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

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Abstract

Cloud service providers can operate several execution instances on a single physical server using virtualization technology, which improves resource utilization. In recent years, container-based virtualization has been developed as a remarkably lightweight alternative to virtual machines. Containers consume less memory than virtual machines, enabling faster setup and portability. Cloud-based applications require dynamic resource allocation in response to fluctuations in the number of incoming requests. Most articles on proactive autoscaling in cloud computing have shortcomings in two ways. 1) During feature extraction, the temporal patterns of the data are ignored, and the historical sequences are assigned equal weight. 2) Existing research omits cool down time (CDT) from the planning phase. 3) Scaling operations can be performed at any time depending only on the current input workload, resulting in a large number of contradicting scaling actions. In response to the above shortcomings, this paper presents a proactive autoscaling method for web applications in Kubernetes using an attention-based gated recurrent unit (GRU) encoder-decoder (K-AGRUED), which predicts the resource usage of several future steps based on CDT. The results demonstrate that the proposed method reduces prediction error by 2–25% compared to state of the art methods. Our approach significantly reduces scaling operations and under-provisioning compared to the standard horizontal pod autoscaler (HPA) of Kubernetes and two previous studies. The K-AGRUED increases the scaling speedup by a factor of up to five in a real environment.

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Data Availability

The datasets analyzed during scenario1 and scenario2 of experiments in the current study are available at ftp://ftp.ita.ee.lbl.gov/html/contrib/NASA-HTTP.html and ftp://ftp.ita.ee.lbl.gov/html/contrib/WorldCup.html, respectively. The datasets analyzed during scenario3 in current study are available from the corresponding author on reasonable request.

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  1. Department of Computer Science and Engineering and IT, School of Electrical and Computer Engineering, Shiraz University, Mollasadara St., Shiraz, 71348-51154, Iran

    Javad Dogani, Farshad Khunjush & Mehdi Seydali

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  1. Javad Dogani
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Contributions

Javad Dogani: Methodology, Software, Simulation, Writing—original draft.

Farshad Khunjush: Conceptualization, Validation, Methodology, Writing—review & editing.

Mehdi Seydali: Conceptualization, software, Writing—review & editing.

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Correspondence to Farshad Khunjush.

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Dogani, J., Khunjush, F. & Seydali, M. K-AGRUED: A Container Autoscaling Technique for Cloud-based Web Applications in Kubernetes Using Attention-based GRU Encoder-Decoder. J Grid Computing 20, 40 (2022). https://doi.org/10.1007/s10723-022-09634-x

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