A survey of Kubernetes scheduling algorithms

As cloud services expand, the need to improve the performance of data center infrastructure becomes more important. High-performance computing, advanced networking solutions, and resource optimization strategies can help data centers maintain the speed and efficiency necessary to provide high-qualit...

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Published in:Journal of cloud computing : advances, systems and applications Vol. 12; no. 1; pp. 87 - 26
Main Authors: Senjab, Khaldoun, Abbas, Sohail, Ahmed, Naveed, Khan, Atta ur Rehman
Format: Journal Article
Language:English
Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.12.2023
Springer Nature B.V
SpringerOpen
Subjects:
Algorithms
Algorithms for Secure Smart Services
Cloud computing
Cloud services
Clusters
Computer Communication Networks
Computer Science
Computer System Implementation
Computer Systems Organization and Communication Networks
Container orchestration
Containerized applications
Containers
Data center infrastructure
Data centers
Information Systems Applications (incl.Internet)
Intelligence in the Edge-Cloud: Theories
Kubernetes
Modelling
Multiple objective analysis
Nodes
Optimization
Performance enhancement
Resource optimization
Resource utilization
Review
Scheduling
Software Engineering/Programming and Operating Systems
Special Purpose and Application-Based Systems
Resource optimization
Data center infrastructure
Container orchestration
Cloud services
Containerized applications
Scheduling algorithm
Kubernetes
ISSN:2192-113X, 2192-113X
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Abstract As cloud services expand, the need to improve the performance of data center infrastructure becomes more important. High-performance computing, advanced networking solutions, and resource optimization strategies can help data centers maintain the speed and efficiency necessary to provide high-quality cloud services. Running containerized applications is one such optimization strategy, offering benefits such as improved portability, enhanced security, better resource utilization, faster deployment and scaling, and improved integration and interoperability. These benefits can help organizations improve their application deployment and management, enabling them to respond more quickly and effectively to dynamic business needs. Kubernetes is a container orchestration system designed to automate the deployment, scaling, and management of containerized applications. One of its key features is the ability to schedule the deployment and execution of containers across a cluster of nodes using a scheduling algorithm. This algorithm determines the best placement of containers on the available nodes in the cluster. In this paper, we provide a comprehensive review of various scheduling algorithms in the context of Kubernetes. We characterize and group them into four sub-categories: generic scheduling, multi-objective optimization-based scheduling, AI-focused scheduling, and autoscaling enabled scheduling, and identify gaps and issues that require further research.
AbstractList As cloud services expand, the need to improve the performance of data center infrastructure becomes more important. High-performance computing, advanced networking solutions, and resource optimization strategies can help data centers maintain the speed and efficiency necessary to provide high-quality cloud services. Running containerized applications is one such optimization strategy, offering benefits such as improved portability, enhanced security, better resource utilization, faster deployment and scaling, and improved integration and interoperability. These benefits can help organizations improve their application deployment and management, enabling them to respond more quickly and effectively to dynamic business needs. Kubernetes is a container orchestration system designed to automate the deployment, scaling, and management of containerized applications. One of its key features is the ability to schedule the deployment and execution of containers across a cluster of nodes using a scheduling algorithm. This algorithm determines the best placement of containers on the available nodes in the cluster. In this paper, we provide a comprehensive review of various scheduling algorithms in the context of Kubernetes. We characterize and group them into four sub-categories: generic scheduling, multi-objective optimization-based scheduling, AI-focused scheduling, and autoscaling enabled scheduling, and identify gaps and issues that require further research.
Abstract As cloud services expand, the need to improve the performance of data center infrastructure becomes more important. High-performance computing, advanced networking solutions, and resource optimization strategies can help data centers maintain the speed and efficiency necessary to provide high-quality cloud services. Running containerized applications is one such optimization strategy, offering benefits such as improved portability, enhanced security, better resource utilization, faster deployment and scaling, and improved integration and interoperability. These benefits can help organizations improve their application deployment and management, enabling them to respond more quickly and effectively to dynamic business needs. Kubernetes is a container orchestration system designed to automate the deployment, scaling, and management of containerized applications. One of its key features is the ability to schedule the deployment and execution of containers across a cluster of nodes using a scheduling algorithm. This algorithm determines the best placement of containers on the available nodes in the cluster. In this paper, we provide a comprehensive review of various scheduling algorithms in the context of Kubernetes. We characterize and group them into four sub-categories: generic scheduling, multi-objective optimization-based scheduling, AI-focused scheduling, and autoscaling enabled scheduling, and identify gaps and issues that require further research.
ArticleNumber 87
Author Abbas, Sohail
Ahmed, Naveed
Khan, Atta ur Rehman
Senjab, Khaldoun
Author_xml – sequence: 1
  givenname: Khaldoun
  surname: Senjab
  fullname: Senjab, Khaldoun
  organization: Department of Computer Science, College of Computing and Informatics, University of Sharjah
– sequence: 2
  givenname: Sohail
  surname: Abbas
  fullname: Abbas, Sohail
  email: sabbas@sharjah.ac.ae
  organization: Department of Computer Science, College of Computing and Informatics, University of Sharjah
– sequence: 3
  givenname: Naveed
  surname: Ahmed
  fullname: Ahmed, Naveed
  organization: Department of Computer Science, College of Computing and Informatics, University of Sharjah
– sequence: 4
  givenname: Atta ur Rehman
  surname: Khan
  fullname: Khan, Atta ur Rehman
  organization: College of Engineering and Information Technology, Ajman University
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Copyright The Author(s) 2023. corrected publication 2023
The Author(s) 2023. corrected publication 2023. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.
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Keywords Resource optimization
Data center infrastructure
Container orchestration
Cloud services
Containerized applications
Scheduling algorithm
Kubernetes
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Snippet As cloud services expand, the need to improve the performance of data center infrastructure becomes more important. High-performance computing, advanced...
Abstract As cloud services expand, the need to improve the performance of data center infrastructure becomes more important. High-performance computing,...
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SubjectTerms Algorithms
Algorithms for Secure Smart Services
Cloud computing
Cloud services
Clusters
Computer Communication Networks
Computer Science
Computer System Implementation
Computer Systems Organization and Communication Networks
Container orchestration
Containerized applications
Containers
Data center infrastructure
Data centers
Information Systems Applications (incl.Internet)
Intelligence in the Edge-Cloud: Theories
Kubernetes
Modelling
Multiple objective analysis
Nodes
Optimization
Performance enhancement
Resource optimization
Resource utilization
Review
Scheduling
Software Engineering/Programming and Operating Systems
Special Purpose and Application-Based Systems
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Title A survey of Kubernetes scheduling algorithms
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Volume 12
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