Quality Of Service Metrics Research Articles

A Metric-Based Approach for Quality Evaluation in Distributed Networking Systems

The distributed networking system is a dominant paradigm in the industry. In today's network industry, security and Quality of service (QoS) are two key services. The distributed network deals with an entire range of parameters coming from different applications. Therefore, it is of great significance to determine a set of key parameters and draw meaningful evaluation outcomes. On the other hand, security mechanisms provide user identification, authentication, consistency and information protection over the distributed environment. This research presents some analyses which draw connections between QoS and security of distributed networks. Then, a metric-based quantifying methodology is presented which quantifies network quality using QoS and security metrics. The QoS quantifying method is then further elaborated. The efficiency of this method is investigated using a range of simulation studies. The simulation analysis and results indicate that the proposed approach facilitates better QoS management and monitoring of the distributed networking systems.

DTE-SDN: A Dynamic Traffic Engineering Engine for Delay-Sensitive Transfer

With ever-rapid development of information and communication technologies, e.g., smart city, industrial Internet, Internet of Things, etc., enormous amounts of data are explosively generated and delivered to the computing center for further processing, which brings additional burden to both transfer components (e.g., the Internet) and data processing units. To efficiently schedule data transfer especially for delay-sensitive traffic, a scalable network architecture with intelligent traffic engineering (TE) policy is indispensable. In this paper, we propose a TE engine DTE-SDN by utilizing the software defined networking (SDN) technology, aiming to schedule the transfer of delay-sensitive traffic. Particularly, with OpenFlow, DTE-SDN captures an overall view of the network in real-time and can monitor the quality of service (QoS) metrics (e.g., throughput and delay) of each network link. In order to schedule the delay-sensitive transfer, a dynamic-scheduling scheme capable of multipath routing is proposed, which allows DTE-SDN to compute the near-optimal scheduling (e.g., path selection and flow distribution), based on the instantaneous QoS metrics. Especially, in the scheduling scheme, we propose a probabilistic-matching approach that aims to distribute the traffic among multiple end-to-end paths according to the computed flow distribution policy and can be deployed in SDN-enabled switches (e.g., Open vSwitch). The simulation results demonstrate that DTE-SDN is able to measure the throughput and delay with acceptable error range and can dramatically enhance the transfer efficiency than the traditional scheduling algorithms.

Subband CQI Feedback-Based Multicast Resource Allocation in MIMO-OFDMA Networks

Multicast transmission is one of the key enablers toward a more spectral- and energy-efficient distribution of multimedia content in current and envisaged cellular networks. In order to ensure that all users in a multicast group are able to correctly decode the received data, most multicast techniques adopt rather conservative strategies that select a very robust modulation and coding scheme (MCS) whose characteristics are determined by the propagation conditions experienced by the worst user in the group. Obviously, this robustness comes at the prize of a low spectral efficiency. Moreover, in the specific context of wideband communication systems, the selection of the multicast MCS has often relied on the use of wideband channel quality indicators (CQIs) providing rather imprecise information regarding the potential capacity of the multicast channel. Only recently has the per-subband CQI been used to improve the spectral efficiency of the system without compromising the link robustness. However, most subband-CQI multicast schemes proposed so far rely on overpessimistic assumptions that preclude the achievement of high data rates. In this paper, novel subband CQI-based multicast strategies are proposed that, relying on the selection of more spectrally efficient transmission modes, lead to increased data rates while still being able to fulfill prescribed quality of service metrics. To this end, a constrained optimization problem is posed that seeks to maximize the data rate of the whole multicast group while ensuring that the average block error rate for all users remains below a threshold and that a minimum data rate is guaranteed for all users in the group.

Smart Process Optimization and Adaptive Execution with Semantic Services in Cloud Manufacturing †

A new requirement for the manufacturing companies in Industry 4.0 is to be flexible with respect to changes in demands, requiring them to react rapidly and efficiently on the production capacities. Together with the trend to use Service-Oriented Architectures (SOA), this requirement induces a need for agile collaboration among supply chain partners, but also between different divisions or branches of the same company. In order to address this collaboration challenge, we propose a novel pragmatic approach for the process analysis, implementation and execution. This is achieved through sets of semantic annotations of business process models encoded into BPMN 2.0 extensions. Building blocks for such manufacturing processes are the individual available services, which are also semantically annotated according to the Everything-as-a-Service (XaaS) principles and stored into a common marketplace. The optimization of such manufacturing processes combines pattern-based semantic composition of services with their non-functional aspects. This is achieved by means of Quality-of-Service (QoS)-based Constraint Optimization Problem (COP) solving, resulting in an automatic implementation of service-based manufacturing processes. The produced solution is mapped back to the BPMN 2.0 standard formalism by means of the introduced extension elements, fully detailing the enactable optimal process service plan produced. This approach allows enacting a process instance, using just-in-time service leasing, allocation of resources and dynamic replanning in the case of failures. This proposition provides the best compromise between external visibility, control and flexibility. In this way, it provides an optimal approach for business process models’ implementation, with a full service-oriented taste, by implementing user-defined QoS metrics, just-in-time execution and basic dynamic repairing capabilities. This paper presents the described approach and the technical architecture and depicts one initial industrial application in the manufacturing domain of aluminum forging for bicycle hull body forming, where the advantages stemming from the main capabilities of this approach are sketched.

Statistical validation of an LTE emulation tool using live video streaming over reliable transport protocols

Multimedia services such as Live Video-Streaming (LVS) or Video on Demand are the main drivers behind the notable traffic growth in cellular networks. This fact has attracted the interest of both academia and telcos in improving the end user’s quality of experience. For this reason, the development of test benches for conducting traffic studies in this type of telecommunication system is necessary and emulators are a good option to validate possible solutions without affecting the real systems. However, most works addressing the emulation of Long-Term Evolution (LTE) systems do not validate their outcomes with real environments and hence ignore critical parameters such as the impact of wireless propagation under different scenarios. Therefore, the goal of this paper is to validate an outdoor propagation model for LVS so that accurate studies of traffic characterization in LTE networks can be carried out using an emulation tool. The validation is done through a statistical analysis of variance (ANOVA) of Quality of Service metrics, comparing the results obtained in an LTE-emulated scenario with those obtained in a real LTE test bench. For this study, the LVS service runs on top of a reliable transport protocol. Specifically, we test the Real-Time Message Protocol and the MPEG Dynamic Adaptive Streaming over HTTP (DASH) protocol, both using TCP at the transport layer. According to the results, the propagation model that better matches the real environment under the considered conditions is the Nakagami-m propagation model with m = 5.

On QoS evaluation for ZigBee incorporated Wireless Sensor Network (IEEE 802.15.4) using mobile sensor nodes

The design of an efficient and scalable Wireless Sensor Network (WSN) to accommodate the fluctuations in topology, node-mobility, node-density, and network-size is an exigent task. A meticulous investigation is necessitated to implement the sensor nodes in an appropriate topology at optimum node-mobility in ZigBee incorporated WSN networks to offer optimum Quality of Services (QoS). Subsequently, an attempt to compute the comprehensive recital of a non-beacon mode based 802.15.4/ZigBee integrated WSN network is demonstrated. An analytical model is designed using parameters such as back-off number, retransmission limit, and back-off exponent considering the impact of node-mobility which has not reported earlier. Further, the investigation is carried out experimentally by evaluating the different QoS metrics, for instance, throughput, network load, bit error rate (BER), received power, signal to noise ratio (SNR) and end-to-end delay for diverse node-density and network-size of a mobile Wireless Sensor Network. Based on the measurements obtained, the authors recommend implementing the mobile sensor nodes in a cluster-tree fashion to afford the best possible QoS services.

Performance analysis of relay subset selection schemes for underlay relay‐assisted CRNs

In underlay cognitive radio networks (CRNs), secondary users (SUs) are allowed to co-exist with the primary users at the cost of reduced transmit power in order to satisfy the stringent interference constraint. This obligation reduces the coverage area of the SUs and they have to involve relaying services for assistance. However, selecting the best combination of relays from the available options is more challenging in an energy-constrained environment, since it is affected not only by the interference constraint, but also the selected subset of relays must guarantee some quality-of-service (QoS) at the secondary destination also. This study is focused on an amplify-and-forward-based relay-assisted CRN operating in an underlay scenario and studies three relay subset selection or multiple relay selection schemes taking into consideration outgoing secondary and primary link qualities of each candidate relay. For each scheme, the closed-form expression for probability density function of signal-to-noise ratio at the secondary destination is derived followed by detailed analysis of two QoS metrics, i.e. outage probability and bit error rate. Finally, simulations have been performed to validate the analytical results.

Towards complex dynamic fog network orchestration using embedded neural switch

ABSTRACT Cloud data centers used for High Performance Computing (HPC) with volatile Internet of Things (IoT) devices absolutely requires zero-speed switching/low latency, normalized throughput, network stability with near zero packet drops during heavy traffic workload. Deadlock traffic condition found in baseline Fog-nodes results when there is no dynamic provisioning of services at Fog layer. In this case, Quality of Service (QoS) metrics of Service Level Agreements (SLA) are violated. Motivated by these concerns, this paper proposes Smart Hierarchical Network (SHN) as a reliable Fog dynamic design structure based on Software Defined Artificial Neural Network (SD-ANN). It features congestion-aware neural switch model with embedded predictive receding horizon for intelligent congestion management. The goal is to exploit the locally optimized Fog neural switch connections and maximize the overall QoS while satisfying the enormous traffic workload requirements. A sampled real-world trace-file workload from Galaxy backbone, Nigeria, is compared with SHN for Fog service provisioning. It is shown that with receding horizon, the ANN-based model ideally offers 100% throughput R value. Under the established training scenarios, the ANN switch offers the lowest mean square error while yielding acceptable QoS metrics. The result is significant for scalable networks supporting massive computational workloads.

A cross layer framework of Radio Resource Allocation for QoS provisioning in multi-channel fading wireless networks

In this paper, we present an analytical framework for the performance evaluation of Radio Resource Allocation (RRA) in Orthogonal Frequency Division Multiple Access (OFDMA) networks. We focus on Quality of Service (QoS) guaranteed traffic whose capacity, in terms of the number of active user connections, depends on the users’ QoS requirements and channel conditions, and the RRA algorithm. The required QoS is guaranteed by restricting the number of admitted calls, which in turn requires an accurate estimate of the QoS metrics and capacity supported by the RRA when a new call arrives. These estimates for OFDMA networks are variable and usually obtained through time-consuming offline computer simulations. Mathematical frameworks on the other hand yield timely and accurate results. However, earlier known works on analytical modelling of RRA either consider a single channel with random traffic arrivals or multiple channels with full buffer data traffic. In contrast, we develop a queueing theoretic framework considering randomly arriving QoS-guaranteed traffic in a variable-rate multi-channel multi-class OFDMA network. The framework can be used online leading to better dynamic Call Admission Control. We characterize the RRA algorithm using a scheduler control parameter which can regulate the call blocking probability while providing predefined QoS constraints. We model the RRA as a variable service rate, multi-server, multi-class, finite buffer queueing system and verify the derived QoS metrics using extensive Monte-Carlo discrete event simulations.

Quantitative and qualitative correlation analysis of optimal route discovery for vehicular ad-hoc networks

Vehicular ad-hoc networks (VANETs) are a significant field in the intelligent transportation system (ITS) for improving road security. The interaction among the vehicles is enclosed under VANETs. Many experiments have been performed in the region of VANET improvement. A familiar challenge that occurs is obtaining various constrained quality of service (QoS) metrics. For resolving this issue, this study obtains a cost design for the vehicle routing issue by focusing on the QoS metrics such as collision, travel cost, awareness, and congestion. The awareness of QoS is fuzzified into a price design that comprises the entire cost of routing. As the genetic algorithm (GA) endures from the most significant challenges such as complexity, unassisted issues in mutation, detecting slow convergence, global maxima, multifaceted features under genetic coding, and better fitting, the currently established lion algorithm (LA) is employed. The computation is analyzed by deploying three well-known studies such as cost analysis, convergence analysis, and complexity investigations. A numerical analysis with quantitative outcome has also been studied based on the obtained correlation analysis among various cost functions. It is found that LA performs better than GA with a reduction in complexity and routing cost.

A QoS guarantee approach for multimedia software system

Quality of service (QoS) is the overall performance of a computer network, which not support or guaranteed well with the current Internet technology. It is hard to track, locate, and recognize the reason of a QoS violation in volatile situations. Even though this issue is solved, other issues may rise, such as the Internet service providers not willing to open their management interfaces. Besides, not all Internet hardware can support QoS controlling. End-to-end QoS controlling is an effective way to overcome these issues. The existing end-to-end QoS solutions have a problem that they analyze and exploit the relationships between the QoS metrics and contexts in an isolated way. This paper proposes a novel comprehensively context-aware approach to exploit these relationships based on the Bayesian networks and fuzzy set. This approach divides into three processes: context discretization, context and QoS metric mapping, and QoS quantitative guarantee. We validate the effectiveness of the proposed approach in our real multimedia software system and compare the performance of our approach with other existing bandwidth-aware and playback-buffer-aware schemes. The experimental results demonstrate that the proposed approach performs better than existing solutions in improving the video Peak Signal-to-Noise Ratio.

RETRACTED ARTICLE: QoS and QoE Enhanced Resource Allocation for Wireless Video Sensor Networks Using Hybrid Optimization Algorithm

Resource allocation has posed a challenging of scarce resources to activities over time. Problems of optimal resource allocation are motivated by questions that arise in project scheduling, production planning, computer control, broadcasting, routing data, maintenance scheduling, and etc. Data transmission in environmental, security, and health monitoring requires both quality of service (QoS) and quality of equipment (QoE) aware network in order to ensure efficient usage of the resources and effective access. In this paper, we propose a resource allocation scheme for wireless video sensor network using hybrid optimization (RAS-HO) algorithm. Firstly, the cluster formation is performed by the modified animal migration optimization algorithm, which enhances the energy consumption. Secondly, an efficient resource allocation is performed by a glowworm swarm optimization based decision making algorithm. Simulation results show that the proposed scheme achieves required resources better than existing schemes in terms of QoS metrics are energy efficient, delay fairness, throughput, and QoE metrics are peak signal to noise ratio, structural similarity.

Impact of the NAND Flash Power Supply on Solid State Drives Reliability and Performance

The NAND flash memory technology is the fundamental building block of storage systems like solid state drives. Their reliability drastically impacts the design choices of the error recovery flows exploited to improve drive’s performance, which progressively degrades as the number of bit errors to be corrected increases. Among the many causes producing errors, it is found that the NAND flash power supply plays a non-negligible role. In this paper, we will show how the drive’s performance degradation rate measured by the sustainable bandwidth, read latency, and quality of service metrics, heavily depends on the power supply. The results will show that by using a lower power supply in endeavor to reduce the energy consumption of the drive will yield a significant bandwidth (up to 44.4 kIOPS), latency (up to $504~\boldsymbol {\mu }\text{s}$ ), and quality-of-service (up to 1.5 ms) detriment. Counterintuitively, we will show that the overall energy consumption per byte will increase (up to 4.5 times) in specific working conditions of the drive.

Network Resource Allocation System for QoE-Aware Delivery of Media Services in 5G Networks

The explosion in the variety and volume of video services makes bandwidth and latency performance of networks more critical to the user experience. The media industry’s response, HTTP-based Adaptive Streaming technology, offers media players the possibility to dynamically select the most appropriate bitrate according to the connectivity performance. Moving forward, the telecom industry’s move is 5G. 5G aims efficiency by dynamic network optimization to make maximum use of the resources to get as high capacity and Quality of Service (QoS) as possible. These networks will be based on software defined networking (SDN) and network function virtualization (NFV) techniques, enabling self-management functions. Here, machine learning is a key technology to reach this 5G vision. On top of machine learning, SDN and NFV, this paper provides a network resource allocator system as the main contribution which enables autonomous network management aware of quality of experience (QoE). This system predicts demand to foresee the amount of network resources to be allocated and the topology setup required to cope with the traffic demand. Furthermore, the system dynamically provisions the network topology in a proactive way, while keeping the network operation within QoS ranges. To this end, the system processes signals from multiple network nodes and end-to-end QoS and QoE metrics. This paper evaluates the system for live and on-demand dynamic adaptive streaming over HTTP and high efficiency video coding services. From the experiment results, it is concluded that the system is able to scale the network topology and to address the level of resource efficiency, required by media streaming services.

Area coverage of heterogeneous wireless sensor networks in support of Internet of Things demands

As the Internet of Things (IoT) evolves, more and more Wireless Sensor Networks (WSNs) are being deployed in the real world. Connected vehicles, smart grids, smart cities, smart healthcare, networks of robots, and disaster recovery networks are some examples. In WSNs, the area coverage is one of the most important quality of service metrics. A WSN without enough area coverage yields incorrect results. So calculating the covered area of a WSN is mandatory. Previous studies have used a simple approach: all nodes send their location to the sink, and it calculates the covered area centrally which makes huge unnecessary communication overhead. In our previous work titled Distributed Exact Coverage Rate Calculation, we calculated the covered area of a homogenous WSN in a distributed manner. In this paper, we provide a Heterogeneous Distributed Precise Coverage Rate (HDPCR) mechanism that calculates the covered area of a Heterogeneous Wireless Sensor Network by using a localized mechanism. With the use of boundary detection mechanisms, the HDPCR detects the boundary of the network and calculates its area. HDPCR also detects holes and calculates their area precisely. By subtracting these two calculated values, the covered area of the network can be computed. Many related studies have evaluated the coverage rate approximately with error and require more calculations to reduce the error rate. HDPCR calculates the coverage rate precisely without an error rate using simple arithmetic calculations. The exhaustive simulation also shows the superiority of HDPCR as compared to the previous approaches.

Route Availability as a Communication Quality Metric of a Mobile Ad Hoc Network

Using ad hoc communication between mobile terminals, MANETs (mobile ad hoc networks) are independent of any communication infrastructure but their communication quality can degrade because, as terminals move about in the service area, routes are constantly disconnected and then re-established. There has been no proposal for a quality metric that models this unstable state, i.e., route nonuniformity. This paper proposes a new concept of route availability (RA) as a metric of route nonuniformity in a MANET and verifies how effectively it represents the quality of service (QoS) of a network or the quality of experience (QoE) of video streaming. We have built an environment that emulates a MANET capable of video streaming, and developed a method of measuring RA for two representative MANET routing methods: AODV (Ad hoc On-Demand Distance Vector) and OLSR (Optimized Link State Routing). We have examined the relationship between RA and conventional network QoS metrics: packet loss rate and throughput. We have also checked RA using a subjective quality assessment test.

An Autonomic SLA Monitoring Framework Managed by Trusted Third Party in the Cloud Computing

This article describes how currently, service level agreements (SLAs) assurance forms one of the major challenges for cloud computing (CC) in order to guarantee quality of service (QoS) in real-time and control SLA violations. However, due to the highly dynamic nature of this open environment, it is important to have a binding agreement between all the service parties for ensuring trust while fulfilling the expected QoS. To properly operate and manage such complex situations, an effective and efficient monitoring is crucial. The participation of a trusted third party (TTP) is necessary in order to resolve conflicts between involved parties. This article proposes an autonomic SLA monitoring framework managed by TTP composed of two modules: the first one SLA establishment module, which aims at providing support for automated SLA generation and management. The second one, a service monitoring module to dynamically monitor QoS metrics by detecting SLA violations at runtime to verify compliances for the respective SLAs, and to propose a mechanism for an adaptive remedy rectification, as a contribution at the third maturity level of the autonomic computing paradigm as defined by IBM. The framework is validated with scenarios on response time and availability, the results obtained are promising. They confirm that this framework manages SLAs in an efficient way as it detects all violations to be communicated to concerned parties, and identifies particular penalty clauses that can be used to modify the reputation of a provider over time. The TTP framework equipped with such reputation module can provide real-time assessment for consumers informed decision making to continue using a service or to migrate to another service provider in the case of service degradation. This creates a fair competitiveness between providers and hence improves service performance and the reliability in the cloud.

E3DOAS: Balancing QoE and Energy-Saving for Multi-Device Adaptation in Future Mobile Wireless Video Delivery

Smart devices (e.g., smartphones, tablets, smart-home devices, etc.) have become important companions to most people in their daily activities, and are very much used for multimedia content exchange (i.e., video sharing, real-time/non-real-time multimedia streaming), contributing to the exponential increase in mobile traffic over the current wireless networks. While the next generation mobile networks will provide higher capacity than the current 4G systems, the network operators will face important challenges associated with the outstanding increase of both video traffic and user expectations in terms of their levels of perceived quality or quality of experience (QoE). Furthermore, the heterogeneity of mobile devices (e.g., screen resolution, battery life, and hardware performance) also impacts severely the end-user QoE. In this context, this paper proposes an evolved QoE-aware energy-saving device-oriented adaptive scheme (E3DOAS) for mobile multimedia delivery over future wireless networks. E3DOAS makes use of a coalition game-based rate allocation strategy within the multi-device heterogeneous environment, and optimizes the trade-off between the end-user perceived quality of the multimedia delivery and the mobile device energy-saving. Testing has involved a prototype of E3DOAS, a crowd-sourcing-based QoE assessment method to model non-reference perceptual video quality, and an energy measurement testbed introduced to collect power consumption parameters of the mobile devices. Simulation-based performance evaluation showed how E3DOAS outperformed other state of the art multimedia adaptive solutions in terms of energy saving, end-to-end quality of service metrics and end-user perceived quality.

Mean Service Metrics: Biased Quality Judgment and the Customer–Server Quality Gap

Problem definition: People often make service-quality judgments based on information about the quality of each server even though they care primarily about the quality each customer experiences. When and how do server-level quality metrics differ from customer-experienced ones? Can people properly account for these differences, or do they drive human judgment and decision biases? Academic/practical relevance: Biased judgments about service quality can cause governments to fund programs suboptimally, organizations to promote the wrong employees, and customers to make disappointing purchases. We further our understanding of the role that cognitive biases play in services and how to manage quality information in light of them. Methodology: We use a mathematical model to define the gap between server-level and customer-experienced quality metrics. We use secondary data in the context of the higher-education industry to quantify the customer–server quality gap in practice. We construct a behavioral model to derive hypotheses about how environmental factors impact the direction and magnitude of judgment biases. Controlled laboratory experiments test the hypothesized biases and mitigation techniques. Results: Our empirical study reveals that the two measures differ enough to drive significant differences in the rank order of school majors, teachers, and airports. Our experiments support our main conjecture that judgments and decisions about customer-experienced metrics are biased toward server-level metrics. Consequently, (1) judgments about customer-experienced quality are biased high/low when quality and server load are negatively/positively correlated, (2) judgments about a server’s absolute impact on customer experience are biased high/low when a server has a smaller/larger load than average, and (3) providing customer-experienced quality metrics mitigate these biases. Managerial implications: Our results help identify when and why service-quality metrics are likely to mislead judgments and bias decisions as well as who is likely to benefit from such biases. The results also guide system designers on how to report metrics when seeking to help support effective decision making.

Utility-Centric Networking: Balancing Transit Costs With Quality of Experience

This paper is focused on techniques for maximizing utility across all users within a total network transit cost budget. We present a new method for selecting between replicated servers distributed over the Internet. First, we introduce a novel utility framework that factors in quality of service metrics. Then we design an optimization algorithm, solvable in polynomial time, to allocate user requests to servers based on utility while satisfying network transit cost constraints, mapping service names to service instance locators. We then describe an efficient, low overhead distributed model which only requires knowledge of a fraction of the data required by the global optimization formulation. Next, a load-balancing variant of the algorithm is explored that substantially reduces blocking caused by congested servers. Extensive simulations show that our method is scalable and leads to higher user utility compared with mapping user requests to the closest service replica, while meeting network traffic cost constraints. We discuss several options for real-world deployment that require no changes to end-systems based on either the use of SDN controllers or extensions to the current DNS system.