A Reputation-Based Approach to Improve QoS in Cloud Service Composition

Comi, Antonello; Fotia, Lidia; Messina, Fabrizio; Pappalardo, Giuseppe; Rosaci, Domenico; Sarné, Giuseppe M. L.

doi:10.1109/wetice.2015.28

Cited by 15 publications

(6 citation statements)

References 28 publications

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“…And the prediction accuracy of this multivariate load sequence forecasting model is obviously different from that of the univariate load sequence forecasting model, and it has higher accuracy. Some research mainly studies the data and related processing and statistical methods of the resource load sequence in the cloud computing platform during use [12]. On the one hand, this new algorithm improves to a certain extent the identification of dirty data in the use of cloud computing platform resource load sequences, as well as the research, improvement, and optimization of dirty data computing algorithms [13].…”

Section: Related Workmentioning

confidence: 99%

Cloud Computing Resource Prediction Model Based on Time Convolutional Network

Xin

Gao

Zhang

2022

Mobile Information Systems

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With the continuous progress and development of modern science and technology, the research on cloud computing-related fields is constantly conducting more in-depth exploration. During the real-life use of cloud computing operations, as the number of tenants continues to increase, the resource usage load capacity of the relevant platform has also undergone tremendous changes. In order to enable the tenants to complete higher-level optimization of the relevant performance and indicators during the actual work of the platform, this article explains how to perform related network resource models on the premise of cloud computing operation management. The researchers used this cloud computing network operation forecasting system as the basic point of view for experimental research and explained and summarized all relevant research results and specific instructions on multivariable load sequences in detail. The multivariable load sequence is embedded in the dimension of the phase space in the calculation process, and the generalization operation from single variable to multivariable can be carried out. However, every time the expansion calculation is performed, the selection criteria available for the researcher to calculate will be reduced, making the result a reconstructed phase space with uncertainty. Therefore, in order to ensure the accuracy of the data of the research experiment results, the researchers must simplify the model, focus on further discussing the correlation between multivariable load sequences in the mechanism, and reduce the number of data calculations in the process. The redundant information generated can select more reasonable data information as input variables in the research process.

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Section: Related Workmentioning

confidence: 99%

Cloud Computing Resource Prediction Model Based on Time Convolutional Network

Xin

Gao

Zhang

2022

Mobile Information Systems

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“…Mehdi et al [33] introduced a stochastic approach to evaluate the reputation of services by leveraging the correlation information among different QoS metrics. Comi et al [34] proposed a hybrid service composition method by exploiting users' reputation of QoS to help users discover and select high-quality services in multicloud environment. However, these studies do not take location information into consideration on the QoS prediction.…”

Section: Reputationmentioning

confidence: 99%

From Reputation Perspective: A Hybrid Matrix Factorization for QoS Prediction in Location-Aware Mobile Service Recommendation System

Wen

Wang

2019

Mobile Information Systems

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With the great development of mobile services, the Quality of Services (QoS) becomes an essential factor to meet end users' personalized requirement on the nonfunctional performance of mobile services. However, most of the QoS values in real cases are unattainable because a service user would only invoke some specific mobile services. erefore, how to predict the missing QoS values and recommend high-quality services to end users becomes a significant challenge in mobile service recommendation research. Previous QoS prediction researches demonstrate that the nonfunctional performance of mobile services is closely related to users' location information. However, most location-aware QoS prediction methods ignore the premise that the obtainable QoS values observed by different users in same location region would probably be untrustworthy, which will lead to inaccurate and unreliable prediction results. To make credible location-aware QoS prediction, we propose a hybrid matrix factorization method integrated location and reputation information (LRMF) to predict the unattainable QoS values. Our approach firstly cluster users into different locational region based on their geographical distribution, and then we compute users' reputation to identify untrustworthy users in every locational region. Finally, the unknown QoS values can be predicted by integrating locational cluster information and users' reputation into a hybrid matrix factorization model. Comprehensive experiments are conducted on a public QoS dataset which contains sufficient real-world service invocation records. e evaluation results indicate that our LRMF method can effectively reduce the impact of unreliable users on QoS prediction and make credible mobile service recommendation.

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“…The authors proposed reputation-based model in [38]. The sole purpose of the proposed work is to provide quality of services.…”

Section: Related Workmentioning

confidence: 99%

Virtual Machine Placement via Bin Packing in Cloud Data Centers

et al. 2018

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With the increasing size of cloud data centers, the number of users and virtual machines (VMs) increases rapidly. The requests of users are entertained by VMs residing on physical servers. The dramatic growth of internet services results in unbalanced network resources. Resource management is an important factor for the performance of a cloud. Various techniques are used to manage the resources of a cloud efficiently. VM-consolidation is an intelligent and efficient strategy to balance the load of cloud data centers. VM-placement is an important subproblem of the VM-consolidation problem that needs to be resolved. The basic objective of VM-placement is to minimize the utilization rate of physical machines (PMs). VM-placement is used to save energy and cost. An enhanced levy-based particle swarm optimization algorithm with variable sized bin packing (PSOLBP) is proposed for solving the VM-placement problem. Moreover, the best-fit strategy is also used with the variable sized bin packing problem (VSBPP). Simulations are done to authenticate the adaptivity of the proposed algorithm. Three algorithms are implemented in Matlab. The given algorithm is compared with simple particle swarm optimization (PSO) and a hybrid of levy flight and particle swarm optimization (LFPSO). The proposed algorithm efficiently minimized the number of running PMs. VM-consolidation is an NP-hard problem, however, the proposed algorithm outperformed the other two algorithms.

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A Reputation-Based Approach to Improve QoS in Cloud Service Composition

Cited by 15 publications

References 28 publications

Cloud Computing Resource Prediction Model Based on Time Convolutional Network

Cloud Computing Resource Prediction Model Based on Time Convolutional Network

From Reputation Perspective: A Hybrid Matrix Factorization for QoS Prediction in Location-Aware Mobile Service Recommendation System

Virtual Machine Placement via Bin Packing in Cloud Data Centers

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