OffDQ: An Offline Deep Learning Framework for QoS Prediction

Chattopadhyay, Sreya; Chanda, Richik; Kumar, Suraj; Adak, Chandranath

doi:10.1145/3485447.3512107

Cited by 5 publications

(3 citation statements)

References 16 publications

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“…However, while matrix decompositionbased methods are effective in handling sparsity, they encounter additional challenges, such as noise handling or difficulty in capturing higher-order relationships among users/services, potentially leading to inaccuracies in predictions. (ii) Designing additional data imputation method: This is used sometimes to predict the missing values [14], [20], [21], [48]. These imputed values are then utilized by the prediction module to estimate the final QoS value for the target user-service pair.…”

Section: Related Workmentioning

confidence: 99%

“…These robust loss functions downweight the impact of outliers during the training process, allowing the model to focus more on learning from the majority of the data. (i) Detecting and removing outliers: Alternatively, certain methods follow the explicit outliers detection [8], [21], [23], [52] and followed by subsequent removal of them, resulting in a more reliable dataset for the prediction model.…”

Section: Presence Of Outliersmentioning

confidence: 99%

“…Among recent advancements on QoS prediction, learning-based methods [8], [17], [18], [19], [20], [21], [22], [23], [24], [25], [26] are particularly notable for their performance improvement, as they excel in capturing the complex features of users and services. Nevertheless, the learningbased methods that heavily rely on contextual features [18], [27], [28], [29], [30] for QoS prediction may struggle to achieve desirable prediction accuracy when collaborative QoS features, which are often more relevant, are absent or not adequately considered.…”

Section: Introductionmentioning

confidence: 99%

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TRQP: Trust-Aware Real-Time QoS Prediction Framework Using Graph-Based Learning

Kumar

Chattopadhyay

2022

Service-Oriented Computing

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In the realm of modern service-oriented architecture, ensuring Quality of Service (QoS) is of paramount importance. The ability to predict QoS values in advance empowers users to make informed decisions, ensuring that the chosen service aligns with their expectations. This harmonizes seamlessly with the core objective of service recommendation, which is to adeptly steer users towards services tailored to their distinct requirements and preferences. However, achieving accurate and real-time QoS predictions in the presence of various issues and anomalies, including outliers, data sparsity, grey sheep instances, and cold start scenarios, remains a challenge. Current state-of-the-art methods often fall short when addressing these issues simultaneously, resulting in performance degradation. In response, in this paper, we introduce an anomaly-resilient real-time QoS prediction framework (called ARRQP). Our primary contributions encompass proposing an innovative approach to QoS prediction aimed at enhancing prediction accuracy, with a specific emphasis on improving resilience to anomalies in the data. ARRQP utilizes the power of graph convolution techniques, a powerful tool in graph-based machine learning, to capture intricate relationships and dependencies among users and services. By leveraging graph convolution, our framework enhances its ability to model and seize complex relationships within the data, even when the data is limited or sparse. ARRQP integrates both contextual information and collaborative insights, enabling a comprehensive understanding of user-service interactions. By utilizing robust loss functions, this approach effectively reduces the impact of outliers during the training of the predictive model. Additionally, we introduce a method for detecting grey sheep users or services that is resilient to sparsity. These grey sheep instances are subsequently treated separately for QoS prediction. Furthermore, we address the cold start problem as a distinct challenge by emphasizing contextual features over collaborative features. This approach allows us to effectively handle situations where newly introduced users or services lack historical data. Experimental results on the publicly available benchmark WS-DREAM dataset demonstrate the framework's effectiveness in achieving accurate and timely QoS predictions, even in scenarios where anomalies abound.

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Section: Related Workmentioning

confidence: 99%

Section: Presence Of Outliersmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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TRQP: Trust-Aware Real-Time QoS Prediction Framework Using Graph-Based Learning

Kumar

Chattopadhyay

2022

Service-Oriented Computing

Self Cite

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QoS prediction of cloud services by selective ensemble learning on prefilling‐based matrix factorizations

Mao,

Chen,

Towey

et al. 2024

Concurrency and Computation

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SummaryWhen selecting services from a cloud center to build applications, the quality of service (QoS) is an important nonfunctional attribute to be considered. However, in actual application scenarios, the QoS details for many services may not be available. This has led to a situation where prediction of the missing QoS records for services has become a key problem for service selection. This article presents a selective ensemble learning (SEL) framework for prefilling‐based matrix factorization (PFMF) predictors. In each PFMF predictor, the improved collaborative filtering is defined by examining the stability of the QoS records when measuring the similarity of users (or services), and then used to prefill empty records in the initial QoS matrix. To ensure the diversity of the basic PFMF predictors, various prefilled QoS matrices are constructed for the matrix factorization. In this process, different reference weights are assigned to the original and the prefilled QoS records. Finally, particle swarm optimization is used to set the ensemble weights for the basic PFMF predictors. The proposed SEL on PFMF (SEL‐PFMF) algorithm is validated on a public dataset, where its prediction performance outperforms the state‐of‐the‐art algorithms, and also shows good stability.

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