Quality Of Service Perspective Research Articles

Optimizing Water Distribution through Explainable AI and Rule-Based Control

Optimizing water distribution both from an energy-saving perspective and from a quality of service perspective is a challenging task since it involves a complex system with many nodes, many hidden variables and many operational constraints. For this reason, water distribution systems need to handle a delicate trade-off between the effectiveness and computational time of the solution. In this paper, we propose a new computationally efficient method, named rule-based control, to optimize water distribution networks without the need for a rigorous formulation of the optimization problem. As a matter of fact, since it is based on a machine learning approach, the proposed method employs only a set of historical data, where the configuration can be labeled according to a quality criterion. Since it is a data-driven approach, it could be applied to any complex network where historical labeled data are available. In particular, rule-based control exploits a rule-based classification method that allows us to retrieve the rules leading to good or bad performances of the system, even without any information about its physical laws. The evaluation of the results on some simulated scenarios shows that the proposed approach is able to reduce energy consumption while ensuring a good quality of the service. The proposed approach is currently used in the water distribution system of the Milan (Italy) water main.

Resource-Aware Video Delivery in Fog Radio Access Networks: A Joint QoE and QoS Perspective

In this paper, the video delivery strategy is studied under the cooperative framework of cache-enabled fog radio access networks (F-RAN), where multicast beamforming and video-quality selection are both taken into account in terms of different scenarios. In the user-centered scenario, an optimization problem regarding to the network quality of experience (QoE) improvement is first formulated under the essential quality of service (QoS) requirements of the users. Furthermore, an energy efficiency maximization scheme is proposed in the user-dense scenario to improve the resource utilization when network resources may be insufficient to guarantee the QoS requirements of all the users. In particular, a user scheduling scheme is studied in this case. More specifically, the non-convex constraints and fractional object functions in the aforementioned problems are dealt with by successive convex approximation (SCA) technique and the Dinkelbach approach. In order to solve the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$l_{0}$</tex-math></inline-formula> -norm problem caused by user scheduling, we introduce the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$l_{1}$</tex-math></inline-formula> -norm approximation to improve the sparsity of the solution. Finally, the effectiveness of the proposed schemes is validated by extensive simulations.

QoS-Aware Energy Management and Node Scheduling Schemes for Sensor Network-Based Surveillance Applications

Recent advances in wireless technologies have led to an increased deployment of Wireless Sensor Networks (WSNs) for a plethora of diverse surveillance applications such as health, military, and environmental. However, sensor nodes in WSNs usually suffer from short device lifetime due to severe energy constraints and therefore, cannot guarantee to meet the Quality of Service (QoS) needs of various applications. This is proving to be a major hindrance to the widespread adoption of WSNs for such applications. Therefore, to extend the lifetime of WSNs, it is critical to optimize the energy usage in sensor nodes that are often deployed in remote and hostile terrains. To this effect, several energy management schemes have been proposed recently. Node scheduling is one such strategy that can prolong the lifetime of WSNs and also helps to balance the workload among the sensor nodes. In this article, we discuss on the energy management techniques of WSN with a particular emphasis on node scheduling and propose an energy management life-cycle model and an energy conservation pyramid to extend the network lifetime of WSNs. We have provided a detailed classification and evaluation of various node scheduling schemes in terms of their ability to fulfill essential QoS requirements, namely coverage, connectivity, fault tolerance, and security. We considered essential design issues such as network type, deployment pattern, sensing model in the classification process. Furthermore, we have discussed the operational characteristics of schemes with their related merits and demerits. We have compared the efficacy of a few well known graph-based scheduling schemes with suitable performance analysis graph. Finally, we study challenges in designing and implementing node scheduling schemes from a QoS perspective and outline open research problems.

QoS-Based Robust Cooperative-Jamming-Aided Beamforming for Correlated Wiretap Channels

This paper studies cooperative-jamming (CJ)-aided beamforming design under Gaussian channel uncertainties to reduce the secrecy loss due to reception correlation. In light of the difficulty of maximizing the outage-probability-constrained secrecy rate, a novel quality-of-service (QoS)-based optimization is considered. By employing the Bernstein-type inequality to approximate the probabilistic constraints, we seek to maximize the target outage signal-to-noise ratio (SNR) at the destination under transmit power and SNR outage constraints. Simulation results verify the designed CJ-aided beamforming substantially enhances the secrecy from a QoS perspective.

Virtualization of Industrial Real-Time Networks for Containerized Controllers.

The virtualization technology has a great potential to improve the manageability and scalability of industrial control systems, as it can host and consolidate computing resources very efficiently. There accordingly have been efforts to utilize the virtualization technology for industrial control systems, but the research for virtualization of traditional industrial real-time networks, such as Controller Area Network (CAN), has been done in a very limited scope. Those traditional fieldbuses have distinguished characteristics from well-studied Ethernet-based networks; thus, it is necessary to study how to support their inherent functions transparently and how to guarantee Quality-of-Service (QoS) in virtualized environments. In this paper, we suggest a lightweight CAN virtualization technology for virtual controllers to tackle both functionality and QoS issues. We particularly target the virtual controllers that are containerized with an operating-system(OS)-based virtualization technology. In the functionality aspect, our virtualization technology provides virtual CAN interfaces and virtual CAN buses at the device driver level. In the QoS perspective, we provide a hierarchical real-time scheduler and a simulator, which enable the adjustment of phase offsets of virtual controllers and tasks. The experiment results show that our CAN virtualization has lower overheads than an existing approach up to 20%. Moreover, we show that the worst-case end-to-end delay could be reduced up to 78.7% by adjusting the phase offsets of virtual controllers and tasks.

Deep learning approach to multimedia traffic classification based on QoS characteristics

With the fast increase of multimedia traffic in Internet of Things (IoT) applications, IoT traffic now requires very different Quality of Service (QoS). By extensive statistical analysis of traffic flow data from a real world network, the authors find that there are some latent features hidden in the multimedia data, which can be useful for accurately differentiating multimedia traffic flows from the QoS perspective. Under limited training data conditions, existing shallow classification methods are limited in performance, and are thus not effective in classifying emerging multimedia traffic types, which have truly entered the era of big data and become very completed in QoS features. This situation inspires us to revisit the multimedia traffic classification problem with a deep learning (DL) approach. In this study, an improved DL-based multimedia traffic classification method is proposed, which considers the inherent structure of QoS features in multimedia data. An improved stacked autoencoder model is employed to learn the relevant QoS features of multimedia traffic. Extensive experimental studies with multimedia datasets captured from a campus network demonstrate the effectiveness of the proposed method over six benchmark schemes.

Internet multimedia traffic classification from QoS perspective using semi-supervised dictionary learning models

To address the issue of finegrained classification of Internet multimedia traffic from a Quality of Service (QoS) perspective with a suitable granularity, this paper defines a new set of QoS classes and presents a modified K-Singular Value Decomposition (K-SVD) method for multimedia identification. After analyzing several instances of typical Internet multimedia traffic captured in a campus network, this paper defines a new set of QoS classes according to the difference in downstream/upstream rates and proposes a modified K-SVD method that can automatically search for underlying structural patterns in the QoS characteristic space. We define bag-QoS-words as the set of specific QoS local patterns, which can be expressed by core QoS characteristics. After the dictionary is constructed with an excess quantity of bag-QoS-words, Locality Constrained Feature Coding (LCFC) features of QoS classes are extracted. By associating a set of characteristics with a percentage of error, an objective function is formulated. In accordance with the modified K-SVD, Internet multimedia traffic can be classified into a corresponding QoS class with a linear Support Vector Machines (SVM) classifier. Our experimental results demonstrate the feasibility of the proposed classification method.

Towards an efficient and a more accurate web service selection using MCDM methods

Nowadays, due to the fast growth of the number of web services with the same functionalities, the selection of the best services becomes a tricky task. Therefore, to provide a more powerful selection tool, web services are now additionally distinguished with respect to their non-functional aspects, especially to Quality of Service (QoS) requirements. Indeed, using QoS perspective in web service selection can help to promote the best candidate web services among those holding the same functional requirements. Hitherto, various QoS based web service selection approaches have been explored in the literature. However, two main challenging and conflicting issues are still demanding a specific attention, namely how accurate is the selection and what is the cost of such a process? In this paper, we target Multi Criteria Decision Making (MCDM) methods to select web services. Within this context, we propose a hybrid method to select the optimal web services based on MCDM. First of all, we use the Skyline method to reduce the research space by removing the dominated web services. Then, the BWM (Best Worst Method) is applied to normalize the weights associated with the QoS parameters according to the user request requirements. To rank the non-dominated web services with respect to the weights normalization, we explore the use of four different MCDM approaches that enjoy an affordable complexity, which are: VIKOR, SAW, TOPSIS and COPRAS. For the sake of a comparison, each method is first tested separately in order to assess its ability to select the optimal elements. Then, in order to provide the best compromise between the different ranking methods, we apply the Borda voting method to determine the compromise solution. Hence, we are able to measure the accuracy of each technique relatively to the Borda solution by applying a similarity function. The implementation and the simulation of the different methods have permitted to assess their performances in terms of time execution and accuracy. The results show clearly that MCDM ranking methods are more likely to diverge in selecting the best elements when the number of web services is increased. This finding argues for the pertinence of computing the Borda solution in the context of MCDM based web service selection.

Novel feature selection and classification of Internet video traffic based on a hierarchical scheme

Accurate traffic classification is critical for efficient network management and resources utilization. Different video traffics have different QoS (Quality of Service) requirements. To provide Internet video services with better QoS support, a fine grained classification scheme for network video traffic is proposed in this paper. Through extensive statistical analysis of typical video traffic flows with a consistency-based method, several new flow statistical features are extracted. They are found to be more effective in discriminating different video traffics, especially from the QoS perspective, than commonly used features available in the literature. A hierarchical k-Nearest Neighbor (kNN) classification algorithm is then developed based on the combinations of these statistical features. Experiments are performed to evaluate the effectiveness of the proposed method on a large scale real network video traffic data. The experimental results show that the proposed method outperforms existing methods applying commonly used flow statistical features.

Resource provisioning and scheduling in clouds: QoS perspective

Resource provisioning of appropriate resources to cloud workloads depends on the quality of service (QoS) requirements of cloud applications and is a challenging task. In cloud environment, heterogeneity, uncertainty and dispersion of resources encounter a problem of allocation of resources, which cannot be addressed with existing resource management frameworks. Resource scheduling, if done after efficient resource provisioning, will be more effective and the cloud resources would be scheduled as per the user requirements (QoS) on provisioned resources. Execution of cloud workloads should be as per QoS parameters to fully satisfy the cloud consumer. Therefore, based on QoS parameters, it is mandatory to predict and verify the resource provisioning before actual resource scheduling. In this paper, a resource provisioning and scheduling framework has been presented which caters to provisioned resource distribution and scheduling of resources. Cloud workloads have been re-clustered using k-means-based clustering algorithm after firstly clustering them through workload patterns to identify the QoS requirements of a workload, and then based on identified QoS requirements resources are provisioned before actual scheduling. Further, scheduling has been done based on different scheduling policies. Finally, the performance of the proposed framework has been evaluated in both real and simulated cloud environment and experimental results show that the framework provisions and schedules resource efficiently by considering energy consumption, execution cost and execution time as QoS parameters.

Performance evaluation on quality of Asian e-government websites &amp;ndash; an AHP approach

The concept of website quality is consisting of many criteria: quality of service perspective, a user perspective, a content perspective or indeed a usability perspective. This research conducts some tests to measure the quality of e-government website of five Asian countries via web diagnostic tools online. We propose a methodology for determining and evaluate the best e-government sites based on many criteria of website quality. The approach has been implemented using analytical hierarchy process (AHP), the proposed model uses the AHP pairwise comparisons and the measure scale to generate the weights for the criteria which are much better and guarantee more fairly preference of criteria. Applying AHP approach for website evaluation has resulted in significant acceleration of implementation, raised the overall effectiveness with respect to the underlying methodology and ultimately enabled more efficient procedure. The result of this study confirmed that the e-government websites of Asian are neglecting performance and quality criteria.

A QoS Perspective on Exception Diagnosis in Service-Oriented Computing

Unlike object-oriented applications it is difficult to address exceptions in multi-agent systems due to their highly dynamic and autonomous nature. Our previous work has examined exception diagnosis in multi-agent systems based on a heuristic classification method. In this paper, we extend our work by applying an exception diagnosis method to web services (WS) by proposing a unified framework for dealing with exceptions occurring in multi-agent systems as well as in web services. Importantly, we relate the impact of exceptions to Quality of Service (QoS), as exceptions normally degrade the quality of service offered to a service consumer. Our framework consists of a QoS monitoring agent that monitors all interactions taking place between service consumers and service providers. The monitoring agent encodes the knowledge of exceptions, their causes and applies the heuristic classification method for reasoning in order to diagnose underlying causes of monitored exceptions. In this paper, we categorize exceptions into three levels in multi-agent systems: Environment Level Exception; Knowledge Level Exception and Social Level Exception. This paper also discusses different classes of exceptions in web services based on the web service stack.

QoS-aware distributed spectrum sharing for heterogeneous wireless cognitive networks

Ubiquitous wireless networking calls for efficient dynamic spectrum allocation (DSA) among heterogeneous users with diverse transmission types and bandwidth demands. To meet user-specific quality-of-service (QoS) requirements, the power and spectrum allocated to each user should lie inside a bounded region in order to be meaningful for the intended application. Most existing DSA methods aim at enhancing the total system utility. As such, spectrum wastage may arise when the system-wise optimal allocation falls outside individual users' desired regions for QoS provisioning. The goal of this paper is to develop QoS-aware distributed DSA schemes using game-theoretic approach. We derive DSA solutions that respect QoS and avoid naively boosting or sacrificing some users' utilities to maximize the network spectrum utilization. Specifically, we propose two game-based DSA algorithms: one resorts to proper scaling of the transmission power according to each user's useful utility range, and the other embeds the QoS factor into the utility function used during gaming. To evaluate DSA schemes from a practical QoS perspective, we introduce two new metrics, namely ''system useful utility'' and ''fraction of QoS-satisfied users''. Simulations confirm that the proposed DSA techniques outperform existing QoS-blind game models in terms of the spectrum sharing efficiency in heterogeneous networks. Convergence analysis of the proposed QoS-aware DSA algorithms is also provided.

Addressing the requirements of QoS management for wireless ad hoc networks☆

Quality of Service (QoS) provisioning and management in ad hoc networks remains a challenging task. Existing research focuses either on providing QoS or on network management, in ad hoc networks. However, a comprehensive approach to QoS management in ad hoc networks, i.e. network management in support of service differentiation, QoS robustness, and network survivability is still lacking. In this paper, we survey the existing literature on ad hoc network management from a QoS perspective. We identify policy-based management as a promising approach for QoS provisioning and management in ad hoc networks, and describe the components that we believe are crucial for effective functioning of such a policy-based framework. Our initial experimental results in assessing signaling overhead and response time for network management in ad hoc networks support the choice of a hybrid policy architecture that combines outsourcing and provisioning policy distribution models.