Service Quality Of Service Research Articles

Neural Net Time Series Forecasting Framework for Time-Aware Web Services Recommendation

Abstract The convergence of Social Mobility Analytics and Cloud (SMAC) technologies resulted in an unexpected upsurge of web services on the internet. The flexibility and rental approach of the cloud makes it an attractive option for the deployment of web services-based applications. Once a number of web services are available to gratify the similar functionalities, then the choice of the web service based on personalized quality of service (QoS) parameters plays an important role in deciding the selection of the web service. The role of time is rarely being discussed in deciding the QoS of web services. The delivery of QoS is not made as declared due to the correlated behavior of the non-functional performance of web services with the invocation time. This happens because service status usually changes over time. These limitations have affected the performance of neighborhood-based collaborative filtering. Hence, the design of the time aware web service recommendation system based on the personalized QoS parameters is very crucial and turns out to be a challenging research issue. In the current work, various neural network models like Levenberg Marquardt (LM), Bayesian-Regularization (BR) and Scaled-Conjugate-Gradient (SCG) are used for experimentation with the input time series to find the best fit model for the prediction of personalized QoS based web services recommendation. The Pearson’s Correlation Coefficient is used as an evaluation metric and their value for the prediction of Response time is found to be 0.84985 and for throughput (TP) is found to be 0.99082 with the Levenberg Marquardt algorithm. Thus, the experimental results show that the with the Levenberg Marquardt model of Time Series Forecasting for Web Services Recommendation Framework is performing better in case of Response time as well as throughput.

An Econometric Time Series Forecasting Framework for Web Services Recommendation

The convergence of SMAC technologies resulted in an unexpected upsurge of web services on the internet. The flexibility and rental approach of the cloud makes it an attractive option for the deployment of web services-based applications. Once a number of web services are available to gratify the similar functionalities, then the choice of the web service based on personalized quality of service (QoS) parameters plays an important role in deciding the selection of the web service. The role of time is rarely being discussed in deciding the QoS of web services. The delivery of QoS is not made as declared due to the correlated behavior of non-functional performance of web services with the invocation time. This happens because service status usually changes over time. These limitations have affected the performance of neighborhood-based collaborative filtering. Hence, the design of the time aware web service recommendation system based on the personalized QoS parameters is very crucial and turn out to be a challenging research issue. In the current work, various econometric models are used for experimentation with the input time series to find the best fit model for the prediction of personalized QoS based web services recommendation. The Akaike Information Criteria (AIC) and Bayesian Information Criteria (BIC) value is used as an evaluation metric and their value for the prediction of Response time is found to be 1.7392e+04 and 1.7416e+04 respectively with the ARMA model. The AIC and BIC value for throughput (TP) is found to be 1.5249e+04 and 1.5334e+04 respectively with the ARIMA. The value of variance is 13.2437 and 6.8131 for RT and TP respectively which is also the lowest among other models with t-statistic greater than the p-value. Thus, the experimental results show that the ARMA and ARIMA model of Time Series Forecasting for Web Services Recommendation Framework is performing better in case of Response time and throughput respectively.

Software Defined Network-Based Management for Enhanced 5G Network Services

Due to the fast development in communication technology and the emerging usage of Internet of Things (IoT) devices that produce a huge amount of data, the fifth generation (5G) mobile network is introduced to support this development. This mobile network can provide many advanced communication features in cellular phones. But unfortunately, this technology faces many challenges. One of its defective challenges is the management of a massive number of devices running different services, so Software Defined Network (SDN) is proposed as a key technology to overcome this drawback. SDN architecture provides higher flexibility, scalability, cost-effectiveness, and energy efficiency in 5G mobile networks. There are usually different architectures for the SDN control plane. We study some of these architectures, and we conciliate the usage of Logically Centralized-Physically Distributed (LC-PD) controller management architecture in 5G networks. This architecture enables providing higher throughput, and lower latency compared to other control plane architectures. In this paper, we focus on the demonstration that the LC-PD control plane architecture improves communication efficiency and the Quality of Services (QoS) of running internet services in the 5G mobile network. We use the Mininet-WIFI emulator in our simulation tests. Our conducted simulations show that employing the LC-PD control plane architecture in 5G networks enhances the QoS of Internet services compared to other SDN implementations.

THE INVESTIGATION AND EVALUATION MULTISERVICE NETWORK NGN/IMS FOR MULTIMEDIA TRAFFIC

Òhe functional architecture of the subsystem picture messaging-IMS (Internet Protocol Multimedia Subsystem) for multimedia session management for multiservice NGN/IMS networks that determine the interaction systems and NGN protocols are investigated. The basic element of the IMS core network architecture are session control function CSCF (Call/Session Control Function) which is implemented on the SIP-server (Session Initiation Protocol) using the protocol. A block diagram of the functioning of the traffic service model of multimedia services in the NGN/IMS network when establishing sessions that use the network elements of the user data server HSS (Home Subscriber Server), CSCF control system core, signaling and media gateways (SGW & MGW, Signaling Gateway & Media Gateway). The characteristics of the effectiveness of the IMS and found that in the NGN / IMS multimedia services network in real time significantly alter the traffic characteristics as a service, and efficiency, which requires new models and approaches to assess the main indicators of quality of the service Triple Play. Based on the analysis of the quality NGN/IMS networks in the provision multimedia services, the mathematical model for evaluating the quality of services. This model takes into account the properties of self-similar random process with Hurst index and describes the quality of functioning of NGN/IMS architectural concept. Based on the model studied when performing voice services with the ability to activate multimedia applications, video telephony, multimedia traffic, Triple Play services. According to the study of mathematical models of NGN / IMS networks using SIP-servers analytical expressions to assess the indicators of a common set of operating characteristics of IP-based networks, which take into account the recommendations of ITU-T Y.1540 and Y.2000 and determined the average waiting time in the queue IMS system in the provision of multimedia services with the required parameters, providing a guaranteed quality of service QoS (Quality of Service). According to the latest recommendations of the ITU-T and based on SMO with queues can be defined by five network characteristics that are indicative of the effectiveness of multi-service NGN/IMS networks and an important indicator of QoS.

IBGSS: An Improved Binary Gravitational Search Algorithm based search strategy for QoS and ranking prediction in cloud environments

Quality of Service (QoS) value prediction and QoS ranking prediction have their significance in optimal service selection and service composition problems. QoS based service ranking prediction is an NP-Complete problem which examines the order of ranked service sequence with respect to the unique QoS requirements. To address the NP-Complete problem, greedy and optimization-based strategies such as CloudRank and PSO have been widely employed in service oriented environments. However, they pose several challenges with respect to the similarity measure based QoS prediction, trap at local optima, and near optimal solution. Hence, this paper presents Improved Binary Gravitational Search Strategy (IBGSS), an optimization based search strategy to address the challenges in the state-of-the-art QoS value prediction and service ranking prediction techniques. IBGSS employs improved cosine similarity measure, and Newton–Raphson inspired Binary Gravitational Search Algorithm (NR-BGSA) for accurate QoS value prediction and optimal service ranking prediction respectively. The effectiveness of IBGSS over the state-of-the-art QoS value prediction and ranking prediction techniques was validated using two real world QoS datasets, namely WSDream#1 and web service QoS dataset in terms of various statistical measures (Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Average Precision Correlation (APC)).

Spatial-temporal data-driven service recommendation with privacy-preservation

The ever-increasing popularity of web service sharing communities have produced a considerable amount of web services that share similar functionalities but vary in Quality of Services (QoS) performances. To alleviate the heavy service selection burden on users, lightweight recommendation ideas, e.g., Collaborative Filtering (CF) have been developed to aid users to select their preferred services. However, existing CF methods often face two challenges. First, service QoS is often context-aware and hence depends on the spatial and temporal information of service invocations heavily. While it requires challenging efforts to integrate both spatial and temporal information into service recommendation decision-making process simultaneously. Second, the location-aware and time-aware QoS data often contain partial sensitive information of users, which raise an emergent privacy-preservation requirement when performing service recommendations. In view of above two challenges, in this paper, we integrate the spatial-temporal information of QoS data and Locality-Sensitive Hashing (LSH) into recommendation domain and bring forth a location-aware and time-aware recommendation approach considering privacy concerns. At last, a set of experiments conducted on well-known WS-DREAM dataset show the feasibility of our approach.

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

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

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

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

Neighborhood-aware web service quality prediction using deep learning

With the rapid growth of web services on the Internet, it becomes more difficult for users who want to choose the high-quality web services from a large number of functionally equivalent candidate services. Therefore, the prediction of quality of service (QoS) values according to the history of web services has received extensive attention. In recent years, deep learning has achieved great success in speech recognition, image processing, and natural language understanding. However, it is rarely applied to the service recommendation field. Therefore, a novel approach for QoS prediction named NDL (neighborhood-aware deep learning) is proposed. NDL first gets the Top-k neighbors of the user and the service through the Pearson correlation coefficient according to the service QoS information. Then, it extracts the potential features of the user neighbor and the service neighbor; after that, it inputs the QoS values of the user and the user neighbor as well as the QoS values of the service and service neighbors as a convolutional neural network. The results of experiments conducted on a real-world dataset demonstrate that the NDL significantly outperforms the current QoS prediction method in prediction accuracy.

Advances on QoS‐aware web service selection and composition with nature‐inspired computing

Service-oriented architecture is becoming a major software framework for complex application and it can be dynamically and flexibly composed by integrating existing component web services provided by different providers with standard protocols. The rapid introduction of new web services into a dynamic business environment can adversely affect the service quality and user satisfaction. Therefore, how to leverage, aggregate and make use of individual component's quality of service (QoS) information to derive the optimal QoS of the composite service which meets the needs of users is still an ongoing hot research problem. This study aims at reviewing the advance of the current state-of-the-art in technologies and inspiring the possible new ideas for web service selection and composition, especially with nature-inspired computing approaches. Firstly, the background knowledge of web services is presented. Secondly, various nature-inspired web selection and composition approaches are systematically reviewed and analysed for QoS-aware web services. Finally, challenges, remarks and discussions about QoS-aware web service composition are presented.

A Load Balancing Vertical Handoff Algorithm Considering QoS of Users for Heterogeneous Networks in Power Communication

With the increase of the number of users and the complexity of services, only single communication network cannot meet all its requests in power communication networks. Therefore, heterogeneous networks are an effective solution. Vertical handoff (VHO) is an important step in the process of heterogeneous network convergence. An appropriate handoff algorithm can improve the quality of service (QoS) of users. In order to solve the problem of network congestion caused by a large number of users connecting to partial networks in heterogeneous networks, a vertical handoff algorithm based on load balance is proposed. Among the networks that can meet users’ requirements, the most load-balancing network is obtained using optimal algorithm. In addition, to consider users’ requirements and ensure the QoS of network service, we use analytic hierarchy process (AHP) to weight different networks. This algorithm can take full use of the various network resources, so that the distribution of the load among networks is average. The simulation results show that the proposed algorithm can select the network according to the user’s demands and each network has close network utilization under the algorithm.

Framework for Cloud Service Selection and Ranking

Cloud technology provides pay as you go computing resources to its user and these resources are offered in the form of different services. The research paper focus that the customers’ requirements which can be fulfilled through multiple conflicting objectives. Therefore cloud services selection and ranking is a big challenge for the user. The paper address how the cloud service performance can be evaluated by using quality of service (QoS) attributes. In order to filter out the cloud services and to provide ranking firstly the cloud models are discussed and introduction of quality of service parameters. Then the framework is proposed for evaluating QoS and ranking of services. The paper justifies its research with an example taken from previous literature for evaluating the framework.

Multi-Objective Service Composition with QoS Dependencies

Service composition is popular for composing a set of existing services to provide complex services. With the increasing number of services deployed in cloud computing environments, many service providers have started to offer candidate services with equivalent functionality but different Quality of Service (QoS) levels. Therefore, QoS-aware service composition has drawn extensive attention. Most existing approaches for QoS-aware service composition assume a service's QoS values are not correlated to those of other services. However, QoS dependency exists in real life, and impacts the overall QoS values of the composite services. In this article, we study QoS dependency-aware service composition considering multiple QoS attributes. Based on the Pareto set model, we focus on searching for a set of Pareto optimal solutions. A candidate pruning algorithm for removing the unpromising candidates is proposed, and a service composition algorithm using Vector Ordinal Optimization techniques is designed. Simulation experiments are conducted to validate the efficiency and effectiveness of our algorithms. We are the first to take advantage of Vector Ordinal Optimization techniques to search for Pareto optimal composition solutions with QoS dependency involved. The capturing of QoS dependency enables us to find truly desirable solutions.

Availability-Aware Service Placement Policy in Fog Computing Based on Graph Partitions

Fog computing extends the cloud to where things are by placing applications closer to the users and Internet of Things devices. The placement of those applications, or their services, has an important influence on the performance of the fog architecture. Improving the availability and the latency of the applications is a challenging task due to the complexity of this type of distributed system. In this paper, we propose a service placement policy inspired by complex networks. We are able to increase the service availability and the quality of service (QoS) satisfaction rate by first mapping applications to communities of fog devices and then transitively placing the services of the applications on the fog devices of the community. The underlying idea is to place as many interrelated services as possible in the devices closest to the users. We compare our solution with an integer linear programming approach, and the simulation results show that our proposal obtains improved QoS satisfaction and service availability.

An adaptive and scalable framework for automated service discovery

A large number of redundant Web services, offering similar functionality with different quality of service (QoS), can truly benefit an organization, if offered an adaptive discovery framework. State-of-the-art approaches cannot meet these requirements for enterprise setups that offer a managed infrastructure. This paper proposes a self-healing and self-managed open-source adaptive QoS-aware discovery framework. An optimal global optimization approach is developed as part of the framework. It yields the shortest route to the best service. Simulation results demonstrate the suitability of the proposed framework to run-time QoS changes and service failures. The run-time adaptation of the system during service downtime is analyzed using graphs. These graphs illustrate the change in client QoS constraints against time. The results indicate the least impact on the client during service failure using the proposed framework.

Your neighbors are misunderstood: On modeling accurate similarity driven by data range to collaborative web service QoS prediction

Quality of service (QoS) is a set of non-functional attributes of Web services for differentiating enriched Web services with same or similar functionality. Predicting the unknown QoS of Web services for service users is often required for any QoS based service computing because QoS plays a fundamental role in reliable Web service recommendation, composition and selection. Existing collaborative filtering based QoS prediction methods suffer from a serious acclimatization issue caused by the difference of QoS data range, which dramatically degrades the prediction accuracy and even impedes its adaptability. The fact that Web service QoS data exhibit large service effect with different data ranges, is verified on public real-world datasets. In this study, we aim to tackle the problem of QoS prediction while considering the influences of QoS data range in the context of collaborative filtering. In particular, a simple yet effective similarity model called JacMinMax, which is driven by QoS data range, is designed. Furthermore, two neighborhood selection strategies using JacMinMax are proposed, and the obtained neighbors are systemically integrated into neighborhood- and model-based methods for collaborative QoS predictions. Experimental results show that the proposed method efficiently alleviates the influence of the concerned QoS data ranges, and performs better than many state-of-the-art approaches with respect to accuracy.

Towards the optimality of QoS-aware web service composition with uncertainty

Quality of service (QoS)-aware web service composition (QWSC) has recently become one of the most challenging research issues. Although much work has been investigated, they mainly focus on certain QoS of web services, while QoS with uncertainty exposes the most important characteristic in real and highly dynamic environment. In this paper, with the consideration of uncertain service QoS and user's preferences, we model the issue of uncertain QoS-aware WSC via interval number and translate it into a multi-objective optimisation problem with global QoS constraints of user's preferences. The encoded optimisation problem is solved by an non-deterministic multi-objective evolutionary algorithm, which exploits new genetic encoding schema, the strategy of crossover and uncertain interval Pareto comparison. To validate the feasibility, large-scale experiments have been conducted on simulated datasets. The results demonstrate that our proposed approach can effectively and efficiently find optimum composite service solutions set with satisfactory convergence.

Towards the optimality of QoS-aware web service composition with uncertainty

Quality of service (QoS)-aware web service composition (QWSC) has recently become one of the most challenging research issues. Although much work has been investigated, they mainly focus on certain QoS of web services, while QoS with uncertainty exposes the most important characteristic in real and highly dynamic environment. In this paper, with the consideration of uncertain service QoS and user's preferences, we model the issue of uncertain QoS-aware WSC via interval number and translate it into a multi-objective optimisation problem with global QoS constraints of user's preferences. The encoded optimisation problem is solved by an non-deterministic multi-objective evolutionary algorithm, which exploits new genetic encoding schema, the strategy of crossover and uncertain interval Pareto comparison. To validate the feasibility, large-scale experiments have been conducted on simulated datasets. The results demonstrate that our proposed approach can effectively and efficiently find optimum composite service solutions set with satisfactory convergence.

A Factorization Machine-Based QoS Prediction Approach for Mobile Service Selection

Mobile services allow us to access the abundant and various resources (including data and services) on the Internet or devices in the physical world via wireless network technologies. It becomes increasingly popular to create mobile applications by combining existing mobile services. Mobile service selection is an important issue since different services with equivalent functions may have quite different qualities (e.g., performance). Even the same service may present different performances due to the volatility of mobile environments and move of users. Hence, getting aware of the quality of mobile services is a crucial need in service selection. To meet this need, a dozen of quality-of-service (QoS) prediction approaches have been proposed for traditional Web services and mobile services. However, their prediction accuracy and time efficiency still have plenty of room for improvement. This paper proposes a collaborative filtering approach to predict the QoS of mobile services based on factorization machines. Factorization machines significantly improve the traditional collaborative filtering techniques in both accuracy and time efficiency. The proposed approach revamps the classic factorization machine model by incorporating the locations of service users to better fit the mobile environments. The experimental results based on real-world QoS data show that the proposed approach outperforms the other collaborative filtering approaches.

Detecting a Business Anomaly Based on QoS Benchmarks of Resource-service Chains for Collaborative Tasks in the IoT

In the Internet of Things (IoT), the online performance of many online services is determined by their distribution resources, which are connected to many different devices. The expected performance of a resource service primarily depends on the optimal use of the service in satisfying end-to-end quality requirements to support its successful execution. Therefore, the performance of a resource service is dynamic and should be discovered as a benchmark to detect a performance anomaly online. A performance anomaly is referred to as a business anomaly because it depends on its usage. The performance is measured by the quality of service (QoS) that is possessed by a resource service. In this paper, an approach based on the resource service QoS is proposed to detect a business anomaly via mining business process data in collaborative tasks in the IoT. First, a resource-service chain (RSC) is considered to be an analysis object because resource services are employed as a “service flow” by a business process. The similarity between any two RSCs is measured according to the QoS indicator values of resource services. Based on the similarity, a clustering algorithm is presented to resolve clustering centers that are considered to be QoS benchmarks. Second, according to the QoS benchmarks of RSCs, the thresholds of QoS indicators of a business anomaly are determined. Third, an algorithm is presented to detect anomalies of the business process. Finally, the proposed approach is illustrated by a simulation experiment. The experimental results show that the approach can be used to effectively detect a business anomaly online.