Burst-Aware Predictive Autoscaling for Containerized Microservices

Abstract

Autoscaling methods are used for cloud-hosted applications to dynamically scale the allocated resources for guaranteeing Quality-of-Service (QoS). The public-facing application serves dynamic workloads, which contain bursts and pose challenges for autoscaling methods to ensure application performance. Existing State-of-the-art autoscaling methods are burst-oblivious to determine and provision the appropriate resources. For dynamic workloads, it is hard to detect and handle bursts online for maintaining application performance. In this article, we propose a novel burst-aware autoscaling method which detects burst in dynamic workloads using workload forecasting, resource prediction, and scaling decision making while minimizing response time service-level objectives (SLO) violations. We evaluated our approach through a trace-driven simulation, using multiple synthetic and realistic bursty workloads for containerized microservices, improving performance when comparing against existing state-of-the-art autoscaling methods. Such experiments show an increase of <inline-formula><tex-math notation="LaTeX">$\times $</tex-math><alternatives><mml:math><mml:mo>&#x00D7;</mml:mo></mml:math><inline-graphic xlink:href="iqbal-ieq1-2995937.gif"/></alternatives></inline-formula>1.09 in total processed requests, a reduction of <inline-formula><tex-math notation="LaTeX">$\times $</tex-math><alternatives><mml:math><mml:mo>&#x00D7;</mml:mo></mml:math><inline-graphic xlink:href="iqbal-ieq2-2995937.gif"/></alternatives></inline-formula>5.17 for SLO violations, and an increase of <inline-formula><tex-math notation="LaTeX">$\times $</tex-math><alternatives><mml:math><mml:mo>&#x00D7;</mml:mo></mml:math><inline-graphic xlink:href="iqbal-ieq3-2995937.gif"/></alternatives></inline-formula>0.767 cost as compared to the baseline method.