Introduction to Qos Prediction

What is/are Qos Prediction?

Qos Prediction - This paper proposes an approach for QoS prediction based on the trustworthiness of IoT devices that allows choosing the reliable and trustworthy fog node. ^[1] Especially with high user mobility, QoS prediction is required in an End-to-End (E2E) fashion to guarantee uninterrupted connectivity and provisioning of real-time applications. ^[2] In this paper, we introduce an entity, that takes the QoS prediction about multiple MNOs and makes decision about how to manage the connectivity in a vehicle, e. ^[3] For this purpose, the QoS prediction of services constitutes a decisive tool to allow end-users to optimally choose high-quality cloud services aligned with their needs. ^[4] The ever-increasing diversity and dynamic of cloud environment pose many challenges on QoS prediction in service recommendation. ^[5] As the number of web services is enormous and the QoS data are highly sparse, improving the accuracy of QoS prediction has become a challenging issue recently. ^[6] In this context, QoS prediction for Web services is a highly important and challenging issue that facilitates the building of value-added processes such as compositions and workflows of services. ^[7] Finally, a new mechanism is shown to map the performance in physical layer into the QoS quantification in higher layer so that the QoS prediction of IEEE 80211bd driven VANETs is enabled. ^[8] However, QoS prediction has become one of the key factors in services recommendation and selection to build high-quality software systems, so it becomes an urgent and crucial research area in both academia and industry. ^[9] To address this problem, we propose a hybrid approach HAP for QoS prediction. ^[10] In service-oriented architectures, collaborative filtering is a key technique for service recommendation based on QoS prediction. ^[11] Recently, latent factor (LF) model has been widely and successfully applied to QoS prediction because it is accurate and scalable under many circumstances. ^[12] A Web service recommendation method based on the QoS prediction and hierarchical tensor decomposition is proposed in this paper. ^[13] As a result, QoS prediction can significantly support service providers in a better understanding of their resources future states. ^[14] We employ basic machine learning approaches to perform QoE and QoS predictions based on this data. ^[15] The system attempts to relegate the impact of unreliable feedbacks provided by dishonest users which affects the accuracy of QoS prediction. ^[16] Firstly, we propose a QoS Prediction (QP) method using explicit factor analysis and linear regression. ^[17] , storage and computation) and the accuracy of QoS predictions, as well as its implications on security. ^[18] However, how to further improve the accuracy of QoS prediction is still a problem. ^[19] QoS prediction is a task to predict the unknown QoS value of an active user to a web service that he/she has not accessed previously for supporting appropriate web service recommendation. ^[20]

Service Qos Prediction

The experimental results show that our model outperforms the baseline solutions for service QoS prediction under a benchmark dataset. ^[1] To obtain the QoS values of web services, a number of web service QoS prediction methods have been proposed. ^[2] To obtain the QoS values of web services, a number of web service QoS prediction methods have been proposed. ^[3] , from one edge server to another) often makes service QoS prediction values deviate from actual values in traditional mobile networks. ^[4]

Existing Qos Prediction

Nevertheless, the existing QoS prediction methods cannot be directly utilized in edge computing networks (ECNs) due to the user mobility and distribution nature in these networks. ^[1] Among the existing QoS prediction methods, the collaborative filtering (CF) method can only learn low-dimensional linear characteristics, and its effect is limited by sparse data. ^[2] Many existing QoS prediction approaches fail to consider the mobility of users and cannot reflect the correlation between time varying QoS values and some external factors such as user s position. ^[3]

Missing Qos Prediction

Quality-of-service (QoS) of Web services vary over time, making it a significant issue to discover temporal patterns from them for addressing various subsequent analyzing tasks like missing QoS prediction. ^[1] Quality-of-service (QoS) of Web services vary over time, making it a significant issue to discover temporal patterns from them for addressing various subsequent analyzing tasks like missing QoS prediction. ^[2]

Collaborative Qos Prediction

In recent years, various collaborative QoS prediction methods have been put forward to coping with the demand for efficient quality-of-service (QoS) evaluation, by drawing lessons from the recommender systems. ^[1] With the exponential growth of Web services, various collaborative QoS prediction methods have been suggested to make an efficient evaluation of quality-of-services (QoS) and assist users selecting appropriate services. ^[2]

qos prediction method

Nevertheless, the existing QoS prediction methods cannot be directly utilized in edge computing networks (ECNs) due to the user mobility and distribution nature in these networks. ^[1] In recent years, various collaborative QoS prediction methods have been put forward to coping with the demand for efficient quality-of-service (QoS) evaluation, by drawing lessons from the recommender systems. ^[2] Although various QoS prediction methods have been proposed, few of them have taken outliers into consideration, which may dramatically degrade the prediction performance. ^[3] To obtain the QoS values of web services, a number of web service QoS prediction methods have been proposed. ^[4] Among the existing QoS prediction methods, the collaborative filtering (CF) method can only learn low-dimensional linear characteristics, and its effect is limited by sparse data. ^[5] To obtain the QoS values of web services, a number of web service QoS prediction methods have been proposed. ^[6] Specifically, we propose a context-aware multi-QoS prediction method for services in MEC. ^[7] Most contemporary QoS prediction methods exploit the QoS characteristics for one specific dimension, e. ^[8] With the exponential growth of Web services, various collaborative QoS prediction methods have been suggested to make an efficient evaluation of quality-of-services (QoS) and assist users selecting appropriate services. ^[9] 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. ^[10] , we propose a personalized QoS prediction method for web services that we call blockchain-based matrix factorization (BMF). ^[11]

qos prediction approach

By taking these two aspects into consideration, we propose a novel QoS prediction approach for Web service recommendation in this paper. ^[1] Many existing QoS prediction approaches fail to consider the mobility of users and cannot reflect the correlation between time varying QoS values and some external factors such as user s position. ^[2] Finally, we present a Kalman-based on-line QoS prediction approach for making decisions regarding the adaptation actions to be taken. ^[3] Specifically, we propose a location-aware QoS prediction approach, called LANFM, by exploiting neural network techniques and factorization machine to improve user-perceived experience. ^[4]

qos prediction result

When historical matrix is very sparse and matrix data is unreliable due to untrustworthy service users, the QoS prediction results is inaccurate. ^[1] In addition to the data sparsity problem, however, most existing approaches also ignore the task similarity among different users, while dissimilar tasks requested by similar users may lead to quite different QoS prediction results, and thus cannot achieve higher prediction accuracy and recommendation quality. ^[2]

qos prediction model

The proposed approach first develops an unknown-QoS prediction model by utilizing similarity features from temporal QoS values. ^[1] By considering the context of services, this paper proposes a QoS prediction model using tensor decomposition based on service collaboration called Service-oriented Tensor (SOT). ^[2]