TY - JOUR
T1 - Burst-Aware Horizontal Autoscaling Based on Deep Learning for Stable Microservices
AU - Park, Jin
AU - Jeong, Byeonghui
AU - Jeon, Jueun
AU - Jeong, Young Sik
N1 - Publisher Copyright:
© This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
PY - 2024
Y1 - 2024
N2 - In the cloud computing environment, container autoscaling is a key resource-management method that provides continuity and scalability for microservices. However, the autoscaling method based on a reactive mechanism is unable to respond instantly to workload changes, leading to resource wastage. Moreover, this method struggles to identify irregular burst states accurately in the workload, resulting in service disruptions. Accordingly, this study proposes a burst-aware horizontal autoscaling (BHAS) method that operates using a proactive mechanism to enhance the stability and resource efficiency of microservices under burst workloads. BHAS uses a time-series forecasting model that combines the reversible instance normalization method with the decomposition linear to predict future resource usage. Then, BHAS flexibly detects local and global bursts in the predicted future workload comprising heterogeneous resource usage. Finally, it performs scaling by calculating the number of efficient containers at each time point for the detected burst and nonburst states. The performance evaluation of BHAS in a cloud-native computing environment revealed that the average number of resource overload instances was reduced by up to 96.74% compared to the existing autoscaling techniques applying the actual container workload trace. In addition, resource utilization improved by approximately 3.3% compared with the reactive mechanismbased autoscaling method.
AB - In the cloud computing environment, container autoscaling is a key resource-management method that provides continuity and scalability for microservices. However, the autoscaling method based on a reactive mechanism is unable to respond instantly to workload changes, leading to resource wastage. Moreover, this method struggles to identify irregular burst states accurately in the workload, resulting in service disruptions. Accordingly, this study proposes a burst-aware horizontal autoscaling (BHAS) method that operates using a proactive mechanism to enhance the stability and resource efficiency of microservices under burst workloads. BHAS uses a time-series forecasting model that combines the reversible instance normalization method with the decomposition linear to predict future resource usage. Then, BHAS flexibly detects local and global bursts in the predicted future workload comprising heterogeneous resource usage. Finally, it performs scaling by calculating the number of efficient containers at each time point for the detected burst and nonburst states. The performance evaluation of BHAS in a cloud-native computing environment revealed that the average number of resource overload instances was reduced by up to 96.74% compared to the existing autoscaling techniques applying the actual container workload trace. In addition, resource utilization improved by approximately 3.3% compared with the reactive mechanismbased autoscaling method.
KW - Burst Detection
KW - Cloud Computing
KW - Container Autoscaling
KW - Resource Management
KW - Time-Series Forecasting
UR - http://www.scopus.com/inward/record.url?scp=85210043275&partnerID=8YFLogxK
U2 - 10.22967/HCIS.2024.14.067
DO - 10.22967/HCIS.2024.14.067
M3 - Article
AN - SCOPUS:85210043275
SN - 2192-1962
VL - 14
JO - Human-centric Computing and Information Sciences
JF - Human-centric Computing and Information Sciences
M1 - 67
ER -