A new QoS prediction model using hybrid IOWA-ANFIS with fuzzy C-means, subtractive clustering and grid partitioning

Article Computer Science, Software Engineering

Distributed service-level agreement management with smart contracts and blockchain

Rafael Brundo Uriarte et al.

Summary: The paper proposes a novel SLA management framework that utilizes a two-level blockchain architecture to enable dynamic specification and enforcement of SLAs, leading to increased flexibility and trust in cloud services.

CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE (2021)

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Article Computer Science, Interdisciplinary Applications

QoS Prediction based on temporal information and request context

Bingzhuo Li et al.

Summary: This study proposes a new method called QSPC that effectively utilizes context data to predict QoS values, and introduces a sequence-to-sequence layer to capture temporal information in the dataset for more accurate predictions.

SERVICE ORIENTED COMPUTING AND APPLICATIONS (2021)

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Article Engineering, Electrical & Electronic

Online QoS Prediction in the Cloud Environments Using Hybrid Time-Series Data Mining Approach

Amin Keshavarzi et al.

Summary: This paper proposes a novel hybrid approach, using time series clustering, minimum description length, and dynamic time warping similarity, to analyze user needs and provide the best-fit quality-of-service prediction solution to users through multi-cloud.

IRANIAN JOURNAL OF SCIENCE AND TECHNOLOGY-TRANSACTIONS OF ELECTRICAL ENGINEERING (2021)

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Article Computer Science, Information Systems

Self directed learning based workload forecasting model for cloud resource management

Jitendra Kumar et al.

Summary: Workload prediction is crucial for intelligent resource scaling and load balancing in cloud services. This paper introduces a self directed workload forecasting method that improves accuracy by capturing error trends and using a heuristic approach based on blackhole phenomena for neural network training. Empirical evaluation shows significant reduction in forecast error compared to existing models, validating the efficacy of the proposed method.

INFORMATION SCIENCES (2021)

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Article Computer Science, Information Systems

UFFDFR: Undersampling framework with denoising, fuzzy c-means clustering, and representative sample selection for imbalanced data classification

Ming Zheng et al.

Summary: A novel three-stage undersampling framework, UFFDFR, is proposed to improve classification performance on imbalanced data by removing noise and unrepresentative samples. Experiments show that UFFDFR outperformed classic and state-of-the-art clustering-based undersampling methods in terms of F-measure, G-mean, and AUC for five classification algorithms.

INFORMATION SCIENCES (2021)

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Review Multidisciplinary Sciences

Trends in Using IoT with Machine Learning in Health Prediction System

Amani Aldahiri et al.

Summary: Machine learning in combination with IoT is crucial in healthcare for intelligent decision-making, real-time patient monitoring, and disease prediction. Different ML algorithms may have varying impacts on prediction results, especially in clinical decision-making processes. Optimizing the choice of ML algorithms based on IoT datasets is essential for predicting critical healthcare data accurately.

FORECASTING (2021)

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Article Computer Science, Theory & Methods