Cloud failure prediction based on traditional machine learning and deep learning

Asmawi, Tengku Nazmi Tengku; Ismail, Azlan; Shen, Jun

doi:10.1186/s13677-022-00327-0

Cited by 15 publications

(4 citation statements)

References 45 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This approach, coupled with our focus on inductive representation learning for scalable capture of large, complex network topologies, differentiates our methodology from existing studies. Additionally, the cloud failure prediction based on traditional ML and deep learning algorithms [43], and the resource allocation strategies in collaborative cloud-edge computing systems [44], provide further context to the evolving landscape of cloud computing and resource management, affirming the uniqueness and relevance of our research.…”

Section: Related Workmentioning

confidence: 75%

Anomaly Detection in Cloud Computing using Knowledge Graph Embedding and Machine Learning Mechanisms

Mitropoulou,

Kokkinos,

Soumplis

et al. 2023

J Grid Computing

View full text Add to dashboard Cite

The orchestration of cloud computing infrastructures is challenging, considering the number, heterogeneity and dynamicity of the involved resources, along with the highly distributed nature of the applications that use them for computation and storage. Evidently, the volume of relevant monitoring data can be significant, and the ability to collect, analyze, and act on this data in real time is critical for the infrastructure’s efficient use. In this study, we introduce a novel methodology that adeptly manages the diverse, dynamic, and voluminous nature of cloud resources and the applications that they support. We use knowledge graphs to represent computing and storage resources and illustrate the relationships between them and the applications that utilize them. We then train GraphSAGE to acquire vector-based representations of the infrastructures’ properties, while preserving the structural properties of the graph. These are efficiently provided as input to two unsupervised machine learning algorithms, namely CBLOF and Isolation Forest, for the detection of storage and computing overusage events, where CBLOF demonstrates better performance across all our evaluation metrics. Following the detection of such events, we have also developed appropriate re-optimization mechanisms that ensure the performance of the served applications. Evaluated in a simulated environment, our methods demonstrate a significant advancement in anomaly detection and infrastructure optimization. The results underscore the potential of this closed-loop operation in dynamically adapting to the evolving demands of cloud infrastructures. By integrating data representation and machine learning methods with proactive management strategies, this research contributes substantially to the field of cloud computing, offering a scalable, intelligent solution for modern cloud infrastructures.

show abstract

Section: Related Workmentioning

confidence: 75%

Anomaly Detection in Cloud Computing using Knowledge Graph Embedding and Machine Learning Mechanisms

Mitropoulou,

Kokkinos,

Soumplis

et al. 2023

J Grid Computing

View full text Add to dashboard Cite

show abstract

“…The paper presented a workload dynamic redistribution strategy, which effectively balances the load between nodes by adjusting the workload of each node in each iteration based on the varying computing performances within the cluster. Reference 146 discussed failure prediction of cloud using machine learning and deep learning approaches. Authors have presented a comprehensive comparison and evaluation of predictive models for job and task failure.…”

Section: Taxonomy Of Fault Tolerance Approachesmentioning

confidence: 99%

Fault‐tolerance approaches for distributed and cloud computing environments: A systematic review, taxonomy and future directions

Kirti,

Maurya,

Yadav

2024

Concurrency and Computation

View full text Add to dashboard Cite

Fault tolerance is crucial in ensuring smooth working of distributed and cloud computing. It is challenging to implement because of the constantly changing infrastructure and complex configurations in distributed and cloud computing. Implementation of various fault tolerance methods require domain‐specific knowledge as well as in‐depth understanding of the existing techniques and approaches. Recent surveys on fault tolerance in cloud and distributed environments exist, but they have limitations. This article systematically reviews fault tolerance approaches in distributed and cloud computing and discusses their taxonomy. Based on the taxonomy provided, fault‐tolerance approaches are divided into four types, that is, reactive approaches, proactive approaches, adaptive approaches, and hybrid approaches. Reactive approaches provide a preventive measure after the occurrence of faults in the system. Proactive approaches prevent the system or minimize failure effects by predicting in advance. The adaptive approaches predict, learn, and adapt the changes to deal with new faults in the system. The hybrid approaches combine reactive, proactive, and adaptive approaches. The objective of this article is to give a better understanding of handling faults using suitable approaches and further compare them on various parameters. The paper also presents a promising research direction based on the challenges and issues in multiple approaches.

show abstract

“…However, there are also approaches which has combined both machine learning and deep learning scheme in order to form a hybrid approach to further harness the predictive potential of both the learning schemes for optimized performance. One such unique and simple form of a hybrid learning model has been presented in work of Asmawi et al [51] by integrating deep learning and machine learning approach. The idea of this work is towards predicting the failures of cloud-based software.…”

Section: Existing Studies Deploying Hybrid Learning Approachesmentioning

confidence: 99%

Insights of effectivity analysis of learning-based approaches towards software defect prediction

Rai,

Arcot Prashant

2024

IJECE

View full text Add to dashboard Cite

Software defect prediction is one of the essential sets of operation towards mitigating issues of risk management in software development known to contribute towards enhancing the quality of software. There is evolution of various methodologies towards resolving this issue while learning-based methodology is witnessed to be the most dominant contributor. The problem identified is that there are yet many unsolved queries associated with practical viability of such learning-based approach adoption in software quality management. Proposed approaches discussed in this paper contributes towards mitigating this challenge by introducing a simplified, compact, and crisp analysis of effectiveness associated with learning-based schemes. The paper presents its major findings of effectivity analysis of machine learning, deep learning, hybrid, and other miscellaneous approaches deployed for fault prediction followed by highlighting research trend. The major findings infer that feature selection, data imbalance, interpretability, and in adequate involvement of context are prime gaps in existing methods. The paper also contributes towards research gap as well as essential learning outcomes of present review work.

show abstract

Cloud failure prediction based on traditional machine learning and deep learning

Cited by 15 publications

References 45 publications

Anomaly Detection in Cloud Computing using Knowledge Graph Embedding and Machine Learning Mechanisms

Anomaly Detection in Cloud Computing using Knowledge Graph Embedding and Machine Learning Mechanisms

Fault‐tolerance approaches for distributed and cloud computing environments: A systematic review, taxonomy and future directions

Insights of effectivity analysis of learning-based approaches towards software defect prediction

Contact Info

Product

Resources

About