Automatic Cloud Resource Scaling Algorithm based on Long Short-Term Memory Recurrent Neural Network

Shahin, Ashraf A.

doi:10.14569/ijacsa.2016.071236

Cited by 15 publications

(2 citation statements)

References 14 publications

(23 reference statements)

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“…Qiu et al 109 proposed a deep learning based approach to predict the workload of virtual machines in a cloud environment, using an underlying model made of a deep belief network (DBN), consisting of multilayer of restricted Boltzmann machines (RBMs) and regression layers 110 . Shahin 111 proposed a dynamic threshold‐based auto‐tuning algorithm that uses a long short‐term memory recurrent neural network 112 (LSTM‐RNN) to predict the required resource size and automatically scales the virtual resources based on the predicted values.…”

Section: Research Resultsmentioning

confidence: 99%

The operation and maintenance governance of microservices architecture systems: A systematic literature review

Wang

Jiang

Wang

et al. 2022

J Software Evolu Process

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Due to its development agility, continuous delivery, scalability and other characteristics, the microservice architecture systems (MASs) have provided complex business functions to hundreds of millions of users in many application fields. The operation and maintenance governance for a large number of microservices with complex relationships is crucial to ensuring the stability and reliability of an MAS. Although this research field has received certain attention and produced some innovative results, there is a lack of systematic reviews covering the different aspects of it. In this context, the central objective of this study is to carry out a systematic literature review (SLR) in this field, in an attempt to review existing issues, discuss the main trends, and share the findings with the academia. As a result, we start from more than 500 scientific papers published from 2009 to 2021 and extract 144 most significant papers, identify that the main research directions of this field include load balancing, fault detection, and autoscaling. Subsequently, we provide a comprehensive description of these research directions, discuss them in particular detail. We also determine limitations of current work and discuss new directions worth exploring in the future. Consequently, the outcomes will assist professionals and experts in the industry as well as academic researchers to focus more on operation and maintenance governance of MASs and further improve the relevant methods and theoretical systems in this field.

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Section: Research Resultsmentioning

confidence: 99%

The operation and maintenance governance of microservices architecture systems: A systematic literature review

Wang

Jiang

Wang

et al. 2022

J Software Evolu Process

View full text Add to dashboard Cite

show abstract

“…For example, Islam et al [18] implemented load prediction by combining NNs and AR models, and Qiu et al [19] proposed a load prediction method based on RBM and DBN to achieve workload prediction for virtual machines in cloud environments. Ashraf [20] used an LSTM-RNN network in automatic scaling for load prediction of virtual resources, and Guo et al [21] developed a type-awarebased prediction method that determines the current load type based on the dynamic change of the load to switch the prediction method accordingly.…”

Section: Related Workmentioning

confidence: 99%

An Efficient Load Prediction-Driven Scheduling Strategy Model in Container Cloud

Wang

Guo

Zhang

et al. 2023

International Journal of Intelligent Systems

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The rise of containerization has led to the development of container cloud technology, which offers container deployment and management services. However, scheduling a large number of containers efficiently remains a significant challenge for container cloud service platforms. Traditional load prediction methods and scheduling algorithms do not fully consider interdependencies between containers or fine-grained resource scheduling, leading to poor resource utilization and scheduling efficiency. To address these challenges, this paper proposes a new load prediction model CNN-BiGRU-Attention and a container scheduling strategy based on load prediction. The prediction model CNN and BiGRU focus on the local features of load data and long sequence dependencies, respectively, as well as introduce the attention mechanism to make the model more easily capture the features of long distance dependencies in the sequence. A container scheduling strategy based on load prediction is also designed, which first uses the load prediction model to predict the load state and then generates a scheduling strategy based on the load prediction value to determine the change of the number of container replicas in a fine-grained manner based on the load prediction value in the next time window, while the established domain-based container selection method is employed to facilitate the coarse-grained online migration of containers. Experiments conducted using public datasets and open-source simulation platforms demonstrate that the proposed approach achieves a 37.4% improvement in container load prediction accuracy and a 21.7% improvement in container scheduling efficiency compared to traditional methods. These results highlight the effectiveness of the proposed approach in addressing the challenges faced by container cloud service platforms.

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K-AGRUED: A Container Autoscaling Technique for Cloud-based Web Applications in Kubernetes Using Attention-based GRU Encoder-Decoder

2022

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Automatic Cloud Resource Scaling Algorithm based on Long Short-Term Memory Recurrent Neural Network

Cited by 15 publications

References 14 publications

The operation and maintenance governance of microservices architecture systems: A systematic literature review

The operation and maintenance governance of microservices architecture systems: A systematic literature review

An Efficient Load Prediction-Driven Scheduling Strategy Model in Container Cloud

K-AGRUED: A Container Autoscaling Technique for Cloud-based Web Applications in Kubernetes Using Attention-based GRU Encoder-Decoder

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