Adaptive AI-based auto-scaling for Kubernetes

Toka, László; Dobreff, Gergely; Fodor, Balazs; Sonkoly, Balázs

doi:10.1109/ccgrid49817.2020.00-33

Cited by 43 publications

(24 citation statements)

References 16 publications

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“…Auto-Scaling Function (The Framework without An Open-Source Server) [5][6][7] User Control for Limited Custom Service Quality Requirements [8] Intelligent Automatic Expansion Kubernetes HPA [9] Auto-Scaling Auto-scaling of Cloud Resources [10] Flexible and Skillful Resource Allocation, The Core Technology of Cloud Computing [11,12] Development of Appropriate Threshold-Based Rules [13] Threshold-based Investigate the automatic expansion system [14] Elastic Docker [15] Horizontal automatic scaling [16] Reinforcement Learning-Based Adaptive Fuzzy Logic Controller [17] Threshold-based Solution for Automatic Expansion of Horizontal Containers [18] Reinforce Learning Solution (i.e., Q Learning, Dyna-Q and Model-Based) [19] Dynamic Number of Servers Using A Threshold-Based Expansion Policy [20] Table 1. Cont.…”

Section: Microservice Computingmentioning

confidence: 99%

“…Threshold-based: Studies focusing on threshold-based expansion rules have improved vertical and horizontal elasticity performance in cloud systems of lightweight virtualization technology [14][15][16]. Specifically, one study examined a resource utilization-based automatic expansion system that demonstrates Kubernetes' VPA through its ability to dynamically adjust container allocation in the Kubernetes cluster without interruption [14].…”

Section: Auto-scalingmentioning

confidence: 99%

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Improved Q Network Auto-Scaling in Microservice Architecture

et al. 2022

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Microservice architecture has emerged as a powerful paradigm for cloud computing due to its high efficiency in infrastructure management as well as its capability of largescale user service. A cloud provider requires flexible resource management to meet the continually changing demands, such as auto-scaling and provisioning. A common approach used in both commercial and open-source computing platforms is workload-based automatic scaling, which expands instances by increasing the number of incoming requests. Concurrency is a request-based policy that has recently been proposed in the evolving microservice framework; in this policy, the algorithm can expand its resources to the maximum number of configured requests to be processed in parallel per instance. However, it has proven difficult to identify the concurrency configuration that provides the best possible service quality, as various factors can affect the throughput and latency based on the workloads and complexity of the infrastructure characteristics. Therefore, this study aimed to investigate the applicability of an artificial intelligence approach to request-based auto-scaling in the microservice framework. Our results showed that the proposed model could learn an effective expansion policy within a limited number of pods, thereby showing an improved performance over the underlying auto expansion configuration.

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Section: Microservice Computingmentioning

confidence: 99%

Section: Auto-scalingmentioning

confidence: 99%

Improved Q Network Auto-Scaling in Microservice Architecture

et al. 2022

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“…Elastic container deployment in cloud computing. The problem of elastic container deployment in Cloud computing has been studied intensively at different resource levels: container deployment, 16‐19 cluster deployment 20‐22 or both 23,24 . These approaches use horizontal methods, 17,21‐23 vertical methods 18,24 or hybrid approaches 16,19,20 depending on the elasticity dimensions.…”

Section: Related Workmentioning

confidence: 99%

“…The problem of elastic container deployment in Cloud computing has been studied intensively at different resource levels: container deployment, 16‐19 cluster deployment 20‐22 or both 23,24 . These approaches use horizontal methods, 17,21‐23 vertical methods 18,24 or hybrid approaches 16,19,20 depending on the elasticity dimensions. However these solutions lack the ability to include spatial aspects in their adaptation processes, which is essential to reduce network latency—a key performance factor for global web applications.…”

Section: Related Workmentioning

confidence: 99%

“…Such manual adaptation decisions are naive and inefficient as they can lead to costly, over‐provisioning issues (excessive deployment of clusters), performance issues (improper cluster placement) and/or adaptation issues, for example, performance degradation during adaptation and/or unnecessary cluster relocation. Much of the prior work on elastic container deployment problems in the Cloud does not consider spatial aspects of workload variations as they focus on local techniques within a single data center to handle fluctuations in workload volumes through localized auto‐scaling of containers, 16‐19 clusters, 20‐22 or both 23,24 . Other work proposes techniques to handle geo‐workload variations, 11,25 however, they do not use containers and hence do not benefit from the benefits of container‐based solutions, and they do not maintain application performance during adaptation.…”

Section: Introductionmentioning

confidence: 99%

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Elastic deployment of container clusters across geographically distributed cloud data centers for web applications

Aldwyan

Sinnott

Jayaputera

2021

Concurrency and Computation

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Containers such as Docker provide a lightweight virtualization technology. They have gained popularity in developing, deploying and managing applications in and across Cloud platforms. Container management and orchestration platforms such as Kubernetes run application containers in virtual clusters that abstract the overheads in managing the underlying infrastructures to simplify the deployment of container solutions. These platforms are well suited for modern web applications that can give rise to geographic fluctuations in use based on the location of users. Such fluctuations often require dynamic global deployment solutions. A key issue is to decide how to adapt the number and placement of clusters to maintain performance, whilst incurring minimum operating and adaptation costs. Manual decisions are naive and can give rise to: over‐provisioning and hence cost issues; improper placement and performance issues, and/or unnecessary relocations resulting in adaptation issues. Elastic deployment solutions are essential to support automated and intelligent adaptation of container clusters in geographically distributed Clouds. In this article, we propose an approach that continuously makes elastic deployment plans aimed at optimizing cost and performance, even during adaptation processes, to meet service level objectives (SLOs) at lower costs. Meta‐heuristics are used for cluster placement and adjustment. We conduct experiments on the Australia‐wide National eResearch Collaboration Tools and Resources Research Cloud using Docker and Kubernetes. Results show that with only a 0.5 ms sacrifice in SLO for the 95th percentile of response times we are able to achieve up to 44.44% improvement (reduction) in cost compared to a naive over‐provisioning deployment approach.

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