Workload-Aware Performance Tuning for Autonomous DBMSs

Yan, Zhengtong; Lu, Jiaheng; Chainani, Naresh; Lin, Chunbin

doi:10.1109/icde51399.2021.00267

Cited by 5 publications

(1 citation statement)

References 30 publications

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“…Unlike applications in the Virtual Machine (Altintas et al 2005) era, where minute-level manual operation is enough, new emerging applications usually require second-level operations. Meanwhile, many fast-developing applications have noticeable cyclical fluctuations (Yan et al 2021;Higginson et al 2020;Cortez et al 2017;Atikoglu et al 2012;Chen et al 2008), such as Internet broadcast, e-Learning, online game, etc. This kind of application repeatedly emerges with peaks and valleys in business demand, so flexible resource utilization with low latency is re-quired.…”

Section: Introductionmentioning

confidence: 99%

AHPA: Adaptive Horizontal Pod Autoscaling Systems on Alibaba Cloud Container Service for Kubernetes

Zhou

Zhang

Lingna

et al. 2023

AAAI

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The existing resource allocation policy for application instances in Kubernetes cannot dynamically adjust according to the requirement of business, which would cause an enormous waste of resources during fluctuations. Moreover, the emergence of new cloud services puts higher resource management requirements. This paper discusses horizontal POD resources management in Alibaba Cloud Container Services with a newly deployed AI algorithm framework named AHPA - the adaptive horizontal pod auto-scaling system. Based on a robust decomposition forecasting algorithm and performance training model, AHPA offers an optimal pod number adjustment plan that could reduce POD resources and maintain business stability. Since being deployed in April 2021, this system has expanded to multiple customer scenarios, including logistics, social networks, AI audio and video, e-commerce, etc. Compared with the previous algorithms, AHPA solves the elastic lag problem, increasing CPU usage by 10% and reducing resource cost by more than 20%. In addition, AHPA can automatically perform flexible planning according to the predicted business volume without manual intervention, significantly saving operation and maintenance costs.

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