KubCG: A dynamic Kubernetes scheduler for heterogeneous clusters

Ahmed, Ghofrane El Haj; Gil‐Castiñeira, Felipe; Costa-Montenegro, Enrique

doi:10.1002/spe.2898

Cited by 12 publications

(3 citation statements)

References 22 publications

(29 reference statements)

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“…They introduced a machine learning-based approach using runtime prediction to better select appropriate cpu or gpu resources. Similarly, in the paper [21], a Kubernetes scheduling platform (KubCG) was proposed, which manages the deployment of Docker containers in heterogeneous clusters. The platform implements a new scheduler that reduces the completion time for different tasks to 64% of the original time.…”

Section: Related Workmentioning

confidence: 99%

K-TAHP: A Kubernetes Load Balancing Strategy Base on TOPSIS+AHP

Gao,

Xie,

Guo

2023

IEEE Access

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Kubernetes is an orchestration platform designed for containerized applications,allows the application provider to scale automatically to match the flfluctuating intensity of processing demand. Container cluster technology is used to encapsulate, isolate, and deploy applications, addressing the issue of low system reliability due to interlocking failures.However, after running for a long time, Kubernetes clusters often suffer from uneven system load, leading to a performance decline. To address this issue, a load balancing strategy called K-TAHP, based on TOPSIS and AHP , is proposed. This strategy takes cpu, memory, and bandwidth usage as load factors and uses K-TAHP to construct load evaluation. By employing a warning module and a migration module, it migrates high-load pods from overloaded nodes to nodes with lower loads, thus improving load balancing in the Kubernetes cluster and resolving performance degradation caused by load imbalance after prolonged cluster operation. Experimental results demonstrate that the K-TAHP load balancing strategy effectively enhances load balancing in Kubernetes clusters, addresses load imbalance issues after long-term operation, and ensures uninterrupted pod services during migration, thereby maintaining cluster performance.

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Section: Related Workmentioning

confidence: 99%

K-TAHP: A Kubernetes Load Balancing Strategy Base on TOPSIS+AHP

Gao,

Xie,

Guo

2023

IEEE Access

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“…Another emerging aspect is that of energy efficiency [13,14], a strategy applied by a specific set of schedulers among the wider group of topology-aware [15] and hardware-aware schedulers. Examples of the latter are a GPU-aware scheduler making use of historic pod executions to speed up calculations [16] and an Intel SGX-aware scheduler [17]. Another crucial aspect focuses on the real-time utilization of node resources to schedule workloads [18].…”

Section: Related Workmentioning

confidence: 99%

Enabling the Rescheduling of Containerized Workloads in an Ad Hoc Cross-Organizational Collaboration

et al. 2022

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A group of organizations wishing to collaborate urgently, for example in case of a crisis, need to have a way to quickly deploy applications which enable them to speed up a potentially crisis-resolving decision-making process. A cross-organizational Kubernetes cluster, which is orchestrated by a central operator, allows to initiate these deployments in an ad hoc way. Performance issues may however arise at runtime, for example, a video pipeline belonging to a CCTV camera may produce a too low number of frames per second. The ad hoc cross-organizational collaboration case is especially prone to such issues as the set of candidate nodes and the environment in which they run may not be fully known to the operator. This article therefore motivates and describes the usage of a probe swarm architecture, which allows the operator to quickly generate an overview of the resource capabilities of a set of nodes, by executing code fragments locally. The obtained measurements can then enable the operator to decide on rescheduling operations. Evaluation of an illustrative probe swarm intervention shows that the performance of an example application could improve with factor five, ten or hundred when the pod would be rescheduled. This indicates that the proposed probe swarm architecture may complement other performance bottleneck detection techniques to improve performance of applications that need to be deployed urgently.

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“…In Ahmed et al [28], the deployment of Docker containers in a heterogeneous cluster with CPU and GPU resources can be managed via the authors' dynamic scheduling framework for Kubernetes. The Kubernetes Pod timeline and previous data about the execution of the containers are taken into account by the platform, known as KubCG, to optimize the deployment of new containers.…”

mentioning

confidence: 99%

A survey of Kubernetes scheduling algorithms

et al. 2023

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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.

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KubCG: A dynamic Kubernetes scheduler for heterogeneous clusters

Cited by 12 publications

References 22 publications

K-TAHP: A Kubernetes Load Balancing Strategy Base on TOPSIS+AHP

K-TAHP: A Kubernetes Load Balancing Strategy Base on TOPSIS+AHP

Enabling the Rescheduling of Containerized Workloads in an Ad Hoc Cross-Organizational Collaboration

A survey of Kubernetes scheduling algorithms

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