۱- Introduction

۲- Materials and methods

۳- Proposed hypergraph based robust heteroscedastic PNN for trust prediction

۴- Experimental results and analysis

۵- Conclusions

References

Abstract

Trustworthiness is a comprehensive quality metric which is used to assess the quality of the services in service-oriented environments. However, trust prediction of cloud services based on the multi-faceted Quality of Service (QoS) attributes is a challenging task due to the complicated and non-linear relationships between the QoS values and the corresponding trust result. Recent research works reveal the significance of Artificial Neural Network (ANN) and its variants in providing a reasonable degree of success in trust prediction problems. However, the challenges with respect to weight assignment, training time and kernel functions make ANN and its variants under continuous advancements. Hence, this work presents a novel multi-level Hypergraph Coarsening based Robust Heteroscedastic Probabilistic Neural Network (HC-RHRPNN) to predict trustworthiness of cloud services to build high-quality service applications. HC-RHRPNN employs hypergraph coarsening to identify the informative samples, which were then used to train HRPNN to improve its prediction accuracy and minimize the runtime. The performance of HC-RHRPNN was evaluated using Quality of Web Service (QWS) dataset, a public QoS dataset in terms of classifier accuracy, precision, recall, and F-Score.

Introduction

Cloud computing, an efficient, and economic business paradigm has attracted a wide range of organizations as it enables the users to access on-demand resources as a service (‘XaaS’-Something as a Service) over the internet in a ‘Pay-As-You-Use’ fashion (Sosinsky, 2010). The increasing popularity of cloud computing has resulted in the proliferation of many Cloud Service Providers (CSPs) and functionally equivalent cloud services. On the other end, the Cloud Users (CU) lack appropriate information and benchmarks to evaluate these services based on their preferences and CSPs provisions (Ali Sunyaev, 2013). In addition, the trade-off between the functional and non-functional Quality of Service (QoS) requirements hardens the identification of appropriate and trustworthy CSPs who can satisfy the users’ unique QoS requirements. Thereby, the presence of a wide range of cloud-based entities (service providers, users, applications and unique demands) has provoked the research communities towards the development of cloud service selection models based on several approaches like multi-criteria decision making (multiple attributes and interrelations among them), optimization, logic, description, and trust (Ma, Zhu, Hu, Li, & Tang, 2017; Qu, 2016; Sun, Dong, Hussain, Hussain, & Chang, 2014).