QoS Properties Research Articles

Development of Trustworthiness for Cloud Service Providers Using DBN-Based Trust Model in Cloud Computing Environment

In order to facilitate diverse computing resources and services, cloud computing has evolve into a promising paradigm on-demand over the internet. To access services, cloud users have to rely on third-party service providers. Choosing a suitable Cloud Service Provider (CSP) with a raise in available cloud services in order to deliver the service safely is considered a serious concern for users. Regrettably, there are various problems that minimize the growth of cloud computing, like privacy, security loss, and control. The security issue is regarded as the most important element that could avoid the evolution of cloud computing. In the cloud environment, to handle the user’s requests, trust measures play a significant role when choosing appropriate service providers. Therefore, trustworthiness evaluation of CSP prior to choosing it to facilitate the service has become a significant obligation in cloud environment. In this work, a trust model, Deep Behavioral Feedback Quality of Service and Statistics based trust (Deep BFQS-trust), is developed to calculate trustworthiness of CSP based on its feedback and behavior, QoS and statistics-based given by the users. Also, to calculate behavioral trust values, various QoS attributes are considered. In order to maintain and calculate feedback trust value for service provider, diverse parameters from service level agreement are utilized. By computing the collective trust, trustworthiness of cloud service provider is judged that is computed by these trust factors. Moreover, the weights of the collective trust are determined by employing Deep belief network (DBN) model. Finally, the proposed Deep BFQS-trust technique is compared with the existing approaches, and exhibits that the proposed model attained utmost trustworthiness and successful interaction with the values of 0.860 and 0.888, respectively.

Web Service Recommendation via Integrating Heterogeneous Graph Attention Network Representation and FiBiNET Score Prediction

The rapid growth in the number and diversity of Web service, coupled with the myriad of similar Web service in functionality, makes it challenging to find most suitable Web service for users to accelerate and accomplish Mashup development. Therefore, this paper proposes a Web service recommendation method via integrating heterogeneous graph attention network representation and FiBiNET (Feature Importance and Bilinear feature Interaction NETwork) score prediction. In this method, firstly, a heterogeneous information service network is constructed by using composite service information, atomic service information, and their respective attribute information. Secondly, the meta-paths are defined according to different semantic information and service similarity matrixes are built by using commuting matrix and meta-path-based similarity measurement technology. A two-layer attention model is designed to calculate the node level attention and meta-path-level attention of the services respectively, and generate the feature representation of Web service. Thirdly, for the Web services in the service cluster, combining their feature representations with multi-dimensional QoS attributes, the FiBiNET is exploited to dynamically learn the importance of features and complex feature interactions, and predict the score of Web services. Finally, the experiments are performed on the real Web service dataset. The experimental results show that the proposed method is better than the other nine methods in terms of accuracy, recall, F1, and AUC, and achieves better classification and recommendation quality.

Norm-based reinforcement learning for QoS-driven service composition

QoS-aware service composition is challenging due to a high number of QoS attributes, component services, and candidate services. Realistic service composition applications operate in uncertain environments where QoS values may change dynamically. Moreover, user requirements on QoS attributes should be considered, and their different nature can make it difficult to express them by adopting relative weights. Reinforcement Learning is proposed as a viable approach in order to deal with the complexity and variability of the environment. In this paper, we propose a novel approach that integrates traditional reinforcement learning with a norm-based paradigm to consider cases where component services may have a different number and types of QoS attributes. In such a way, it is possible to consider additional local requirements that may hold only for specific service components still pursuing a global optimization. Norms allow using a uniform formalism to express qualitative and quantitative as well as hard and soft user requirements. The approach has been tested on a real dataset of 2500 web services showing its performance, scalability, and adaptability properties.

Web Service Recommendation via Combining Bilinear Graph Representation and xDeepFM Quality Prediction

With the increasing number of Web services, how to provide developers with Web services that meet their Mashup requirements accurately and efficiently has become a challenging problem. Therefore, focusing on the problem of “recommending appropriate services to build high-quality Mashup applications”, this paper proposes a Web service recommendation method via combining bilinear graph attention representation and xDeepFM (eXtreme Deep Factorization Machine) quality prediction. This method is based on content and structure-oriented service func-tion classification and combines it with the service invocation prediction based on multi-dimensional quality attributes. Firstly, it uses the Word2Vec model to learn the latent semantic repre-sentations from service description documents. Then, it constructs the service relationship network according to tags and shared annotation relationships of Web services. Next, a bilinear aggre-gator is used to model the pairwise interactions between neighbor service nodes. Integrated with the traditional weighted sum ag-gregator, a bilinear graph neural network (BGNN) with stronger node representation ability is constructed. It exploits BGNN to calculate the representation of service nodes in the network and divides services into different functionality clusters. Finally, the high-quality representation results are combined with mul-ti-dimensional QoS attributes. Aiming at the Web services in the service cluster, it utilizes xDeepFM to model and mine the com-plex interactions between Web services’ features, and predict and rank the invocation scores of Web services. The experimental results on the real dataset of ProgrammableWeb show that com-pared with the other ten methods, the proposed approach has better performance in terms of Accuracy, Recall, F1, Logloss, and AUC, and has better performance in classification and recom-mendation.

Semantic Annotation Based Effective and Quality Oriented Web Service Discovery

The main objective of this work is to design an effective web service discovery engine that retrieves the best matching results based on the measure of syntactic cum semantic similarity between the user query and the web service to be fetched. The idea is to draw bridges across the broad spectrum of lexical entities based on their relativeness. The essence of this work could be extended to support a wide range of applications from ‘being inculcated in search engines to fetch user-relevant data’ to ‘being used for training robots and AI based devices to respond/adhere appropriately to the different phrases of human commands’.With the accelerated revolution of internet, enterprises and organizations highly rely on Service oriented computing. Web services support inter-operation of distributed applications. Humongous amount of web services present on the internet the user is searching for. The paper focuses ondesigninga practical means of fast and relevant retrieval of web services.The phrase used for searching any given web service differs from one person to another. This work deals with the syntactic cum semantic mechanism proposed for retrieving web services based on the measure of similarity between the name of the web service and the search query entered by the user to discover that service. A list of the Web Service Description Language (WSDL) files is taken as the dataset. Protégé is the tool used for semantic annotation of WSDL files for converting them to Semantically Annotated Web Service Description Language (SAWSDL) files. ‘Wordnet’ is used as the lexical dictionary and Java language has been used to build the rest of the package for implementing the search discovery mechanism.Netbeans is used as IDE.Wamp server with PHPMyAdmin was used for managing the database of SAWSDL files. The degree of similarity is measured by evaluating performance of the engine using metrics such as ‘Precision’, ‘Recall’, ‘Accuracy’ and ‘F-measure’.While the syntactic approach is easier to implement, it suffers from keyword polysemy issues. The proposed search discovery mechanism is based on semantically annotating WSDL files and retrieving the files based on a novel syntactic cum semantic discovery algorithm which usesLeocockChordorow function for computing the similarity. The effectiveness of the proposed algorithm is tested experimentally by building a desktop application using Java.The WSDL files from various domains were semantically annotated by tagging related concepts using ontology. Themachine learning algorithm that best classifies the web services on the basis of their performance metrics is identified. The related services are retrieved by the application of the proposed LCH based Syntactic cum Semantic discovery algorithm (LCH based SSDA).A ranking system is proposed to rank the results by evaluating various QoS attributes. The results of the experiment showed that the proposed system yielded high precision and recall value.The solution has been found to be effective in minimizing the execution time and in improving the degree of relevancy. With an impending need for constructing a semantic context based secure structure,the proposed solution will- help in refining the search results and in minimizing users’ cognitive load during search formulation and execution.

A Fuzzy-Multi Attribute Decision Making approach for efficient service selection in cloud environments

In general, objective and subjective QoS assessment data forms the major data source for service evaluations. Especially, in the most dynamic service-oriented computing ecosystem like cloud environments, such assessment data is subjected to high uncertainty due to the dynamics in the number of cloud services and related QoS values. In such cases, the accuracy and reliability of the most efficient cloud service selection model remain questionable. Further, the complex interdependencies among the multiple QoS parameters and the trustworthiness of the cloud services complicate the process of handling uncertainty during cloud service selection. This work presents Fuzzy-Multi Attribute Decision Making (FMADM), a hybrid trust prediction model to provide accurate trust-based cloud service evaluations while efficiently handling the uncertainty in the cloud service assessment data. FMADM employs (i) Picture Fuzzy Sets (PFSs): to capture inconsistency, uncertainty, and ambiguity in the QoS data, (ii) Naïve Bayes: to recompute the weights of the QoS attributes, (iii) Measurement of Alternatives and Ranking according to COmpromise Solution (MARCOS): to model the non-linear relationship between the QoS values of cloud services and their corresponding trust result, and (iv) Random Forest Classifier (RFC): to predict the trust value of cloud services. The performance of FMADM was evaluated using Quality Web Service (QWS) v1.0 dataset under different scenarios using various quality metrics.

PPDRL: A Pretraining-and-Policy-Based Deep Reinforcement Learning Approach for QoS-Aware Service Composition

Service composition is a mainstream paradigm for rapidly constructing large-scale distributed applications. QoS-aware service composition, i.e., selection of the optimal execution plan that maximizes the composition’s end-to-end QoS properties, is an active area of research and development endeavors in service composition. In this paper, we propose PPDRL, a pretraining-and-policy-based deep reinforcement learning approach, to solve the QoS-aware service composition problem. Its significant feature is to incorporate a maximum likelihood estimate and a policy scoring mechanism into a deep reinforcement learning framework. As a result, our approach can balance the exploitation and exploration efforts adaptively and can search for the solution space in a robust and efficient manner. We have executed our approach to solve 6 randomly generated QoS-aware service composition problems with different sizes and structures based on QWS data set including 2,507 real Web services classified into 233 categories. The results indicate that our approach can find near-optimal solutions within moderate numbers of iteration and has performance superiority in comparison with five state-of-the-art algorithms.

Tourism Management Strategies under the Intelligent Tourism IoT Service Platform

This paper conducts an in-depth study and analysis of the tourism management strategy of the intelligent tourism IoT service platform. The intelligent tourism system is designed from the overall system architecture to the design of each functional module of the system, and the database design is completed with the help of the database E-R diagram. At the same time, this paper uses a weighted regression mathematical method to analyze the passenger flow at different stages of the scenic spot by establishing a scenic spot passenger flow prediction model and giving the results of passenger flow prediction for the scenic spot in the next seven days. In addition, this paper conducts a research based on the ant colony algorithm to establish an itinerary planning and recommendation model to recommend an itinerary that meets the needs of scenic tourists and provides reliable tour guidance based on their expected touring time and touring expenses in the scenic area. The method simplifies the solution of service combination problems in the IoT environment by transforming them into service instance preferences within abstract services. The effectiveness of the method is demonstrated through theoretical derivation. On this basis, the residual energy and energy consumption of smart terminal devices abstracted as cumulative aggregation QoS attributes, and an energy consumption balancing service combination method based on cumulative aggregation QoS is proposed. The countermeasures and suggestions are proposed to increase tourism publicity and give full play to the advantages of tourism resources; strengthen tourism environment monitoring and improve the legal and regulatory system; raise tourism construction funds and strengthen infrastructure construction; improve residents' participation and promote healthy tourism development, as well as to make long-term planning and innovate the management mode; establish a coordinating body and improve the supervision system; establish government macro-control and formulate regulations; and strengthen the management function and the management optimization strategy of cultivating grassroots organizations. Through experimental comparison with other methods, the results show that the method ensures the lifecycle and stability of the entire service portfolio workflow while providing low-energy portfolio services.

Process for Identifying QoS Requirements in the Multi-Domain Operations Environment

A network QoS model for the joint integrated C4I structure<sup>[1]</sup> was proposed for the integration of network infrastructure and network operations(NetOps) for NCOE. Detailed QoS requirements process of the joint integrated C4I systems are needs in the Multi-Domain Operation Environment(MDOE). A process is proposed for identifying QoS requirements and establishing in the MDOE using JMT(Joint Mission Thread) reference architecture and solution architecture. Mission analysis identify JCOAs(Joint Critical Operational Activities) and related activities based on JMT & System architecture's OVs, and Information analysis identify QoS attributes using System architecture's SVs. Identifying QoS attributes will be registered at PPS Registry by pre-regulated process, and will be set-up by NetOps. MDOE QoS requirement Process will support efficiently MUM-T and smart defense platform users under the future uncertain battlefield circumstances.

An extended artificial bee colony with local search for solving the Skyline-based web services composition under interval QoS properties

In this paper, we introduce an extended version of artificial bee colony with a local search method (EABC) for solving the QoS uncertainty-aware web service composition (IQSC) problem, where the ambiguity of the QoS properties are represented using the interval-number model. At first, we formulate the addressed problem as an interval constrained single-objective optimization model. Then, we use the skyline operator to prune the redundant and dominated web services from their sets of functionally equivalent ones. Whereas, EABC is employed to solve the IQSC problem in a reduced search space more effectively and more efficiently. For the purpose of validation of the performance and the efficiency of the proposed approach, we present the experimental comparisons to an existing skyline-based PSO, an efficient discrete gbest-guided artificial bee colony and a recently provided Harris Hawks optimization with an elite evolutionary strategy algorithms on an interval extended version of the public QWS dataset.

MAACO: A Dynamic Service Placement Model for Smart Cities

Smart cities generate huge volumes of data to be processed by applications with different criticality and requirements. For example, a healthcare application needs lower latency when requested from an ambulance travelling to a hospital during an emergency compared to applications in less-critical domains. Cities can use Multi-access Edge Computing to reduce latency by placing applications’ services closer to users. A service placement process selects the set of servers to run the services for deployment. Smart cities challenge this selection as a large number of servers and services generate a large number of potential solutions with different QoS properties. Additionally, placement approaches must consider applications’ criticality and users’ mobility to offer an appropriate overall latency. Current approaches have considered servers’ utilisation and users’ location to place services. However, they do not consider applications’ criticality and mobile users’ paths. This paper presents MAACO, a Mobility-Aware, priority-driven, ACO-based service placement model that prioritises applications according to their criticality and minimises critical applications’ latency, while considering predicted paths for mobile users. Evaluation results show that MAACO achieves lower latency and waiting time compared against baselines at the cost of reduced load balance between the network servers.

Eagle strategy using uniform mutation and modified whale optimization algorithm for QoS-aware cloud service composition

Cloud manufacturing (CMfg) has received increasingly attention from both academia and industry. Cloud service composition is a critical technique in CMfg that connects different available manufacturing cloud services (MCSs) to generate a composite manufacturing cloud service (CMCS) to satisfy users’ requirements. Many available MCSs with the same or similar functionality but different QoS attributes are deployed in the CMfg platform. So it is challenging to obtain an optimal CMCS to satisfy the users’ complex requirements. Considerable numbers of approaches have been proposed to solve this problem. However, most of them often fall in a local optimum instead of the global one. In this paper, a novel eagle strategy using uniform Mutation and modified Whale Optimization Algorithm (MWOA) is proposed to maintain a balance between the global and local search abilities. In this approach, the uniform mutation is applied to perform the global search to preserve the diversification of the population, and a modified whale optimization algorithm is designed to perform the local search. The performance of the new approach is verified on various benchmark functions and different scales of QoS-aware cloud service composition problems. The experimental results demonstrate that the proposed MWOA has superior performance over the other methods.

Web Application Resource Requirements Estimation Based on the Workload Latent Features

Most cloud computing platforms offer reactive resource auto-scaling mechanisms for dealing with variable traffic patterns to deliver the desired QoS properties while keeping low provisioning costs. However, a range of scenarios have not been fully addressed by the current auto-scaling solutions, particularly dealing with a rapid increase in workload and the risk of thrashing due to frequent workload variations. A reactive system is vulnerable in such conditions. Realizing the full potential of auto-scaling still remains challenging particularly due to the need of accurately estimating the application resource requirements for time-varying workload patterns. In this work, we propose and evaluate a novel method using only application access logs to estimate more accurately the hardware resource demands and application response time. In particular, we propose novel workload latent features which we compute by applying unsupervised learning on the access logs. We use these latent features to estimate the application hardware resource requirements and response time for various workload patterns. We evaluate the proposed method using multiple benchmark web applications and compare it with current state-of-the-art. Extensive experimental evaluations show an excellent performance of our proposed workload latent features in estimating response time, CPU, memory, and bandwidth utilization.

Web service quality of service prediction via regional reputation‐based matrix factorization

AbstractQuality of Service (QoS) of Web services plays an essential role in selecting Web services by consumers. The dynamic QoS attributes of Web services have different values for different users. Therefore, the value of many Web services' QoS features for many users are undetermined, and these values should be predicted. The collaborative filtering (CF) method is one of the most successful approaches to predict these values. CF‐based methods use the QoS values contributed by the other users for prediction and, consequently, the values contributed by unreliable users can decrease the accuracy of prediction. To utilize the reputation of users can be regarded as one of the conventional approaches to overcome this problem. In this paper, we have defined a concept called regional reputation that represents the reputation of a user for users in each geographical region. Regional reputation has been achieved with the combination of the location information of the users and their reputation. Subsequently, by combining this concept with the matrix factorization, we have proposed a prediction method called regional reputation‐based matrix factorization. This approach has been able to improve the accuracy of prediction and be more persistent to the data contributed by unreliable users.

BSS: a brokering model for service selection using integrated weighting approach in cloud environment

Cloud providers shares their resources and services through collaboration in order to increase resource utilization, profit and quality of services. The offered services with different access patterns, similar characteristics, varied performance levels and cost models create a heterogeneous service environment. It becomes a challenging task for users to decide a suitable service as per their application requirements. Cloud broker, an inter-mediator is required in service management to help both cloud providers and users. Cloud broker has to store all the information related to services and feedback of users on those services in order to provide the best services to end-users. Brokering model for service selection (BSS) has been proposed which employs integrated weighting approach in cloud service selection. Subjective and objective weights of QoS attributes are combined to compute integrated total weight. Subjective weight is obtained from users’ feedback on QoS attributes of a cloud service while objective weight is computed from benchmark tested data of cloud services. Users’ feedback and preferences given to QoS parameters are employed in subjective weight computation. Objective weight is computed using Shannon’s Entropy method. Total weight is obtained by combining subjective and objective weights. BSS method is employed to rank cloud services. Simulation with a case study on real dataset has been done to validate the effectiveness of BSS. The obtained results demonstrate the consistency of model for handling rank reversal problem and provides better execution time than other state-of-the art solutions.

Drone-as-a-Service Composition Under Uncertainty

We propose an uncertainty-aware service approach to provide drone-based delivery services called Drone-as-a-Service (DaaS) effectively. Specifically, we propose a service model of DaaS based on the dynamic spatiotemporal features of drones and their in-flight contexts. The proposed DaaS service approach consists of three components: scheduling, route-planning, and composition. First, we develop a DaaS scheduling model to generate DaaS itineraries through a Skyway network. Second, we propose an <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">uncertainty-aware DaaS route-planning algorithm</i> that selects the optimal Skyways under weather uncertainties. Third, we develop two DaaS composition techniques to select an optimal DaaS composition at each station of the planned route. A <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">spatiotemporal DaaS composer</i> first selects the optimal DaaSs based on their spatiotemporal availability and drone capabilities. A <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">predictive DaaS composer</i> then utilises the outcome of the first composer to enable fast and accurate DaaS composition using several Machine Learning classification methods. We train the classifiers using a new set of spatiotemporal features which are in addition to other DaaS QoS properties. Our experiments results show the effectiveness and efficiency of the proposed approach.

QoS-driven metaheuristic service composition schemes: a comprehensive overview

Services Oriented Architecture provides Web Services (WSs) as reusable software components that can be applied to create more complicate composite services for users according to the specified QoS limitations. However, considering many WSs that may be appropriate for each task of a user-submitted workflow, finding the optimal WSs for a composite WS to maximize the overall QoS is an NP-hard problem. As a result, numerous composition schemes have been suggested in the literature to untangle this problem by using various metaheuristic algorithms. This paper presents a comprehensive survey and taxonomy of such QoS-oriented metaheuristic WS composition schemes provided in the literature. It investigates how metaheuristic algorithms are adapted for the WS composition problem and highlight their main features, advantages, and limitations. Also, in each category of the studied composition schemes, a comparison of their applied QoS factors, evaluated metrics, exploited simulators, and properties of the applied metaheuristic algorithms are explained. Finally, the concluding remarks and future research directions are summarized to help researchers in working in this area.

QoS Impacts of Slice Traffic Limitation

Slicing is an essential building block of 5G networks and beyond. Different slices mean sets of traffic demands with different requirements, which need to be served over separated or shared network resources. Various service chaining methods applied to support slicing lead to different network load patterns, impacting the QoS experienced by the traffic. In this paper, we analyze QoS properties applying a theoretical model. We also suggest appropriate parameter setting policies in slice-aware service function chaining (SFC) algorithms to increase QoS. We evaluate several metrics in different analysis scenarios to show the advantages of the slice-aware approach.

A Trade-Off Negotiation Strategy for Pareto-Optimal Service Composition with Additive QoS-constraints

QoS-based service composition enables the development of complex business applications. They are composed of distributed services characterized by QoS attributes representing non-functional characteristics, such as cost, execution time, and reliability. With the proliferation of services on the Internet, more candidates for each component service may be available with different QoS values. Hence, the ones satisfying QoS global constraints required for the application have to be selected. Automated negotiation is adopted to select component services in a dynamic market of services where provided QoS values may vary according to different market strategies. Nevertheless, when dealing with multiple QoS attributes, multiple service providers, and not shared information, it is difficult to guarantee the formal properties of the negotiation outcomes. In the present work, we propose a trade-off negotiation strategy that allows reaching a Pareto-optimal agreement, if it exists. The agreement consists of the QoS values of component services that are the ones selected to provide the complete application. The strategy exploits both the competition, that is due to multiple services providing the same functionality with different QoS values, and the cooperation among the providers of the different component services, that are necessary to meet the required end-to-end QoS constraints.

Service selection using fuzzy multi-criteria decision making: a comprehensive review

The growing number of web services (WSs) and cloud services, which can meet the users’ functional and non-functional requirements, have inspired researchers to provide more effective approaches for ranking the available services, regarding different QoS factors and selecting the best of them. In this context, several service selection frameworks using the fuzzy multicriteria decision making (MCDM) techniques are introduced in the literature. This paper focuses on such schemes, and firstly provides the required background knowledge about service selection and MCDM methods. Then, it puts forward a taxonomy of the service selection schemes, regarding their utilized fuzzy MCDM methods, and describes how the fuzzy MCDM methods are adapted to handle the fuzziness of the users’ preferences and QoS properties. Furthermore, the main features of these schemes are compared, and their contributions and possible shortcomings are discussed. Finally, the concluding remarks are provided, and directions for future studies are illuminated.