Adaptive application scheduling under interference in Kubernetes

Medel, Víctor; Rana, Omer F.; Bañares, José Ángel; Arronategui, Unai

doi:10.1145/2996890.3007889

Cited by 19 publications

(10 citation statements)

References 4 publications

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“…Based on these data, it automatically analyzes and scales the application according to the resource provisioning strategy. The paper [ 13 ] presented a Reference Net-based performance and management model for Kubernetes. The goal is to identify the effects of the different interference sources (e.g., CPU usage and network usage) on the applications; therefore, the developer can consider such interference sources and improve the application’s performance.…”

Section: Related Workmentioning

confidence: 99%

Balanced Leader Distribution Algorithm in Kubernetes Clusters

Nguyen

Kim

2021

Sensors

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Container-based virtualization is becoming a de facto way to build and deploy applications because of its simplicity and convenience. Kubernetes is a well-known open-source project that provides an orchestration platform for containerized applications. An application in Kubernetes can contain multiple replicas to achieve high scalability and availability. Stateless applications have no requirement for persistent storage; however, stateful applications require persistent storage for each replica. Therefore, stateful applications usually require a strong consistency of data among replicas. To achieve this, the application often relies on a leader, which is responsible for maintaining consistency and coordinating tasks among replicas. This leads to a problem that the leader often has heavy loads due to its inherent design. In a Kubernetes cluster, having the leaders of multiple applications concentrated in a specific node may become a bottleneck within the system. In this paper, we propose a leader election algorithm that overcomes the bottleneck problem by evenly distributing the leaders throughout nodes in the cluster. We also conduct experiments to prove the correctness and effectiveness of our leader election algorithm compared with a default algorithm in Kubernetes.

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Section: Related Workmentioning

confidence: 99%

Balanced Leader Distribution Algorithm in Kubernetes Clusters

Nguyen

Kim

2021

Sensors

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“…Its flexibility and openness puts it in stark contrast to other conceptually similar approaches, such as [16]. Further, in regards to the numerous Fog/Edge emulators, see Section 4.14.1 in [26], cloud simulators such as CloudSim [4], or direct modeling of Kubernetes-based platforms, such as [17,18], the sandbox enables researchers to utilize the entire cluster software stack. We believe that due to the rapid pace of development in cloud orchestration tools such as Kubernetes and Istio, a practical sandbox that lets researchers deploy the actual software and not models thereof, have a greater impact and thus provide more valuable future insights.…”

Section: Related Workmentioning

confidence: 99%

FedApp: a Research Sandbox for Application Orchestration in Federated Clouds using OpenStack

Ruuskanen,

Peng,

Åkesson

et al. 2021

Preprint

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Multi-cluster federation is envisioned to be the next-generation cloud infrastructure, where it will play a vital part in the realization of concepts such as edge and fog computing. Orchestrating applications in federated environments poses new challenges to well-known research problems in various fields, such as load-balancing, auto-scaling, resource allocation and service migration. However, as access to real multi-cluster infrastructure is limited, a test-bed that provides similar characteristics to a real system is in demand. To enable researchers in associated fields to quickly setup experiments in a federated cloud environment, we have created the open-source sandbox FedApp that simplifies the process of deploying multiple virtual clusters in an Open-Stack environment with the possibility of adding realistic network characteristics between sites. Each cluster comes deployed with the open-source and production-grade container orchestrator Kubernetes, complete with federationwide monitoring using Prometheus/Grafana and simplified inter-cluster microservice communication using Istio.

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“…In addition, our target domain is Kubernetes specifically. This particular domain appears to be less explored in the literature than that of general cloud environments, with simulation-based studies [41][42][43] being the most highly cited works in recent years.…”

Section: Related Workmentioning

confidence: 99%

Impact of etcd deployment on Kubernetes, Istio, and application performance

et al. 2020

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Summary This experience article describes lessons learned as we conducted experiments in a Kubernetes‐based environment, the most notable of which was that the performance of both the Kubernetes control plane and the deployed application depends strongly and in unexpected ways on the performance of the etcd database. The article contains (a) detailed descriptions of how networking with and without Istio works in Kubernetes, based on the Flannel Container Networking Interface (CNI) provider in VXLAN mode with IP Virtual Server (IPVS)‐backed Kubernetes Services, (b) a comprehensive discussion about how to conduct load and performance testing using a closed‐loop workload generator, and (c) an open source experiment framework useful for executing experiments in a shared cloud environment and exploring the resulting data. It also shows that statistical analysis may reveal the data resulting from such experiments to be misleading even when careful preparations are made, and that nondeterministic behavior stemming from etcd can affect both the platform as a whole and the deployed application. Finally, it is demonstrated that using high‐performance backing storage for etcd can reduce the occurrence of such nondeterministic behaviors by a statistically significant (P < .05) margin. The implication of this experience article is that systems researchers studying the performance of applications deployed on Kubernetes cannot simply consider their specific application to be under test. Instead, the particularities of the underlying Kubernetes and cloud platform must be taken into account, in particular because their performance can impact that of etcd.

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Adaptive application scheduling under interference in Kubernetes

Cited by 19 publications

References 4 publications

Balanced Leader Distribution Algorithm in Kubernetes Clusters

Balanced Leader Distribution Algorithm in Kubernetes Clusters

FedApp: a Research Sandbox for Application Orchestration in Federated Clouds using OpenStack

Impact of etcd deployment on Kubernetes, Istio, and application performance

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