Cdascaler: a cost-effective dynamic autoscaling approach for containerized microservices

Shafi, Numan; Abdullah, Muhammad; Iqbal, Waheed; Erradi, Abdelkarim; Bukhari, Faisal

doi:10.1007/s10586-023-04228-y

Cited by 1 publication

(4 citation statements)

References 54 publications

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“…For instance, one of the latest works [3] suggests a custom autoscaler, which aims to select the correct number of pod resources, such as the number of CPUs to be allocated to pods (pod pinning). It predicts the load and calculates the number of CPU cores and the number of pods allocated to a specific workload pinned to the CPU.…”

Section: Autoscaling In Kubernetesmentioning

confidence: 99%

“…Values falling at least 1.5 × IQR above Q3 or below Q1 are considered anomalous. The IQR, Q1, and Q3 are computed as presented in Equation (3).…”

Section: Threshold-determination Approachmentioning

confidence: 99%

“…Once the RPC and RPP are calculated, the metrics M nozeroes are sorted by the RPC value in ascending order and the IQR method is applied to remove anomalies. The value of the first quartile (Q 1 RPC ) and third quartile (Q 3 RPC ) of the RPC is identified using Equation (3). Finally, all the metrics, where…”

Section: Threshold-determination Approachmentioning

confidence: 99%

“…However, the authors of Kubernetes do not provide recommendations on setting the threshold, especially for cases when the user aims to ensure that the application performs as per the Service Level Objectives (SLOs) defined in a Service Level Agreement (SLA). As a result, the determination of the threshold becomes a long and challenging process [3]. If the thresholds are set too low, this can result in the overprovisioning of resources.…”

Section: Introductionmentioning

confidence: 99%

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SLA-Adaptive Threshold Adjustment for a Kubernetes Horizontal Pod Autoscaler

Pozdniakova,

Mažeika,

Cholomskis

2024

Electronics

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Kubernetes is an open-source container orchestration system that provides a built-in module for dynamic resource provisioning named the Horizontal Pod Autoscaler (HPA). The HPA identifies the number of resources to be provisioned by calculating the ratio between the current and target utilisation metrics. The target utilisation metric, or threshold, directly impacts how many and how quickly resources will be provisioned. However, the determination of the threshold that would allow satisfying performance-based Service Level Objectives (SLOs) is a long, error-prone, manual process because it is based on the static threshold principle and requires manual configuration. This might result in underprovisioning or overprovisioning, leading to the inadequate allocation of computing resources or SLO violations. Numerous autoscaling solutions have been introduced as alternatives to the HPA to simplify the process. However, the HPA is still the most widely used solution due to its ease of setup, operation, and seamless integration with other Kubernetes functionalities. The present study proposes a method that utilises exploratory data analysis techniques along with moving average smoothing to identify the target utilisation threshold for the HPA. The objective is to ensure that the system functions without exceeding the maximum number of events that result in a violation of the response time defined in the SLO. A prototype was created to adjust the threshold values dynamically, utilising the proposed method. This prototype enables the evaluation and comparison of the proposed method with the HPA, which has the highest threshold set that meets the performance-based SLOs. The results of the experiments proved that the suggested method adjusts the thresholds to the desired service level with a 1–2% accuracy rate and only 4–10% resource overprovisioning, depending on the type of workload.

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Section: Autoscaling In Kubernetesmentioning

confidence: 99%

“…Values falling at least 1.5 × IQR above Q3 or below Q1 are considered anomalous. The IQR, Q1, and Q3 are computed as presented in Equation (3).…”

Section: Threshold-determination Approachmentioning

confidence: 99%

Section: Threshold-determination Approachmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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