Wei Tang

Bio: Wei Tang is an academic researcher from Xidian University. The author has contributed to research in topics: Cloud testing & Mobile computing. The author has an hindex of 1, co-authored 1 publications receiving 4 citations.

CloudRec: A Mobile Cloud Service Recommender System Based on Adaptive QoS Management

Abstract: The rapid development and growth of mobile Internet and cloud computing have made the usage of mobile cloud services widespread. However, many similar functionalized cloud services available in global markets cause difficulties for mobile users to select proper services to consume. Obviously the quality of a mobile cloud service is affected by its usage context. Users expect qualified services that can cope with their usage situations and are optimized for satisfying their expectations. In this paper, we introduce a mobile cloud service recommender system named CloudRec based on adaptive Quality of Service (QoS) management. The QoS is assessed based on service performance monitoring at mobile devices and user feedback on service quality, which are important factors influencing user trust. The system can adaptively recommend the most appropriate mobile cloud service to a user according to the user's usage context. System evaluation based on a prototype implementation shows the effectiveness of CloudRec.

Compliance-based Multi-dimensional Trust Evaluation System for determining trustworthiness of Cloud Service Providers

Abstract: This paper addresses the problem of determining trustworthiness of Cloud Service Providers (CSPs) in a cloud environment. For the current work, trustworthiness is defined as the degree of compliance of a CSP to the promised quantitative QoS parameters as defined in the Service Level Agreement (SLA). Due to large number of CSPs offering similar kinds of services in the cloud environment, it has become a challenging task for Cloud Clients (CCs) to identify and differentiate between the trustworthy and untrustworthy CSPs. At present, there is no trust evaluation system that allows CCs to evaluate the trustworthiness of CSPs on the basis of their adherence to the SLA i.e. the compliance provided by the CSPs to CCs as per the SLAs. This paper proposes a Compliance-based Multi-dimensional Trust Evaluation System (CMTES) that enables CCs to determine the trustworthiness of a CSP from different perspectives, as trust is a subjective concept. Such a system is of great help to CCs who want to choose a CSP from a pool of CSPs, satisfying their desired QoS requirements. The framework enables us to evaluate the trustworthiness of a CSP from the CC’s perspective, Cloud Auditor’s perspective, Cloud Broker’s perspective and Peers’ perspective. Experimental results show that the CMTES is effective and stable in differentiating trustworthy and untrustworthy CSPs. The validation of the CMTES has been done with the help of synthetic data due to lack of standardized dataset and its applicability has been demonstrated with the help of a case study involving the use of real cloud data.

TruSD: Trust framework for service discovery among IoT devices

Abstract: IoT provides an environment which enables access to a plethora of different services. In order to reach these services, devices need to decide if the providers are trustable or not. The decision to trust a node with whom one has not communicated earlier becomes more critical when the system has unrecoverable damages with inaccurate services. In this paper, we propose a framework which enables trusted communication among devices during service discovery. It focuses not only on the communication between the known devices but also the stranger communications which have not contacted earlier. Our framework works in a decentralized manner on top of a structured P2P network based on a Distributed Hash Table (DHT). In our system, for each device there are several nodes which are responsible for holding a trust value for this device. These responsible nodes are called Reference Holders for this device. By utilizing DHT, we propose a novel way of choosing Reference Holders that prevents the malicious nodes to control these nodes. Our protocols provide trust aggregation, service provision and feedback aggregation. In our threat model, attacker provides on-off, bad mouthing, ballot stuffing and selective attacks. We present closed form of probabilistic analysis and provide simulations that manage to give network-wide probabilistic security guarantees. Our results suggest that until 60% of the devices are captured, the results are perfect. Also, just three reference holders are enough to get accurate services through the network. Additionally, we analyze the framework in terms of memory, computational cost and communication overhead since we propose the framework for IoT devices. Due to these analysis, our framework is affordable for IoT devices.

Fuzzy Multi-Criteria Based Trust Management in Heterogeneous Federated Future Internet Testbeds

Abstract: A federation of heterogeneous testbeds, which provides a wide range of services, attracts many experimenters from academia and industry to evaluate novel future Internet architectures and network protocols. The candidate experimenter reserves the appropriate testbeds’ resources based on various diverse criteria. Since several testbeds offer similar resources, a trust mechanism between the users and the providers will facilitate the proper selection of testbeds. This paper proposes a fuzzy reputation-based trust framework that is based on a modification of the fuzzy VIKOR multi-criteria decision making method and combines the user’s opinion from previously-conducted experiments with retrieved monitoring data from the utilized testbeds, in order to quantify the reputation of each testbed and the credibility of the experimenter. The proposed framework can process various types of numeric and linguistic data in an on-line fashion and can be easily extended for new types of testbeds and services. Data from active federated testbeds are used to evaluate the performance of the fuzzy reputation-based trust framework under dynamic conditions. Furthermore, a comparison of the proposed framework with another existing state of the art trust framework for federated testbeds is presented, and its superiority is demonstrated.

An elastic recommender process for cloud service recommendation scalability

Abstract: Cloud computing services are ubiquitous in society and cloud recommender systems play a crucial role in intelligently selecting services for cloud users. Currently, recommendations are static with low scalability. Only one recommendation list is generated at a time and the recommender strategy in the recommendation cycle is not adjustable. This paper presents a new elastic recommender process (ERP) for cloud users. A Markov model is used to characterize the dynamic relationship between different user states. The ERP generates an elastic recommendation that can be used to dynamically adjust the recommender strategy to meet the user's needs based on their browsing records in the current service cycle without the recommender system's involvement. Experimental results show that the ERP improves the effectiveness of the recommender thus increasing the accuracy and diversity of its recommendations.

Cloud Services User’s Recommendation System Using Random Iterative Fuzzy-Based Trust Computation and Support Vector Regression

Abstract: Cloud computing is now a fundamental type of computing due to technological innovation and it is believed to be a benefit for mid-scale enterprises. The use of cloud computing is increasing daily, which improves service quality but also gives rise to security concerns. Finding trustworthy service can be very challenging, take a great deal of time, or produce subpar services. Due to these difficulties, the client needs a service that is dependable, suitable, time-saving, and trustworthy. As a result, from the end user’s perspective, adopting a cloud service’s trustworthiness becomes crucial. Trust is a measure of how well users’ expectations about a service’s capabilities are realized. In this research, a recommendation system for cloud service customers based on random iterative fuzzy computation (RIFTC) is proposed. RIFTC focuses on the assessment of trust using Quality of Service (QoS) characteristics. RIFTC calculates trust using the machine learning approach Support Vector Regression (SVR). RIFTC can helpfully recommend a cloud service to the end user and anticipate the trust values of cloud services.. Precision (97%), latency (51%), throughput (25.99 mbps), mean absolute error (54%), and re-call (97%) rates are used to assess how well this recommendation system performs. RIFTC’s average F-measure rate is calculated by adjusting the number of users from 200 to 300, and it is 93.46% more accurate on average with less time spent than the current methodologies.