ARIMA-PID: container auto scaling based on predictive analysis and control theory

Containerization has become a widely popular virtualization mechanism alongside Virtual Machines (VMs) to deploy applications and services in the cloud. Containers form the backbone of the modern architectures around microservices and provide a lightweight virtualization mechanism for IoT and Edge s...

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Bibliographic Details
Published inMultimedia tools and applications Vol. 83; no. 9; pp. 26369 - 26386
Main Authors Joshi, Nisarg S, Raghuwanshi, Raghav, Agarwal, Yash M, Annappa, B, Sachin, DN
Format Journal Article
LanguageEnglish
Published New York Springer US 01.03.2024
Springer Nature B.V
Subjects
Algorithms
Autoregressive models
Central processing units
Computer Communication Networks
Computer Science
Containers
Control theory
CPUs
Data Structures and Information Theory
Elasticity
Multimedia Information Systems
Predictive control
Proportional integral derivative
Resource utilization
Scaling
Special Purpose and Application-Based Systems
Virtual environments
Cloud computing
Virtual machines
Containerization
Auto-scaling
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Summary:Containerization has become a widely popular virtualization mechanism alongside Virtual Machines (VMs) to deploy applications and services in the cloud. Containers form the backbone of the modern architectures around microservices and provide a lightweight virtualization mechanism for IoT and Edge systems. Elasticity is one of the key requirements of modern applications with various constraints ranging from Service Level Agreements (SLA) to optimization of resource utilization, cost management, etc. Auto Scaling is a technique used to attain elasticity by scaling the number of containers or resources. This work introduces a novel mechanism for auto-scaling containers in cloud environments, addressing the key elasticity requirement in modern applications. The proposed mechanism combines predictive analysis using the Auto-Regressive Integrated Moving Average (ARIMA) model and control theory utilizing the Proportional-Integral-Derivative (PID) controller. The major contributions of this work include the development of the ARIMA-PID algorithm for forecasting resource utilization and maintaining desired levels, comparing ARIMA-PID with existing threshold mechanisms, and demonstrating its superior performance in terms of CPU utilization and average response times. Experimental results showcase improvements of approximately 10% in CPU utilization and 30%
ISSN:1573-7721
1380-7501
1573-7721
DOI:10.1007/s11042-023-16587-0