Effective BigData-Space Service Selection over Trust and Heterogeneous QoS Preferences

Industrial Internet of things (IIoT) deploys a large number of smart devices to obtain industrial data, which will be transmitted to cloud for analysis to improve industrial productivity. The management of large‐scale devices is complicated, and it's also a challenge to choose a high‐quality cloud service for data analysis as the number of service with similar functions increases. To address these issues, we propose a reliable fog‐cloud service solution with blockchain‐based fog‐cloud architecture. In fog layer, we build a management blockchain between fog servers and design a management method for industrial devices; In cloud layer, we construct a service blockchain between cloud service providers to form an open “service market”. Quality of service and reputation based matching algorithm and reputation‐based consensus algorithm are designed. The simulation results show correctness and efficiency of algorithms, and validate effectiveness of our proposed solution.

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“…The importance of recommender, subjectivity problem and collusion [ 23 ] are key issues to consider when calculating recommended reputation.…”

Section: Key Technologies For Distributed Cloud Service In Cloudmentioning

confidence: 99%

Blockchain‐Based Reliable Fog‐Cloud Service Solution for IIoT

Dai

Shao

et al. 2021

Chinese J of Electronics

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“…A multi-objective optimization approach was developed in [32] to make trade-off decisions between service's trust value and user's QoS preferences in order to rank candidate cloud services depending on their match degrees with users' requirements. The authors focused on heterogeneous preference and trust-based service selection.…”

Section: Qos Prediction and Service Selectionmentioning

confidence: 99%

A Recommendation Model for Energy-Efficient Data Mining Services Based on Data Properties

Al-Zanbouri¹

2021

Preprint

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Currently, there is a big increase in the usage of data analytics applications and services because of the growth in the data produced from different sources. The QoS properties such as response time and latency of these services are important factors to decide which services to select. As a result of IT expansion, energy consumption has become a big issue. Therefore, establishing a QoS-based web service recommender system that considers energy consumption as one of the essential QoS properties represents a significant step towards selecting the energy efficient web services. This dissertation presents an experimental study on energy consumption levels and latency behavior collected from a set of data mining web services running on different datasets. Our study shows that there is a strong relation between the dataset properties and the QoS properties. Based on the findings from this study, a recommender system is built which considers three dimensions (user, service, dataset). The energy consumption values of candidate services invoked by specific users can be predicted for a given dataset. Afterwards, these services can be ranked according to their predicted energy values and presented to users. We propose three approaches to build our recommender system and we treat it as a context-aware recommendation problem. The dataset is considered as contextual information and we use a context-aware matrix factorization model to predict energy values. In the first approach, we adopt the pre-filtering model where the contextual information serves as a query for filtering relevant rating data. In the second approach, we propose a new method for the pre-filtering implementation. Finally, in the last approach, we adopt the contextual modeling method and we explore different ways of representing dataset information as contextual factors to investigate their impacts on the recommendation accuracy. We compare the proposed approaches with the baseline approaches and the results show the effectiveness of the proposed ones. Also, we compare the performance of the three approaches to discover the best-fit approach when being measured using different metrics. Both prediction and recommendation accuracy of the proposed approaches are significantly better than the baseline models.

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“…Hang and Singh [44] proposed two distributed trust-aware service selection approaches for Service-Oriented Computing (SOC) environments. Also, some researchers proposed service selection algorithms based on trust and QoS preferences, such as [45].…”

Section: B Trust Management Strategiesmentioning

confidence: 99%

A Trust-Based Agent Learning Model for Service Composition in Mobile Cloud Computing Environments

Cao

et al. 2019

IEEE Access

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Mobile cloud computing has the features of resource constraints, openness, and uncertainty which leads to the high uncertainty on its quality of service (QoS) provision and serious security risks. Therefore, when faced with complex service requirements, an efficient and reliable service composition approach is extremely important. In addition, preference learning is also a key factor to improve user experiences. In order to address them, this paper introduces a three-layered trust-enabled service composition model for the mobile cloud computing systems. Based on the fuzzy comprehensive evaluation method, we design a novel and integrated trust management model. Service brokers are equipped with a learning module enabling them to better analyze customers' service preferences, especially in cases when the details of a service request are not totally disclosed. Because traditional methods cannot totally reflect the autonomous collaboration between the mobile cloud entities, a prototype system based on the multi-agent platform JADE is implemented to evaluate the efficiency of the proposed strategies. The experimental results show that our approach improves the transaction success rate and user satisfaction. INDEX TERMS Mobile cloud computing, service composition, trust management, user preference learning, multi-agent technology.

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Effective BigData-Space Service Selection over Trust and Heterogeneous QoS Preferences

Cited by 33 publications

References 51 publications

Blockchain‐Based Reliable Fog‐Cloud Service Solution for IIoT

Blockchain‐Based Reliable Fog‐Cloud Service Solution for IIoT

A Recommendation Model for Energy-Efficient Data Mining Services Based on Data Properties

A Trust-Based Agent Learning Model for Service Composition in Mobile Cloud Computing Environments

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