Dynamic QoS Research Articles

A Generalized Nesterov's Accelerated Gradient-Incorporated Non-Negative Latent-Factorization-of-Tensors Model for Efficient Representation to Dynamic QoS Data

A Generalized Nesterov's Accelerated Gradient-Incorporated Non-Negative Latent-Factorization-of-Tensors Model for Efficient Representation to Dynamic QoS Data

UAVs-assisted QoS guarantee scheme of IoT applications for reliable mobile edge computing

Unmanned aerial vehicles (UAVs) assisted mobile edge computing (MEC) is an emerging network architecture that has been considered as a promising transformative service paradigm. It enhances the coverage of intelligent mobile communication network and promotes the rapid development of a wide range of internet of things (IoT) applications. However, the ever-increasing data transmission demands given rise by these IoT applications based on MEC have posed significant challenges to the service of quality (QoS) guarantee of UAVs-assisted MEC (UAVs-MEC) network. To address the issue, we propose a novel UAVs-MEC QoS guarantee scheme that can enhances the reliability of IoT applications in UAVs-MEC network. Specifically, a dynamic QoS-aware based service level agreement (SLA) compliance verification model for MEC service is proposed in the scheme. It can monitor and detect the consistency of dynamic QoS of IoT applications through UAVs to ensure the continuous SLA compliance of service provided by MEC. Additionally, an objective weight assignment method and an adaptive update method are presented to improve the accuracy and scalability of the scheme. Finally, the simulation experiments conducted using a real-world dataset show that the proposed scheme can efficiently and effectively verify the SLA compliance of MEC service for guaranteeing QoS of IoT applications in UAVs-MEC context while outperforms other existing SLA violation detection methods.

Dynamic QoS Aware Service Composition Framework Based on AHP and Hierarchical Markov Decision Making

Dynamic QoS Aware Service Composition Framework Based on AHP and Hierarchical Markov Decision Making

Dynamic QoS: Automatically Modifying QoS Queue's Maximum Bandwidth Rate-Limit of Network Devices for Network Improvement

The heterogeneous data traffic of today's network is a huge challenge to existing best-effort network technology, particularly in the context of large Ethernet, which handles hundreds to thousands of users. The existing conventional best-effort network technology is no longer efficient to handle the diversity of traffic types in the network and requires network management equipment such as Quality of Service (QOS). Usually, QOS is implemented on the gateway router. However, for better network performance and management, to guarantee high priority for sensitive traffic like video conferencing, Voice over Internet Protocol (VoIP), and streaming media within an internal network, it is nice to have QoS implemented on each router in the LAN network, starting from the access router to the gateway router. This paper is to demonstrate the effectiveness of the proposed dynamic QoS that has been developed and deployed in the LAN, purposely to provide adequate bandwidth for sensitive traffic when the network utilization is high and congested, by automatically modifying the QoS Queue's Maximum Bandwidth Rate-Limit of the best-effort traffic queue of the related router. The performance of the proposed developed dynamic QoS was evaluated via a comparison study before and after the dynamic QoS was presented in the network simulation environment that was built using Mininet. Results from the testing show that the developed dynamic QoS can improve the network's performance by automatically giving the appropriate bandwidth for sensitive traffic on the fly while needed/on demand.

Dynamic QoS: Automatically Modifying QoS Queue's Maximum Bandwidth Rate-Limit of Network Devices for Network Improvement

The heterogeneous data traffic of today's network is a huge challenge to existing best-effort network technology, particularly in the context of large Ethernet, which handles hundreds to thousands of users. The existing conventional best-effort network technology is no longer efficient to handle the diversity of traffic types in the network and requires network management equipment such as Quality of Service (QOS). Usually, QOS is implemented on the gateway router. However, for better network performance and management, to guarantee high priority for sensitive traffic like video conferencing, Voice over Internet Protocol (VoIP), and streaming media within an internal network, it is nice to have QoS implemented on each router in the LAN network, starting from the access router to the gateway router. This paper is to demonstrate the effectiveness of the proposed dynamic QoS that has been developed and deployed in the LAN, purposely to provide adequate bandwidth for sensitive traffic when the network utilization is high and congested, by automatically modifying the QoS Queue's Maximum Bandwidth Rate-Limit of the best-effort traffic queue of the related router. The performance of the proposed developed dynamic QoS was evaluated via a comparison study before and after the dynamic QoS was presented in the network simulation environment that was built using Mininet. Results from the testing show that the developed dynamic QoS can improve the network's performance by automatically giving the appropriate bandwidth for sensitive traffic on the fly while needed/on demand.

Retracted: Mobile Internet of Things Dynamic Grid QoS Service Matching Mechanism and Simulation Analysis

Retracted: Mobile Internet of Things Dynamic Grid QoS Service Matching Mechanism and Simulation Analysis

Profit-maximization spectrum sharing in opportunistic duopoly market under dynamic spectrum pricing and QoS constraints

Profit-maximization spectrum sharing in opportunistic duopoly market under dynamic spectrum pricing and QoS constraints

Dynamic QoS Management for a Flexible 5G/6G Network Core: A Step toward a Higher Programmability.

The academic and professional community has recently started to develop the concept of 6G networks. The scientists have defined key performance indicators and pursued large-scale automation, ambient sensing intelligence, and pervasive artificial intelligence. They put great efforts into implementing new network access and edge computing solutions. However, further progress depends on developing a more flexible core infrastructure according to more complex QoS requirements. Our research aims to provide 5G/6G core flexibility by customizing and optimizing network slices and introducing a higher level of programmability. We bind similar services in a group, manage them as a single slice, and enable a higher level of programmability as a prerequisite for dynamic QoS. The current 5G solutions primarily use predefined queues, so we have developed highly flexible, dynamic queue management software and moved it entirely to the application layer (reducing dependence on the physical network infrastructure). Further, we have emulated a testbed environment as realistically as possible to verify the proposed model capabilities. Obtained results confirm the validity of the proposed dynamic QoS management model for configuring queues’ parameters according to the service management requirements. Moreover, the proposed solution can also be applied efficiently to 5G core networks to resolve complex service requirements.

MQTT-Based QoS Model for IoT-M2M Critical Applications

The expeditious development of information technology provides opportunities for new remote and monitoring critical systems to be performed based on IoT technologies and M2M communications. This paper discusses important QoS issues in IoT systems and suggests a new QoS model for critical IoT applications, where each information must be delivered only once and in real-time. The proposal is based on the MQTT protocol with dynamic QoS handling, accordingly to the information importance. A prioritization scheme is adopted using different traffic classes, considering specific requirements for real-time communications and reliable operations while reducing end-to-end delay, packet loss, bandwidth, and energy consumption.

A QoS-Based routing algorithm over software defined networks

Video streaming requires reliable transmission and a steady network to satisfy the video users and applications QoS needs. The network service must provide and meet a certain level of performance to accommodating these QoS, specially in Smart City environments, but current Internet best-effort service does not guarantee QoS. Software-defined networking (SDN) is an approach intended to separate the network control and data (forwarding) planes to get better management and optimization, which clearly enhance the planning and development of smart urban communities. In this paper, we propose a dynamic QoS algorithm over SDN to select the optimum path that meets video QoS in order to optimize the quality of experience (QoE). We differentiate the traffic based on video resolution QoE parameters to Standard Definition (SD) and High Definition (HD). The results demonstrate that the proposed method obtained better viewing quality for an smart community environment and increases the overall network throughput.

QoE-Aware Traffic Aggregation Using Preference Logic for Edge Intelligence

Traffic flows with different requirements of quality of service (QoS requirements) are aggregated into different QoS classes to provide differentiated services (Diffserv) and better quality of experience (QoE) for users. The existing aggregation approaches/QoS mapping methods are based on quantitative QoS requirements and static QoS classes. However, they are typically qualitative and time-varying at the edge of the beyond fifth generation (B5G) networks. Therefore, the artificial intelligence technology of preference logic is applied in this paper to achieve an intelligent method for edge computing, called the preference logic based aggregation model (PLM), which effectively groups flows with qualitative requirements into dynamic classes. First, PLM uses preferences to describe QoS requirements of flows, and thus can deal with both quantitative and qualitative cases. Next, the potential conflicts in these preferences are eliminated. According to the preferences, traffic flows are finally mapped into dynamic QoS classes by logic reasoning. The experimental results show that PLM presents better performance in terms of QoE satisfaction compared with the existing aggregation methods. Utilizing preference logic to group flows, PLM implements a novel way of edge intelligence to deal with dynamic classes and improves the Diffserv for massive B5G traffic with quantitative and qualitative requirements.

Dynamic QoS Management for Interactive Multimedia in Integrated 4G/5G Networks

INTRODUCTION: Emerging interactive multimedia applications such as VR have some key requirements that communication medium must full fill in order to have a smooth interactive experience. These requirements include higher uplink and downlink throughput requirement, low latency, and consistent throug

Web service quality of service prediction via regional reputation‐based matrix factorization

AbstractQuality of Service (QoS) of Web services plays an essential role in selecting Web services by consumers. The dynamic QoS attributes of Web services have different values for different users. Therefore, the value of many Web services' QoS features for many users are undetermined, and these values should be predicted. The collaborative filtering (CF) method is one of the most successful approaches to predict these values. CF‐based methods use the QoS values contributed by the other users for prediction and, consequently, the values contributed by unreliable users can decrease the accuracy of prediction. To utilize the reputation of users can be regarded as one of the conventional approaches to overcome this problem. In this paper, we have defined a concept called regional reputation that represents the reputation of a user for users in each geographical region. Regional reputation has been achieved with the combination of the location information of the users and their reputation. Subsequently, by combining this concept with the matrix factorization, we have proposed a prediction method called regional reputation‐based matrix factorization. This approach has been able to improve the accuracy of prediction and be more persistent to the data contributed by unreliable users.

A Dynamic QoS Adjustment Enabled and Load-balancing-aware Service Composition Method for Multiple Requests

Previous QoS-aware service composition methods mainly focus on how to generate composite service with the optimal QoS efficiently for a single request. However, in the real application scenarios, there are multiple service requests and multiple service providers. It is more important to compose services with suboptimal QoS and maintain the load balance between services. To solve this problem, in this paper, we propose a service composition method, named as dynamically change and balancing composition method (DCBC). It assumes that the QoS of service is not static, and the services can adjust the value of QoS to gain more opportunities to be selected for composition. The method mainly includes two steps, which are the preprocessing step and the service selection step. In the preprocessing step, a backward global best QoS calculation is performed which regarding the static and dynamic QoS respectively; then guided by the global QoS, the feasible services can be selected efficiently in the service selection step. The experiments show that the DCBC method can not only improve the overall quality of composite services but also guarantee the fulfill ratio of requests and the load balance of services.

Service Discovery and Selection Based on Dynamic QoS in the Internet of Things

Web services are self-describing and self-contained modular applications based on the network. With the deepening of web service applications, service consumers have gradually increased their requirements for service functions and service quality. Aiming at how to select the optimal plan from a large number of execution plans with the same function and different QoS characteristics, this paper proposes a web service selection algorithm that supports QoS global optimization and dynamic replanning. The algorithm uses position matrix coding to represent all execution paths and replanning information of the service combination. By calculating the Hamming distance of the service quality between individuals, the quality of the service portfolio is improved. By specifying the total user time limit and implementing a good solution retention strategy, the problem of the impact of algorithm running time on service quality is solved. The experimental results show that the method proposed in this paper is effectively integrated into the development trend of QoS and close to the requester’s needs and can better meet user needs. This algorithm improves the user’s satisfaction with the returned service to a certain extent and improves the efficiency of service invocation.

Adjusting Learning Depth in Nonnegative Latent Factorization of Tensors for Accurately Modeling Temporal Patterns in Dynamic QoS Data

A nonnegative latent factorization of tensors (NLFT) model precisely represents the temporal patterns hidden in multichannel data emerging from various applications. It often adopts a single latent factor-dependent, nonnegative and multiplicative update on tensor (SLF-NMUT) algorithm. However, learning depth in this algorithm is not adjustable, resulting in frequent training fluctuation or poor model convergence caused by overshooting. To address this issue, this study carefully investigates the connections between the performance of an NLFT model and its learning depth via SLF-NMUT to present a joint learning-depth-adjusting scheme for it. Based on this scheme, a Depth-adjusted Multiplicative Update on tensor algorithm is innovatively proposed, thereby achieving a novel depth-adjusted nonnegative latent-factorization-of-tensors (DNL) model. Empirical studies on two industrial data sets demonstrate that compared with the state-of-the-art NLFT models, a DNL model achieves significant accuracy gain when performing missing data estimation on a high-dimensional and incomplete tensor with high efficiency. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —Multichannel data are often encountered in various big-data-related applications. It is vital for a data analyzer to correctly capture the temporal patterns hidden in them for efficient knowledge acquisition and representation. This article focuses on analyzing temporal QoS data, which is a representative kind of multichannel data. To correctly extract their temporal patterns, an analyzer should correctly describe their nonnegativity. Such a purpose can be achieved by building a nonnegative latent factorization of tensors (NLFT) model relying on a single latent factor-dependent, nonnegative and multiplicative update on tensor (SLF-NMUT) algorithm. But its learning depth is not adjustable, making an NLFT model frequently suffer from severe fluctuations in its training error or even fail to converge. To address this issue, this study carefully investigates the learning rules for an NLFT model’s decision parameters using an SLF-NMUT and proposes a joint learning-depth-adjusting scheme. This scheme manipulates the multiplicative terms in SLF-NMUT-based learning rules linearly and exponentially, thereby making the learning depth adjustable. Based on it, this study builds a novel depth-adjusted nonnegative latent-factorization-of-tensors (DNL) model. Compared with the existing NLFT models, a DNL model better represents multichannel data. It meets industrial needs well and can be used to achieve high performance in data analysis tasks like temporal-aware missing data estimation

BCOOL: A Novel Blockchain Congestion Control Architecture Using Dynamic Service Function Chaining and Machine Learning for Next Generation Vehicular Networks

This paper presents the first, novel, dynamic, resilient, and consistent Blockchain COngestion ContrOL (BCOOL) system for vehicular networks that fills the gap of trustworthy Blockchain congestion prediction systems. BCOOL relies on the heterogeneity of Machine Learning, Software-Defined Networks and Network Function Virtualization that is customized in three hybrid cloud/edge-based On/Offchain smart contract modules and ruled by an efficient and reliable communication protocol. BCOOL’s first novel module aims at managing message and vehicle trustworthiness using a novel, dynamic and hybrid Blockchain Fog-based Distributed Trust Contract Strategy (FDTCS). The second novel module accurately and proactively predicts the occurrence of congestion, ahead of time, using a novel Hybrid On/Off-Chain Multiple Linear Regression Software-defined Contract Strategy (HOMLRCS). This module presents a virtualization facility layer to the third novel K-means/Random Forest-based On/Off-Chain Dynamic Service Function Chaining Contract Strategy (KRF-ODSFCS) that dynamically, securely and proactively predicts VNF placements and their chaining order in the context of SFCs w.r.t users’ dynamic QoS priority demands. BCOOL exhibits a linear complexity and a strong resilience to failures. Simulation results show that BCOOL outperforms the next best comparable strategies by 80% and 100% reliability and efficiency gains in challenging data congestion environments. This yields to fast, reliable and accurate congestion prediction decisions, ahead of time, and optimizes transaction validation processing time. Globally, the Byzantine resilience, complexity and attack mitigation strategies along with simulation results prove that BCOOL securely predicts the congestion and provides real-time monitoring, fast and accurate SFC deployment decisions while lowering both capital and operational expenditures (CAPEX/OPEX) costs.

SARL: A reinforcement learning based QoS-aware IoT service discovery model

Abstract The IoT environment includes the enormous amount of atomic services with dynamic QoS compared with traditional web services. In such an environment, in the service composition process, discovering a requested service meeting the required QoS is a di cult task. In this work, to address this issue, we propose a peer-to-peer-based service discovery model, which looks for the information about services meeting the requested QoS and functionality on an overlay constructed with users of services versus service nodes, with probably constrained resources. However, employing a plain discovery algorithm on the overlay network such as flooding, or k-random walk could cause high message overhead or delay. This necessitates an intelligent and adaptive discovery algorithm, which adapts itself based on users’ previous queries and the results. To fill this gap, the proposed service discovery approach is equipped with a reinforcement learning-based algorithm, named SARL. The reinforcement learning-based algorithm enables SARL to significantly reduce delay and message overhead in the service discovery process by ranking neighboring nodes based on users’ service request preferences and the service query results. The proposed model is implemented on the OMNet simulation platform. The simulation results demonstrate that SARL remarkably outperforms the existing approaches in terms of message overhead, reliability, timeliness, and energy usage efficiency.

SDN-Controlled and Orchestrated OPSquare DCN Enabling Automatic Network Slicing With Differentiated QoS Provisioning

In this work, we propose and experimentally assess the automatic and flexible NSs configurations of optical OPSquare DCN controlled and orchestrated by an extended SDN control plane for multi-tenant applications with differentiated QoS provisioning. Optical Flow Control (OFC) protocol has been developed to prevent packet losses at switch sides caused by packet contentions.Based on the collected resource topology of data plane, the optical network slices can be dynamically provisioned and automatically reconfigured by the SDN control plane. Meanwhile, experimental results validate that the priority assignment of application flows supplies dynamic QoS performance to various slices running applications with specific requirements in terms of packet loss and transmission latency. In addition, the capability of exposing traffic statistics information of data plane to SDN control plane enables the implementation of load balancing algorithms further improving the network performance with high QoS. No packet loss and less than 4.8 us server-to-server latency can be guaranteed for the sliced network with highest priority at a load of 0.5.

Support Vector Regression based Mapreduce Throttled Load Balancer for Data Centers

The process of analyzing big data and other valuable information is a significant process in the cloud. Since big data processing utilizes a large number of resources for completing certain tasks. Therefore, the incoming tasks are allocated with better utilization of resources to minimize the workload across the server in the cloud. The conventional load balancing technique failed to balance the load effectively among data centers and dynamic QoS requirements of big data application. In order to improve the load balancing with maximum throughput and minimum makespan, Support Vector Regression based MapReduce Throttled Load Balancing (SVR-MTLB) technique is introduced. Initially, a large number of cloud user requests (data/file) are sent to the cloud server from different locations. After collecting the cloud user request, the SVR-MTLB technique balances the workload of the virtual machine with the help of support vector regression. The load balancer uses the index table for maintaining the virtual machines. Then, map function performs the regression analysis using optimal hyperplane and provides three resource status of the virtual machine namely overloaded, less loaded and balanced load. After finding the less loaded VM, the load balancer sends the ID of the virtual machine to the data center controller. The controller performs migration of the task from an overloaded VM to a less loaded VM at run time. This in turn assists to minimize the response time. Experimental evaluation is carried out on the factors such as throughput, makespan, migration time and response time with respect to a number of tasks. The experimental results reported that the proposed SVR-MTLB technique obtains high throughput with minimum response time, makespan as well as migration time than the state -of -the -art methods.