques must be used to guarantee the timely service delivery. This paper reviews state-of-the-art QoS and Cloud techniques for real-time service delivery and data analysis. A review of straggler mitigation and a classification of real-time QoS techniques is provided. Then a mathematical framework is presented capturing the relationship between the host execution environment and the executing service allowing the response-times to predicted throughout execution. The framework is shown experimentally to reduce the number of QoS violations by 21% and provides alerts during the first 14ms provide alerts for 94% of future violations.