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 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%

Automated summary

Containerization has become a widely used virtualization mechanism for deploying applications and services in the cloud alongside Virtual Machines (VMs). This work introduces a novel mechanism for auto-scaling containers in cloud environments by utilizing predictive analysis with the ARIMA model and control theory with the PID controller. Experimental results demonstrate the superior performance of the proposed ARIMA-PID algorithm in terms of CPU utilization and average response times.