Container‐based cluster orchestration systems: A taxonomy and future directions

Summary Containers, enabling lightweight environment and performance isolation, fast and flexible deployment, and fine‐grained resource sharing, have gained popularity in better application management and deployment in addition to hardware virtualization. They are being widely used by organizations...

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Bibliographic Details
Published in:Software, practice & experience Vol. 49; no. 5; pp. 698 - 719
Main Authors: Rodriguez, Maria A., Buyya, Rajkumar
Format: Journal Article
Language:English
Published: Bognor Regis Wiley Subscription Services, Inc 01.05.2019
Subjects:
cluster management
Clusters
container orchestration
container scheduling
Containers
Data centers
Data management
Fault tolerance
Management systems
Resource management
survey
Taxonomy
Web services
ISSN:0038-0644, 1097-024X
Online Access:Get full text
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Summary:Summary Containers, enabling lightweight environment and performance isolation, fast and flexible deployment, and fine‐grained resource sharing, have gained popularity in better application management and deployment in addition to hardware virtualization. They are being widely used by organizations to deploy their increasingly diverse workloads derived from modern‐day applications such as web services, big data, and internet of things in either proprietary clusters or private and public cloud data centers. This has led to the emergence of container orchestration platforms, which are designed to manage the deployment of containerized applications in large‐scale clusters. These systems are capable of running hundreds of thousands of jobs across thousands of machines. To do so efficiently, they must address several important challenges including scalability, fault tolerance and availability, efficient resource utilization, and request throughput maximization among others. This paper studies these management systems and proposes a taxonomy that identifies different mechanisms that can be used to meet the aforementioned challenges. The proposed classification is then applied to various state‐of‐the‐art systems leading to the identification of open research challenges and gaps in the literature intended as future directions for researchers.
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ISSN:0038-0644
1097-024X
DOI:10.1002/spe.2660