A Generalized Nesterov-Accelerated Hessian-Vector-Based Latent Factor Analysis Model for QoS Prediction

A latent factor analysis (LFA) model can efficiently address a high-dimensional and sparse (HiDS) matrix with a stochastic gradient descent (SGD) algorithm. However, an SGD-based LFA model's performance depends heavily on its hyper-parameters. The popular method is based on grid-search, which fails because of expensive computation and time-consuming. Aiming at implementing a hyper-parameter-free LFA model, this study proposes an adaptive moment estimation-incorporated particle swarm optimization (Adam-PSO) algorithm that efficiently addresses the premature issues in a PSO algorithm. With achieved the hyper-parameter adaptation in an SGD-based LFA model, an Adam-PSO-based LFA (APL) model possessing hyper-parameter-free training is further implemented. Empirical studies on four HiDS matrices indicate that compared with state-of-the-art models with hyper-parameter adaptation settings, an APL model achieves the most efficient hyper-parameter-free training and highly competitive prediction accuracy for missing data of an HiDS matrix. Hence, it fits the need of real applications with high scalability and efficiency.

The latent factor analysis (LFA) model has been widely used to uncover latent relationships from high-dimensional sparse (HiDS) matrices. However, the performance of LFA depends largely on the hyper-parameter value used in the model training. Traditional hyper-parameter tuning methods such as grid search suffer from inefficiency and inaccuracy. In recent years, the particle swarm optimization (PSO) algorithm offers an intelligent approach to adaptively adjust the hyper-parameter of LFA. However, the global optimal solution of the hyper-parameter tuning problem is not fixed due to its dynamic decision space. Therefore, it is difficult for PSO to determine the best hyper-parameter for each training iteration. To address this problem, this paper proposes a novel hyper-parameter adaptive adjustment algorithm called dynamic stochastic reorientation PSO (DSR-PSO) that adapts to constantly changing decision spaces. By randomly adjusting the search directions of particles and perturbing the elite particles, the dynamic property of the DSR-PSO can be enhanced, so that the hyper-parameter can be adjusted in real time throughout the model training process. Furthermore, this paper proves the convergence of the DSR-PSO and gives its convergence condition by discussing the distribution of the characteristic roots. Finally, this paper proposes the DSR-PSO-based LFA (DPL) model by incorporating the DSR-PSO-based hyper-parameter adjustment into the LFA to promote its model training, and analyzes its complexity. Experimental results on benchmark datasets show that the proposed DPL surpasses state-of-the-art LFA models in terms of accuracy and efficiency.

Latent factor analysis (LFA) is highly efficient in knowledge discovery from high-dimensional and sparse (HiDS) matrices frequently encountered in big data and web service related applications. A stochastic gradient descent (SGD) algorithm is commonly adopted as a learning algorithm for LFA owing to its high efficiency. However, its sequential nature makes it less scalable when processing large-scale data. Although an alternating SGD algorithm decouples an LFA process to achieve parallelization, its performance relies on its hyper-parameter selection that is highly expensive to tune. To address it, this paper presents three adaptive alternating SGD algorithms, thus leading to three Parallel Adaptive LFA (PAL) models for LFA on large-scale HiDS matrices. Experimental studies on HiDS matrices from industrial service applications show that the proposed PAL models perform significantly better than existing ones in terms of both convergence rate and computational efficiency, as well as achieve competitive prediction accuracy for missing data.

High-dimensional and sparse (HiDS) matrices are frequently adopted to describe the complex relationships in various big data-related systems and applications. A Position-transitional Latent Factor Analysis (PLFA) model can accurately and efficiently represent an HiDS matrix. However, its involved latent factors are optimized by particle-swarm-optimization-incorporated stochastic gradient descent with the specific gradient direction step-by-step, which may cause a suboptimal solution. To address this issue, this paper proposes a <u>S</u>equential-<u>G</u>roup-<u>D</u>ifferential-<u>E</u>volution (SGDE) algorithm to refine the latent factors optimized by a PLFA model, thereby achieving a highly-accurate SGDE-PLFA model to HiDS matrices. The main idea of SGDE-PLFA is two-fold. First, it divides the obtained latent factors by PLFA into row and column sub-groups to ensure the efficient low-dimensional optimization. Second, it initializes and optimizes these sub-group latent factors with our improved Differential Evolution algorithm to search more accurate solutions nearby the suboptimum. As demonstrated by the extensive experiments on four HiDS matrices, an SGDE-PLFA model outperforms the state-of-the-art models in representing HiDS matrices.

How to extract useful knowledge from a high-dimensional and sparse (HiDS) matrix efficiently is critical for many big data-related applications. A latent factor (LF) model has been widely adopted to address this problem. It commonly relies on an iterative learning algorithm like stochastic gradient descent. However, an algorithm of this kind commonly consumes many iterations to converge, resulting in considerable time cost on large-scale datasets. How to accelerate an LF model's training process without accuracy loss becomes a vital issue. To address it, this study innovatively proposes a <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">m</u> ultilayered-and-randomized <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">l</u> atent <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">f</u> actor (MLF) model. Its main idea is two-fold: a) adopting randomized-learning to train LFs for implementing a ‘one-iteration’ training process for saving time; and 2) adopting the principle of a generally multilayered structure as in a deep forest or multilayered extreme learning machine to structure its LFs, thereby enhancing its representative learning ability. Empirical studies on six HiDS matrices from real applications demonstrate that compared with state-of-the-art LF models, an MLF model achieves significantly higher computational efficiency with satisfactory prediction accuracy. It has the potential to handle LF analysis on a large scale HiDS matrix with real-time requirements.

For Web services providing similar functionality, quality of service (QoS) is the main factor to differentiate them. In this paper, we propose a new approach which enables a more efficient service selection in dynamic environment of QoS characteristics. First, we present a new broker-based architecture for Web services in which the role of service broker is separated from service quality analyzer. Then we propose a three-phase technique that is performed by a third party broker. In phase one, we use the QoS Class concept to categorize service providers based on QoS issues. In phase two, we suppress the uncertainties of QoS provided by service provider leveraging recurrent counter classes. Finally, we consider a third phase which is referenced to an AI planning algorithm the result of which produces a chain of services while considering service requesters restrictions in service selection problems. In this paper, not only do we consider the past QoS acquisition of services, but we consider also the online variation of QoS characteristics in serving services. Hence, the QoS assessment is dynamic and performed online.

With the widespread adoption of cloud computing, Service-Orientated Architecture (SOA) facilitates the deployment of large-scale online applications in many key areas where quality and reliability are critical. In order to ensure the performance of cloud applications, Quality of Service (QoS) is widely used as a key metric to enable QoS-driven service selection, composition, adaption, etc. Since QoS data observed by users is sparse due to technical constraints, previous studies have proposed prediction approaches to solve this problem. However, the dynamic nature of the cloud environment requires timely prediction of time-varying QoS values. In addition, unreliable QoS data from untrustworthy users may significantly affect the prediction accuracy. In this paper, we propose a credible online QoS prediction approach to address these challenges. We evaluate user credibility through a reputation mechanism and employ online learning techniques to provide QoS prediction results at runtime. The proposed approach is evaluated on a large-scale real-world QoS dataset, and the experimental results demonstrate its effectiveness and efficiency in unreliable cloud environment.

For Web services providing similar functionality, Quality of service (QoS) is the main factor to differentiate them. In this paper, we propose a new approach which enables a more efficient service selection in dynamic environment of QoS Characteristics. First, we present a new Broker-based architecture for Web Services in which the role of service broker is separated from Service Quality Analyzer. Then we propose a three-phase technique that is performed by a third party broker. In phase one, we use the QoS Class concept to categorize service providers based on QoS issues. In phase two, we suppress the uncertainties of QoS provided by service provider using Learning Automata algorithm. Finally, we consider a third phase which is referenced to an AI Planning algorithm the result of which produces a chain of services while considering service requesters restrictions in service selection problems. In this paper, not only do we consider the past QoS acquisition of services, but we consider also the online variation of QoS characteristics in serving services. Hence, the QoS assessment is dynamic and performed online.

Dynamic relationships are frequently encountered in big data and services computing-related applications, like dynamic data of user-side QoS in Web services. They are modeled into a high-dimensional and sparse (HiDS) tensor, which contain rich knowledge regarding temporal patterns. A non-negative latent factorization of tensors (NLFT) model is very effective in extracting such patterns from an HiDS tensor. However, it commonly suffers from overfitting with improper regularization schemes. To address this issue, this article investigates NLFT models with diversified regularization schemes. Six regularized NLFT models, i.e., <inline-formula><tex-math notation="LaTeX">$L_{2}, L_{1}$</tex-math></inline-formula> , elastic net, log, dropout, and swish-regularized ones, are proposed and carefully investigated. Moreover, owing to their diversified regularization designs, they possess strong model diversity to achieve an effective ensemble. Empirical studies on HiDS QoS tensors from real applications demonstrate that compared with state-of-the-art models, the proposed ones better describe the temporal patterns hidden in an HiDS tensor, thereby achieving significantly higher prediction accuracy for missing data. Moreover, their ensemble further outperforms each of them in terms of prediction accuracy for missing QoS data.

Services as part of our daily life represent an important means to deliver value to their consumers and have a great economic impact for organizations. The service consumption and their exponential proliferation show the importance and acceptance by their customers. In this sense, it is possible to predict that the infrastructure of future cities will be supported by different kind of services, such as smart city services, open data services, as well as common services (e.g., e-mail services), etc. Nowadays a large percentage of services are provided on the web and are commonly called web services (WSs). This kind of services has become one of the most used technologies in software systems. Among the challenges when integrating web services in a given system, requirements-driven selection occupies a prominent place. A comprehensive selection process needs to check compliance of Non-Functional Requirements (NFRs) which can be assessed by analyzing the Quality of Service (QoS). In this paper, we describe a framework called WeSSQoS that aims at ranking available WSs based on the comparison of their QoS and the stated NFRs. The framework is designed as an open Service Oriented Architecture (SOA) that hosts a configurable portfolio of normalization procedures and ranking algorithms which can be selected by users when starting a selection process. The QoS data from WSs can be obtained either from a static, WSDL-like description or dynamically through monitoring techniques. WeSSQoS is designed to work over multiple WS repositories and QoS sources. The impact of having a portfolio of different normalization and ranking algorithms is illustrated with an example.

The purpose of this study was to examine the structural relationship between customer participation, customer citizenship behavior, service quality and customer satisfaction of sport center. The subjects were ten sport center customer and 400 customers was collected and 395 of them were chosen as for final data analysis. Data analyses included frequency analysis, confirmatory factor analysis, reliability analysis, correlation analysis and structural equation model with SPSSWIN Ver. 21.0 and AMOS 21.0. The result of the study were as follows. Firstly, customer participation had a significant effect on service quality. Secondly, customer citizenship behavior had a significant effect on service quality. Thirdly, service quality had a significant effect on customer satisfaction.

The idea behind the proposition of Networks-on-Chip (NoCs) for modern and future systems on chip capitalizes on the fact that busses do not scale well when shared by a large number of cores. Even if NoC research is a relatively young field, the literature abounds with propositions of NoC architectures. Several of these propositions claim providing quality of service (QoS) guarantees, which is essential for real time and multimedia applications. The most widespread approach to attain some degree of QoS guarantee relies on a two-step process. The first step is to characterize application performance through traffic modeling and simulation. The second step consists in tuning a given network template to achieve some degree of QoS guarantee. These QoS targeted NoC templates usually provide specialized structures to allow either the creation of connections (circuit switching) or the assignment of priorities to connectionless flows. It is possible to identify three drawbacks in this two-step process approach. First, it is not possible to guarantee QoS for new applications expected to run on the system, if those are defined after the network design phase. Second, even with end-to-end delay guarantees, connectionless approaches may introduce jitter. Third, to model traffic precisely for a complex application is a very hard task. If this problem is tackled by oversimplifying the modeling phase, errors may arise, leading to NoC parameterization that is poorly adapted to achieve the required QoS. This Chapter has two main objectives. The first one is to evaluate the area-performance trade-off and the limitations of circuit switching and priority scheduling to meet QoS. This evaluation will show where such implementations are really suited for QoS, and when more elaborate mechanisms to meet QoS are needed. The second objective comprises proposing a method, called rate-based scheduling, to approach QoS requirements considering the execution time state of the NoC. The evaluation of circuit switching and priority scheduling show that: (i) circuit switching can guarantee QoS only to a small number of flows; the technique do not scale well, and can potentially waste significant bandwidth; (ii) priority-based approaches may display best-effort behavior and, in worst-case situations, may lead to unacceptable latency for low priority flows, besides being subject to jitter. In face of these limitations, rate-based scheduling arises as an option to improve the performance of QoS flows when varying traffic scenarios are used.

5G network is an enhanced communication network, designed to converge the requirements of quality of service (QoS) parameters, data and capacity, by means of signals with high QoS and high-speed data rate. Several state-of-art technologies are involved to obtain the requirement of energy efficient communication system with increased number of users, devices, higher data rate with low latency. This paper presents a system which demonstrates the energy and spectral efficiency achieved for various number of nodes in a specific area. This study addresses the improvement in energy and spectral efficiency when the proposed algorithm is used. The proposed algorithm is a combination of swarm based artificial bee colony (ABC) algorithm with neural network. Experimental results have been carried out to observe the performance of QoS parameters such as bit error rate (BER), throughput, power consumption and mean square error (MSE). The maximum energy efficiency achieved is 34% and Spectral efficiency is 36%.

SLA is a contract between service providers and consumers, mandating specific numerical target values which the service needs to achieve. For service providers, detecting and preventing SLA violation becomes very important to enhance customer trust and avoid penalty charges. Therefore, it is necessary for providers to detect and forecast possible service violations. However, this is difficult to achieve when dealing with service violation in a cloud-based system due to multiple Quality of Service (QoS) parameters. In this work, manifold learning is used to reduce the high dimensionality problem arising from multiple QoS data into 1-D output of violation level (low, medium, high) data. From the transformed data, service violation will be detected as well as predicted based on violation level data. The violation level is obtained from aggregate value of each QoS weightage. Based on QoS data of 14 days, manifold learning is able to scale down 5 various parameters into a single parameter before detection and prediction process is performed. The prediction accuracy of Support Vector Regression as the time series analysis technique used is able to achieve 80%.

This study attempted to investigate the mediating effects of satisfaction with college major in the influence of quality of education service on career maturity. For this, a total of 410 junior and 2-year, 4-year college students majoring in beauty in Seoul and Gyeonggi-do were examined. Research models were set according to the analysis of the results of a pilot study, and then 4 research hypotheses were formulated. The collected data were analyzed by descriptive statistics, confirmatory factor analysis, correlation analysis and structural equation model analysis, and the results found the followings: First, the quality of educational services had a positive influence on satisfaction with academic major with statistical significance. Second, the quality of educational services revealed a positive effect on career maturity with statistical significance. Third, satisfaction with academic major showed a positive influence on career maturity with statistical significance. Fourth, satisfaction with academic major revealed a partial mediating effect in the effects of the quality of educational services on career maturity. According to the above results, four hypotheses were all supported. It is anticipated that the study results would be available as the empirical data needed to improve the quality of college education services. They would also be useful in developing an efficient career development and employment plan for college students majoring in beauty.