Auto-scaling in Kubernetes-Based Fog Computing Platform

Zheng, Wei Sheng; Yen, Li-Hsing

doi:10.1007/978-981-13-9190-3_35

Cited by 10 publications

(10 citation statements)

References 11 publications

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“…This algorithm, for example, can consider the round trip delay when scheduling instances of time-critical applications. Another Kubernetes-based fog computing platform that manages geographically distributed containers is proposed in Reference [27]. In this paper, the authors design a service called Autoscaling Broker (AS Broker) to get raw metrics and calculate the optimal number of replicas for HPA based on both CPU and memory usage while reducing applications' response time.…”

Section: Related Workmentioning

confidence: 99%

Horizontal Pod Autoscaling in Kubernetes for Elastic Container Orchestration

Nguyen

Yeom

Kim

et al. 2020

Sensors

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Kubernetes, an open-source container orchestration platform, enables high availability and scalability through diverse autoscaling mechanisms such as Horizontal Pod Autoscaler (HPA), Vertical Pod Autoscaler and Cluster Autoscaler. Amongst them, HPA helps provide seamless service by dynamically scaling up and down the number of resource units, called pods, without having to restart the whole system. Kubernetes monitors default Resource Metrics including CPU and memory usage of host machines and their pods. On the other hand, Custom Metrics, provided by external software such as Prometheus, are customizable to monitor a wide collection of metrics. In this paper, we investigate HPA through diverse experiments to provide critical knowledge on its operational behaviors. We also discuss the essential difference between Kubernetes Resource Metrics (KRM) and Prometheus Custom Metrics (PCM) and how they affect HPA’s performance. Lastly, we provide deeper insights and lessons on how to optimize the performance of HPA for researchers, developers, and system administrators working with Kubernetes in the future.

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Section: Related Workmentioning

confidence: 99%

Horizontal Pod Autoscaling in Kubernetes for Elastic Container Orchestration

Nguyen

Yeom

Kim

et al. 2020

Sensors

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“…Several papers are based on Kubernetes. Zheng et al propose to vary the number of pods according to the load, but does not address the question of pod placement nor efficient routing between the end users and their closest pod [27]. Several other works propose location-aware pod placement mechanisms for Kubernetes [4], [8], [14], [24].…”

Section: State Of the Artmentioning

confidence: 99%

“…Voilà integrates seamlessly with Kubernetes, the de-facto standard container orchestration framework in clusters and data centers [12]. Kubernetes is also a promising basis for designing future-generation fog computing platforms [4], [7], [14], [24], [27]. Voilà continuously monitors the request workload produced by all potential traffic sources in the system, and uses efficient algorithms to determine the number and location of replicas that are necessary to maintain the application's QoS within its expected bounds despite potentially large variations in the request workload characteristics.…”

Section: Introductionmentioning

confidence: 99%

Voilà: Tail-Latency-Aware Fog Application Replicas Autoscaler

Fahs

Pierre

Elmroth

2020

2020 28th International Symposium on Modeling, Analysis, and Simulation of Computer and Telecommunication Systems (MASCOTS)

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Latency-sensitive fog computing applications may use replication both to scale their capacity and to place application instances as close as possible to their end users. In such geo-distributed environments, a good replica placement should maintain the tail network latency between end-user devices and their closest replica within acceptable bounds while avoiding overloaded replicas. When facing non-stationary workloads it is essential to dynamically adjust the number and locations of a fog application's replicas. We propose Voilà, a tail-latencyaware auto-scaler integrated in the Kubernetes orchestration system. Voilà maintains a fine-grained view of the volumes of traffic generated from different user locations, and uses simple yet highly-effective procedures to maintain suitable application resources in terms of size and location.

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“…• A. Al-Dulaimy is with the School of Innovation, Design and Engineering, Mälardalen University, Sweden. E-mail: auday.aldulaimy@mdh.se methods and schemes to implement new deployment [5] and performance optimization techniques [6] at various levels: starting from modern container scheduling [7]- [10] methods to predictive request load-balancing [11] and resource auto-scaling algorithms [12], [13].…”

Section: Introductionmentioning

confidence: 99%

PerfSim: A Performance Simulator for Cloud Native Microservice Chains

Khan

Taheri

Al-Dulaimy

et al. 2023

IEEE Trans. Cloud Comput.

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Cloud native computing paradigm allows microservice-based applications to take advantage of cloud infrastructure in a scalable, reusable, and interoperable way. However, in a cloud native system, the vast number of configuration parameters and highly granular resource allocation policies can significantly impact the performance and deployment cost. For understanding and analyzing these implications in an easy, quick, and cost-effective way, we present PerfSim, a discrete-event simulator for approximating and predicting the performance of cloud native service chains in user-defined scenarios. To this end, we proposed a systematic approach for modeling the performance of microservices endpoint functions by collecting and analyzing their performance and network traces. With a combination of the extracted models and user-defined scenarios, PerfSim can then simulate the performance behavior of all services over a given period and provide an approximation for system KPIs, such as requests' average response time. Using the processing power of a single laptop, we evaluated both simulation accuracy and speed of PerfSim in 104 prevalent scenarios and compared the simulation results with the identical deployment in a real Kubernetes cluster. We achieved ∼81-99% simulation accuracy in approximating the average response time of incoming requests and ∼16-1200 times speed-up factor for the simulation.

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Auto-scaling in Kubernetes-Based Fog Computing Platform

Cited by 10 publications

References 11 publications

Horizontal Pod Autoscaling in Kubernetes for Elastic Container Orchestration

Horizontal Pod Autoscaling in Kubernetes for Elastic Container Orchestration

Voilà: Tail-Latency-Aware Fog Application Replicas Autoscaler

PerfSim: A Performance Simulator for Cloud Native Microservice Chains

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