Dynamic Quality Of Service Research Articles

QoS provisioning framework for service-oriented internet of things (IoT)

The proliferation of ubiquitous sensing technology is bringing a rising number of the innovative models that have unique characteristics of the utility computing. These models have offered great opportunities to improve IT industries and business processes through the convergence of cloud computing and internet of things (IoT). Although this convergence establishes seamless intelligent interaction among physical and virtual entities, it has difficulty not only to meet the required level of quality of service(QoS) but also to satisfy the user’s complex demands. As a result, the predisposition to create a dynamic service-oriented environment has become a fundamental design issue. The main objective of this study is to introduce a dynamic QoS provisioning framework (QoPF) for service-oriented IoT using backtracking search optimization algorithm (BSOA). The QoPF framework is proposed to maximize the composite service quality in IoT application layer by making a balance between service reliability and acceptable cost of the computational time. The effectiveness of the QoPF framework is evaluated using a number of performance metrics such as throughput, delay time, and jitter. The experimental results demonstrate that worthiness of the QoPF to meet QoS requirements more than other state-of-the-art techniques in the literature review.

Efficient Algorithm for Identification and Cache Based Discovery of Cloud Services

Efficient resource identification and discovery is the primary requirements for cloud computing services, as it assists in scheduling and managing of cloud applications. Cloud computing is a revolution of the economic model rather than technological. It takes advantage of several technologies that were tested and modified by replacing the local use of computers with centralized shared resources that are managed and stored by Cloud Service Providers (CSPs) in a transparent manner for Cloud Consumers (CCs). With this new use, various cloud services have appeared and it is mainly classified into three broad categories i.e., Infrastructure as a service (IaaS), Software as a service (SaaS) and Platform as a service (PaaS). Each of these cloud services provides several benefits to the CCs through their respective Quality of Service (QoS) metric. Among the cloud service models, most of the QoS attribute and metric are identical and some are different. The vendors of cloud have focused their objectives on the development of scalability, resource consumption and performance, other characteristics of cloud have been ignored. While CSPs face challenging difficulties in publishing cloud services that displays their cloud resources, at the same time CCs do not have standard mechanism for cloud resource discovery, automated cloud services selection, and easy use of cloud services. In this frame, this paper puts forward a set of QoS metric for SaaS, IaaS, PaaS services and propose (i) An efficient algorithm for identifying the cloud services based on the QoS metric given by the cloud consumer using decision tree classification algorithm (ii) An efficient algorithm for Cloud service resource registry which aims to enable CSPs to register their services with its QoS attributes and (iii) A Cloud service resource discovery that search for the suitable cloud service and their attributes in the cloud service registry that meets the CCs application requirements using Split and Cache (SAC) algorithm. Our new approach makes the provisioning of cloud service possible by ease of resource identification, publication, discovery based on dynamic QoS attributes via web GUI interface backed by series of test that has validated and the proposed approach is feasible and sound. The recommended solution is important: instead of putting effort in locating, learning about the services and evaluating them, CCs can easily identify, discover the services, select and use the required cloud resources. The efficiency of our algorithms was assessed through experiments using CloudSim, which primarily decreases the response time, CPU utilization and memory consumption for identifying and searching the cloud services and increases the accuracy of the CSPs list retrieved along with their QoS attributes.

Temporal Pattern-Aware QoS Prediction via Biased Non-Negative Latent Factorization of Tensors.

Quality-of-service (QoS) data vary over time, making it vital to capture the temporal patterns hidden in such dynamic data for predicting missing ones with high accuracy. However, currently latent factor (LF) analysis-based QoS-predictors are mostly defined on static QoS data without the consideration of such temporal dynamics. To address this issue, this paper presents a biased non-negative latent factorization of tensors (BNLFTs) model for temporal pattern-aware QoS prediction. Its main idea is fourfold: 1) incorporating linear biases into the model for describing QoS fluctuations; 2) constraining the model to be non-negative for describing QoS non-negativity; 3) deducing a single LF-dependent, non-negative, and multiplicative update scheme for training the model; and 4) incorporating an alternating direction method into the model for faster convergence. The empirical studies on two dynamic QoS datasets from real applications show that compared with the state-of-the-art QoS-predictors, BNLFT represents temporal patterns more precisely with high computational efficiency, thereby achieving the most accurate predictions for missing QoS data.

Modeling and Forecasting of Time-Aware Dynamic QoS Attributes for Cloud Services

Currently, statistical time-series methods have primarily been employed to predict time-aware dynamic quality of service (QoS) attributes for Web services. In this paper, we propose the application of genetic programming (GP) for such predictions. Our experimental results indicate that the GP-based approach is more accurate than the other approaches presented for comparison. However, for the efficient management of such attributes for cloud services, including their modeling and forecasting, the current research is insufficient because a set of research questions remains unanswered. In this paper, we first clearly define these research questions and then design and perform a set of empirical experiments to address the questions. Finally, the experimental results are exhaustively discussed to answer the studied research questions. The empirical study and analysis presented in this paper could be informative for the management (modeling and forecasting) of the time-aware dynamic QoS attributes of cloud services. For example, we verify that machine-learning approaches are generally superior to the widely used statistical time-series methods in terms of both modeling accuracy and forecasting accuracy. Furthermore, after considering a variety of situations and cases, the GP-based approach is still the best option for the studied problem. In addition, except for the technical approaches, this paper also exhaustively studies the influence of the properties of the cloud dynamic QoS attributes, including their size and time granularity.

Sequential Individual Rationality in Dynamic Ridesharing

In dynamic ridesharing systems, both operational and economic policies impact Quality-of-Service (QoS). Field experiments have found that firms benefit from proactively compensating users whose QoS expectations are violated. This motivates a broader analytical study of how behavioral perceptions of QoS impact operational and economic policy design in ridesharing systems. We introduce a novel, QoS-centric framework with the following elements: (a) users’ state-dependent utility model that bridges operational (detours) and economic effects (prices or cost shares), (b) dynamic QoS notion, sequential individual rationality, defined on the sequence of (dis)utilities from successive stages of a shared ride, influenced by behavioral drivers (reference/recency effects, loss aversion), and (c) QoS-sensitive economic objectives (profit or fairness) that endogenize users’ choices and QoS constraints. Our framework can be used to extract key operational insights from QoS-sensitive economic objectives in two different environments: (i) commercial ridesharing, which involves pricing exclusive/shared service to maximize profit (considering penalties for QoS-violations), and (ii) community carpooling, which involves designing fair cost sharing schemes. In the commercial setting, we characterize a ride’s optimal shareable region, and show that it may be optimal for a QoS-sensitive service provider to violate QoS and get penalized, no matter how strong the users’ loss aversion. In the carpooling setting, we characterize routes that admit budget-balanced, QoS-compliant cost sharing schemes, resulting in a ride’s QoS-compliant shareable region. We define sequential fairness and characterize fair, QoS-compliant schemes that bring out insightful structural properties, including a strong requirement that commuters must compensate each other for the detour-inconveniences they cause.

Power Allocation Study for Non-Orthogonal Multiple Access Networks With Multicast-Unicast Transmission

This paper considers a downlink single-cell non-orthogonal multiple access (NOMA) network, where the base station, which has multiple antennas, broadcasts the mixed multicast and unicast messages to multiple users with a single antenna. We propose two types of power allocation schemes for NOMA networks with multicast-unicast transmission to investigate the impact of performance, namely, cognitive radio inspired NOMA with dynamic quality of service (QoS) at multicast users (CR-NOMA-D-M), and CR-NOMA constrains the dynamic QoS at the unicast user (CR-NOMA-D-U). Based on the proposed schemes, we drive the exact/closed-form expressions for the secrecy outage probability, outage probability, as well as the approximate outage probability at high signal-to-noise ratio to study the diversity gain. Compared with existing works, the derived analytical results show that both proposed schemes cannot only significantly improve the outage performance for multicast users, but also remarkably enhance the diversity gain and the secrecy outage probability for the unicast user. Finally, the theoretical results are validated by the numerical results.

Smart Multi-RAT Access Based on Multiagent Reinforcement Learning

The ongoing increasing traffic in the era of big data yields unprecedented demands in user experience and network capacity expansion. The users of next generation mobile networks (5 G) should be able to use 3GPP, IEEE, and other technologies simultaneously. The integration of multiple radio access technologies (RATs) of licensed or unlicensed bands has been widely deemed as a cost-efficient way to greatly increase the network capacity. In this paper, we propose a smart aggregated RAT access (SARA) strategy with aim of maximizing the long-term network throughput while meeting diverse traffic quality of service (QoS) requirements. We consider the scenario that users with different QoS requirements access to a heterogeneous network with coexisting cellular-WiFi. In order to maximize system throughput while meeting diverse traffic QoS requirements in such a complex and dynamic environment, we exploit multiagent reinforcement learning to perform RAT selection in conjunction with resource allocation for individual user access requests, through sensing dynamic channel states and traffic QoS requirements. In SARA, we first use Nash Q-learning to provide a set of feasible RAT selection strategies while decreasing the strategy space in learning process, and then employ Monte Carlo tree search (MCTS) based Q-learning to perform resource allocation. Numerical results reveal that the network throughput can be maximized while meeting various traffic QoS requirements with limited number of searches by using our proposed SARA algorithm. For bulk arrival access requests, a suboptimal solution can be obtained as high computational complexity is incurred for achieving global optimality. Another attractive feature of SARA is that a tradeoff between the solution optimality and learning time can be readily made by terminating the search of MCTS according to the time constraint. Compared with traditional WiFi offloading schemes, SARA can significantly improve network throughput while guaranteeing traffic QoS requirements.

A Novel User-Oriented Quality of Service Algorithm for Home Networks

This paper aims to present a novel dynamic quality of service (QoS) algorithm for home networks. The algorithm carries out the QoS management of a service for a user in a home network based on the profile consisting of the past responses of the user for that service. It performs the prediction of the future responses to the service from the profile using the general regression neural network algorithm. The algorithm finds the bandwidth allocation consuming minimum total bandwidth among those predicted to produce desired responses. The minimum bandwidth allocation is then used for the service. The corresponding user responses are used to update the profiles for the subsequent dynamic QoS managements. The algorithm requires no offline training and achieves fast adaptation to the user responses. Both analytical and numerical results reveal that the proposed algorithm provides an effective alternative for user-oriented QoS management in the heterogeneous home networks.

Quality of Service Management for Home Networks Using Online Service Response Prediction

A novel quality of service (QoS) provisioning algorithm for home networks is presented in this paper. The algorithm carries out the QoS-aware bandwidth allocation using the general regression neural networks (GRNNs). Among all the allocations predicted to receive positive service responses, the algorithm finds the allocation with minimum total bandwidth for the current service. The service response prediction is based on the GRNN with the training set containing the bandwidth allocations and their service responses for past transmissions. The new service responses will then be used to update the training set for the subsequent transmissions. To attain accurate tracking of diversified service requirements, flexible specification of service response levels and QoS levels are provided. Both analytical and numerical studies reveal that the proposed algorithm is able to provide prompt or steady reactions to the service feedback depending on the variations of the source data rates. Because of its simplicity and effectiveness, the proposed algorithm is well suited for dynamic QoS management for heterogeneous home networks.

Adaptive checkpointing with reliable storage in cloud environment

Cloud computing has recently gained popularity as a resource platform for on-demand, high availability and high scalability access to resources, while offering dynamic flexible infrastructures and QoS (Quality of Services) guaranteed services. In this environment, reliability is very important to e nsure the effectiveness of the system and meet the desired SLA (System Level Agreement). Our work proposed in this paper uses the checkpointing of tasks to ensure the reliability and the replication of checkpointing files to ensure the accessibility. To ensure the reliability of services, we used a fault tolerance strategy based on adaptive checkpointing of two levels. This strategy takes into account the complexity and characteristics of cloud computing and it minimizes the overhead. To ensure the accessibility and the availability of checkpointing storage, we used a dynamic passive replication based on an availability degree specified by SLA criteria to decide the placement and the number of replicas.

OpenIPTV: a comprehensive SDN-based IPTV service framework

IPTV is an emerging TV content delivery service that should guarantee Quality of Service (QoS) to deliver television contents over IP for their customers. However, providing such QoS regarding service level agreements (SLA) requires frequent service monitoring and adaptive configuration mechanisms. Nowadays, Software Defined Networks (SDNs) provide capabilities to deploy and manage networks dynamically and can maintain QoS. In this paper, a novel IPTV service framework (OpenIPTV) is proposed, which utilizes SDN as an underlying technology for providing QoS for IPTV customers in a shared backbone network. OpenIPTV is implemented in a well-known OpenDayLight controller and strictly followed a modular design for the sake of efficiency. OpenIPTV comprises all service requirements such as resource monitoring, channel changing, multicast group managing and dynamic QoS multicast traffic engineering. The performance of OpenIPTV is evaluated under different scenarios and experimental results confirmed the effectiveness of the proposed framework in terms of QoS metrics. Furthermore, experimental results show that OpenIPTV is a feasible and practical solution to deliver IPTV services with high level of QoS over SDN.

Self-Adaptive and Online QoS Modeling for Cloud-Based Software Services

In the presence of scale, dynamism, uncertainty and elasticity, cloud software engineers faces several challenges when modeling Quality of Service (QoS) for cloud-based software services. These challenges can be best managed through self-adaptivity because engineers’ intervention is difficult, if not impossible, given the dynamic and uncertain QoS sensitivity to the environment and control knobs in the cloud. This is especially true for the shared infrastructure of cloud, where unexpected interference can be caused by co-located software services running on the same virtual machine; and co-hosted virtual machines within the same physical machine. In this paper, we describe the related challenges and present a fully dynamic, self-adaptive and online QoS modeling approach, which grounds on sound information theory and machine learning algorithms, to create QoS model that is capable to predict the QoS value as output over time by using the information on environmental conditions, control knobs and interference as inputs. In particular, we report on in-depth analysis on the correlations of selected inputs to the accuracy of QoS model in cloud. To dynamically selects inputs to the models at runtime and tune accuracy, we design self-adaptive hybrid dual-learners that partition the possible inputs space into two sub-spaces, each of which applies different symmetric uncertainty based selection techniques; the results of sub-spaces are then combined. Subsequently, we propose the use of adaptive multi-learners for building the model. These learners simultaneously allow several learning algorithms to model the QoS function, permitting the capability for dynamically selecting the best model for prediction on the fly. We experimentally evaluate our models in the cloud environment using RUBiS benchmark and realistic FIFA 98 workload. The results show that our approach is more accurate and effective than state-of-the-art modelings.

Search based approach to forecasting QoS attributes of web services using genetic programming

ContextCurrently, many service operations performed in service-oriented software engineering (SOSE) such as service composition and discovery depend heavily on Quality of Service (QoS). Due to factors such as varying loads, the real value of some dynamic QoS attributes (e.g., response time and availability) changes over time. However, most of the existing QoS-based studies and approaches do not consider such changes; instead, they are assumed to rely on the unrealistic and static QoS information provided by service providers, which may seriously impair their outcomes. ObjectiveTo predict dynamic QoS values, the objective is to devise an approach that can generate a predictor to perform QoS forecasting based on past QoS observations. MethodWe use genetic programming (GP), which is a type of evolutionary computing used in search-based software engineering (SBSE), to forecast the QoS attributes of web services. In our proposed approach, GP is used to search and evolve expression-based, one-step-ahead QoS predictors. To evaluate the performance (accuracy) of our GP-based approach, we also implement most current time series forecasting methods; a comparison between our approach and these other methods is discussed in the context of real-world QoS data. ResultsCompared with common time series forecasting methods, our approach is found to be the most suitable and stable solution for the defined QoS forecasting problem. In addition to the numerical results of the experiments, we also analyze and provide detailed descriptions of the advantages and benefits of using GP to perform QoS forecasting. Additionally, possible validity threats using the GP approach and its validity for SBSE are discussed and evaluated. ConclusionsThis paper thoroughly and completely demonstrates that under a realistic situation (with real-world QoS data), the proposed GP-based QoS forecasting approach provides effective, efficient, and accurate forecasting and can be considered as an instance of SBSE.

Model Aspects of Quality of Service Management in Machine to Machine Communications

Abstract Machine-to-Machine communications (M2M) allow connected devices to exchange data and perform actions without human intervention. Different M2M applications have different quality of service (QoS) requirements. The dynamic QoS control is critical for applications like video surveillance, transportation services, and industrial control. The provided QoS depends on device connectivity. Connectivity management provides centralized management of M2M devices and connections. The paper studies model aspects of QoS management for connected M2M devices by means of connectivity management. Models for connectivity management are suggested, formally described and verified. The capabilities for device connectivity management are used to model the behavior of autonomous agent for dynamic QoS control.

Estimator Goore Game based quality of service control with incomplete information for wireless sensor networks

Quality of Service (QoS) control is one of the key mechanisms to ensure the effective operation of wireless sensor networks (WSNs). In many specific applications such as battlefield communications, the optimum number of sensors sending information at any given time is an important QoS requirement. For such requirement, some Goore Game based QoS control approaches have been exploited to dynamically adjust the number of active sensors to the optimal one in the literatures, but they assume that the optimum number is known to the base station explicitly, and each method has its own shortcomings.In this paper, a novel approach is proposed to solve the QoS control problem. A framework for solving such problem is firstly proposed, in which the optimum sensor number is not known in advance but learnt by interacting with upper level applications in a reinforcement manner. On this basis, an algorithm named Estimator Goore Game (EGG) is designed to adjust the number of active sensors effectively. The proposed algorithm cannot only relax the above assumption, but also has a competitive performance. Furthermore, the algorithm is also applicable for both stationary and non-stationary environments. Simulation results on the convergence time, the network life and the ability of tracking dynamic QoS have demonstrated the effectiveness of the proposed algorithm.

Dynamic quality of service model for improving performance of multimedia real-time transmission in industrial networks.

Nowadays, quality of service (QoS) is very popular in various research areas like distributed systems, multimedia real-time applications and networking. The requirements of these systems are to satisfy reliability, uptime, security constraints and throughput as well as application specific requirements. The real-time multimedia applications are commonly distributed over the network and meet various time constraints across networks without creating any intervention over control flows. In particular, video compressors make variable bit-rate streams that mismatch the constant-bit-rate channels typically provided by classical real-time protocols, severely reducing the efficiency of network utilization. Thus, it is necessary to enlarge the communication bandwidth to transfer the compressed multimedia streams using Flexible Time Triggered- Enhanced Switched Ethernet (FTT-ESE) protocol. FTT-ESE provides automation to calculate the compression level and change the bandwidth of the stream. This paper focuses on low-latency multimedia transmission over Ethernet with dynamic quality-of-service (QoS) management. This proposed framework deals with a dynamic QoS for multimedia transmission over Ethernet with FTT-ESE protocol. This paper also presents distinct QoS metrics based both on the image quality and network features. Some experiments with recorded and live video streams show the advantages of the proposed framework. To validate the solution we have designed and implemented a simulator based on the Matlab/Simulink, which is a tool to evaluate different network architecture using Simulink blocks.

A Northbound API for QoS Management in Real-Time Interactive Applications on Software-Defined Networks

Real-Time Online Interactive Applications (ROIA), e.g., multiplayer online games and simulation-based e-learning, make high Quality of Service (QoS) demands on the underlying network. These demands depend on the number of users and the actual application state and, therefore, vary at runtime. Traditional networks have very limited possibilities of influencing the network behavior to meet the dynamic QoS demands, such that most ROIA use the underlying network on a best-effort basis. The emerging Software-Defined Networking (SDN) technology decouples the control and forwarding logic from the network infrastructure, making the network behavior programmable for applications. This paper analyses ROIA requirements on the underlying network and describes the specification and design of an SDN Northbound API that allows ROIA applications to specify their dynamic network requirements and to meet them at runtime using SDN networks. Index Terms—Quality of service, network management, software-defined networking, soft real-time applications, real- time framework (RTF)

支持风险偏好的Web 服务动态组合方法

With the prevalence of services-oriented architecture, cloud computing and software as a service, there is an increasing number of service providers offering the same or similar functionality but differing in QoS (Quality of Services). Therefore, appropriate service providers should be dynamically selected for the abstract services of a composite service with the objective of optimizing the overall QoS satisfaction. However, traditional service composition approaches select an identical service binding solution for all of the service requests until the next self-adaptation. In addition, these approaches only consider the expected QoS of candidate services but neglect the potential risk from the dynamic QoS fluctuation. As a result, the SLA (Service Level Agreement) between service requesters and providers may be frequently violated, thus affecting the business value of composite service providers. In this paper, we propose a dynamic Web service composition approach that supports different risk appetites. The proposed approach considers both the expected value and fluctuation of QoS, and applies portfolio theory to generate multiple service binding solutions satisfying the given risk appetite, thereby controlling the risk and adapting to different risk appetites. Compared with global service selection approaches, the experimental study shows that our approach can effectively reduce the SLA violations and improve the business value of composite services.

Lightweight routing with dynamic interests in wireless sensor and actor networks

Wireless sensor and actor networks (WSANs) have been increasingly popular for environmental monitoring applications in the last decade. While the deployment of sensor nodes enables a fine granularity of data collection, resource-rich actor nodes provide further evaluation of the information and reaction. Quality of service (QoS) and routing solutions for WSANs are challenging compared to traditional networks because of the limited node resources. WSANs also have different QoS requirements than wireless sensor networks (WSNs) since actors and sensor nodes have distinct resource constraints.In this paper, we present, LRP-QS, a lightweight routing protocol with dynamic interests and QoS support for WSANs. LRP-QS provides QoS by differentiating the rates among different types of interests with dynamic packet tagging at sensor nodes and per flow management at actor nodes. The interests, which define the types of events to observe, are distributed in the network. The weights of the interests are determined dynamically by using a nonsensitive ranking algorithm depending on the variation in the observed values of data collected in response to interests. Our simulation studies show that the proposed protocol provides a higher packet delivery ratio and a lower memory consumption than the existing state of the art protocols.

2013 Association for Radiologic & Imaging Nursing Convention

Wireless sensor and actor networks (WSANs) have been increasingly popular for environmental monitoring applications in the last decade. While the deployment of sensor nodes enables a fine granularity of data collection, resource-rich actor nodes provide further evaluation of the information and reaction. Quality of service (QoS) and routing solutions for WSANs are challenging compared to traditional networks because of the limited node resources. WSANs also have different QoS requirements than wireless sensor networks (WSNs) since actors and sensor nodes have distinct resource constraints.In this paper, we present, LRP-QS, a lightweight routing protocol with dynamic interests and QoS support for WSANs. LRP-QS provides QoS by differentiating the rates among different types of interests with dynamic packet tagging at sensor nodes and per flow management at actor nodes. The interests, which define the types of events to observe, are distributed in the network. The weights of the interests are determined dynamically by using a nonsensitive ranking algorithm depending on the variation in the observed values of data collected in response to interests. Our simulation studies show that the proposed protocol provides a higher packet delivery ratio and a lower memory consumption than the existing state of the art protocols.