Cost-efficient scheduling algorithms based on beetle antennae search for containerized applications in Kubernetes clouds

Li, Hongjian; Shen, Jie; Li, Zheng; Cui, Yuzheng; Mao, Zhi

doi:10.1007/s11227-023-05077-7

Cited by 6 publications

(4 citation statements)

References 27 publications

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“…It can not perform fine-grained storage management according to different business requirements. K8S provides a configuration management function to separate the configuration parameters, and manage and inject them through ConfigMap and Secret objects [10]. Lack of centralized configuration and key management tools The lack of fine-grained access control mechanism can not fine-grained control the access of different roles, which may lead to the risk of configuration and key leakage.…”

Section: Kubernetes Core Capabilitiesmentioning

confidence: 99%

Research and Implementation of Container Based Application Orchestration Service Technology

Liu,

Wang,

Zhao

et al. 2024

J. Phys.: Conf. Ser.

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With the rapid development of cloud computing technology, Kubernetes(K8S), as the main orchestration tool for cloud native applications, has become the preferred choice for enterprises and developers. This article is based on container based application orchestration service technology. Through a set of templates containing cloud resource descriptions, it quickly completes functions such as application creation and configuration, application batch cloning, and application multi environment deployment. It simplifies and automates the lifecycle management capabilities required for cloud applications, such as resource planning, application design, deployment, status monitoring, and scaling. Users can more conveniently complete infrastructure management and operation and maintenance work, In order to focus more on innovation and research and development, and improve work efficiency. The actual application effect of the technology used in this article depends to a certain extent on the ability level of basic service resources, and manual template creation is required for the first use. In production use, a certain professional ability is required to create a good application layout template, adjust and optimize resources according to the production environment, in order to significantly improve the effectiveness and efficiency of practical applications.

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Section: Kubernetes Core Capabilitiesmentioning

confidence: 99%

Research and Implementation of Container Based Application Orchestration Service Technology

Liu,

Wang,

Zhao

et al. 2024

J. Phys.: Conf. Ser.

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“…The authors of articles: [7], [8] present approaches to reduce costs by improving the Kubernetes scheduler. However, their main interest is in cloud computing and cannot be extended over the whole continuum without additional work.…”

Section: Related Workmentioning

confidence: 99%

“…However, their main interest is in cloud computing and cannot be extended over the whole continuum without additional work. Li et al [7] present a meta-heuristic-based scheduler that minimizes the energy costs of CPU, RAM, and network usage in addition to the networking costs of offsite nodes. They also propose a rescheduler to monitor changes in business requirements.…”

Section: Related Workmentioning

confidence: 99%

Cost-Effective Scheduling for Kubernetes in the Edge-to-Cloud Continuum

Rac,

Brorsson

2023

2023 IEEE International Conference on Cloud Engineering (IC2E)

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The edge to data center computing continuum is the aggregation of computing resources located anywhere between the network edge (e.g. close to 5G antennas), and servers in traditional data centers. Kubernetes is the de facto standard for container orchestration. It is very efficient in a data center environment, but it fails to give the same performance when adding edge resources. At the edge, resources are more limited, and networking conditions are changing over time.In this paper, we present a methodology that lowers the costs of running applications in the edge-to-cloud computing continuum. A cost-aware scheduler enables this optimization. We are also monitoring the Key Performance Indicators of the applications to ensure that cost optimizations do not impact negatively their Quality of Service. In addition, to ensure that performances are optimal even when users are moving, we introduce a background process that periodically checks if a better location is available for the application. To demonstrate the performance of our scheduling approach, we evaluate it on a vehicle cooperative perception use case, a representative 5G application.

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“…Several previous studies have discussed Kubernetes Clusters and their benefits. Research conducted by (Li et al, 2023) discusses the use of Kubernetes Clusters as a solution to increase deployment efficiency and flexibility in a cloud environment. In this research, the authors use Kubernetes to manage and deploy applications in several cloud environments, such as AWS, Azure, and Google Cloud Platform.…”

Section: Introductionmentioning

confidence: 99%

Survey Paper: Optimization and Monitoring of Kubernetes Cluster using Various Approaches

Hadikusuma

Lukas

Bachri

2023

SinkrOn

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This research compares different methods for optimizing and monitoring Kubernetes clusters. Three referenced journals are analyzed: "Kubernetes cluster optimization using hybrid shared-state scheduling framework" by Oana-Mihaela Ungureanu, Călin Vlădeanu, Robert Kooij; "Monitoring Kubernetes Clusters Using Prometheus and Grafana" by Salma Rachman Dira, Muhammad Arif Fadhly Ridha; and "Cluster Frameworks for Efficient Scheduling and Resource Allocation in Data Center Networks: A Survey" by Kun Wang, Qihua Zhou, Song Guo, and Jiangtao Luo. These journals explore various approaches to optimizing and monitoring Kubernetes clusters. This review concludes that selecting appropriate technologies for optimizing and monitoring Kubernetes clusters can enhance performance and resource management efficiency in data centre networks. The research addresses the problem of improving Kubernetes cluster performance through optimization and efficient monitoring. The required methods include utilizing hybrid state-sharing scheduling frameworks, implementing Prometheus and Grafana for monitoring, and employing efficient cluster frameworks. The study's findings demonstrate that adopting a hybrid shared-state scheduling framework can improve Kubernetes cluster performance. Additionally, leveraging Prometheus and Grafana as monitoring tools offer valuable insights into cluster health and performance. The survey also reveals various cluster frameworks that enable efficient scheduling and resource allocation in data centre networks. In conclusion, this research emphasizes the significance of employing suitable technologies to optimize and monitor Kubernetes clusters, leading to enhanced performance and efficient resource management in data centre networks. By leveraging appropriate scheduling frameworks and monitoring tools, organizations can optimize their utilization of Kubernetes clusters and ensure efficient resource allocation

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Cost-efficient scheduling algorithms based on beetle antennae search for containerized applications in Kubernetes clouds

Cited by 6 publications

References 27 publications

Research and Implementation of Container Based Application Orchestration Service Technology

Research and Implementation of Container Based Application Orchestration Service Technology

Cost-Effective Scheduling for Kubernetes in the Edge-to-Cloud Continuum

Survey Paper: Optimization and Monitoring of Kubernetes Cluster using Various Approaches

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