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

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...

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Bibliographic Details
Published in:Journal of grid computing Vol. 20; no. 4; p. 40
Main Authors: Dogani, Javad, Khunjush, Farshad, Seydali, Mehdi
Format: Journal Article
Language:English
Published: Dordrecht Springer Netherlands 01.12.2022
Springer Nature B.V
Subjects:
Applications programs
Cloud computing
Coders
Computer engineering
Computer Science
Containers
Cost control
Encoders-Decoders
Error reduction
Feature extraction
Management of Computing and Information Systems
Neural networks
Processor Architectures
Provisioning
Resource allocation
Resource utilization
Scaling
Software
Time series
User Interfaces and Human Computer Interaction
Virtual environments
Virtual memory systems
Workloads
Elasticity
Cloud computing
Attention-based GRU encoder-decoder
Time series prediction
Horizontal autoscaling
Kubernetes
ISSN:1570-7873, 1572-9184
Online Access:Get full text
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Summary: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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ISSN:1570-7873
1572-9184
DOI:10.1007/s10723-022-09634-x