Design of Kubernetes Scheduling Strategy Based on LSTM and Grey Model

Yang, Ying Jie; Chen, Lijun

doi:10.1109/iske47853.2019.9170419

Cited by 11 publications

(6 citation statements)

References 2 publications

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“…Y. Yang et al [11] proposed a container scheduling strategy (hereafter referred to as GLP-S) based on a predictive model that consisted of a gray model (GM) and a long short-term memory (LSTM) model to reduce the resource fragmentation that occurs when container scheduling is performed. GLP-S predicted future resource usage by analyzing the patterns of past usage with GM(1,1) and LSTM models and then rescheduled to minimize idle resources.…”

Section: Related Workmentioning

confidence: 99%

“…It scores nodes based on resource usage and then selects nodes with resource utilization below a certain threshold to deploy containers. For the deep learning-based container scheduling technique, we selected GLP-S [11]. In addition, this study used the resource consumer image of Kubernetes to create workloads for three types of datasets [32].…”

Section: Perfermance Evaluationmentioning

confidence: 99%

“…Various studies on container scheduling utilize artificial intelligence (AI) to predict future workloads in order to maximize resource utilization by appropriately allocating initial resources [11,12]. Although these studies minimize idle resources by utilizing predicted resource usage, they fail to accurately predict certain workloads that include irregular noise.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Efficient Container Scheduling With Hybrid Deep Learning Model for Improved Service Reliability in Cloud Computing

Jeon,

Park,

Jeong

et al. 2024

IEEE Access

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In a cloud computing environment, the container scheduling technique ensures reliability for containerized applications by selecting nodes that satisfy various resource requirements and then deploying containers. If the initial resources of a container are over-allocated, resources may be wasted, or other containers that are waiting in a scheduling queue may not be allocated. However, if resources are underallocated, service disruptions may occur due to node overbooking, and service reliability cannot be ensured. Therefore, in this study, a forecasted resource-evaluating scheduler (FoRES) is proposed as a container scheduling technique that ensures resource efficiency and service reliability. FoRES predicts future CPU and memory usage by using a time-series decomposition-based hybrid forecasting (DeHyFo) model that combines multiple linear regressions with the LightTS model. FoRES then calculates the optimal scheduling decisions that minimize idle resources and node overload by applying an efficient resource utilization (SERU) scoring function to the predicted resource usage. Evaluating the performance of FoRES based on various scenarios improved resource efficiency and service reliability by up to 2.07 and 2.32 times, respectively, compared with existing scheduling techniques, even if the initial resources of the container were inefficiently allocated.INDEX TERMS Cloud computing, container scheduling, deep learning, resource efficiency, service reliability, time-series forecasting.

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Section: Related Workmentioning

confidence: 99%

Section: Perfermance Evaluationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Efficient Container Scheduling With Hybrid Deep Learning Model for Improved Service Reliability in Cloud Computing

Jeon,

Park,

Jeong

et al. 2024

IEEE Access

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“…In Yang et al [56], the authors propose a method for optimizing Kubernetes' container scheduling algorithm by combining the grey system theory with the LSTM (Long Short-Term Memory) neural network prediction method. They perform experiments to evaluate their approach and find that it can reduce the resource fragmentation problem of working nodes in the cluster and increase the utilization of cluster resources.…”

Section: Ai Focused Schedulingmentioning

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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“…The paper [1] presented a way of improving the scheduling algorithm of the container by integrating grey system theory with the Long Short-Term Memory (LSTM) neural network prediction approach by assessing the overall design and scheduling strategy of Kubernetes. Based on the results of the experiments, the paper concludes that the method may decrease the fragmentation problem of resources of the cluster's working nodes and boost the use of cluster resources.…”

Section: Literature Surveymentioning

confidence: 99%

Deployment of Applications Using Nginx Ingress Controller

Gautham¹,

Abhijit²,

B³

2021

JUSST

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Cloud computing is a method of storing and manipulating data by utilizing a network of remote servers. Cloud computing is becoming increasingly popular owing to its large storage capacity, ease of access, and wide range of services. Virtualization entered the picture when cloud computing progressed, and technologies or software such as virtual machines emerged. However, when customers’ computational needs for storage and servers rose, virtual machines were unable to meet those expectations owing to scalability and resource allocation limitations. As a result, containerization came into the picture. Containerization refers to the packaging of software code together with all of its necessary elements such as frameworks, libraries, and other dependencies such that they are isolated or segregated in their own container. Kubernetes used as an orchestration tool implements an ingress controller to route external traffic to deployments running on pods via ingress resource. This enables effective traffic management among the running applications avoiding unwanted blackouts in the production environment.

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Design of Kubernetes Scheduling Strategy Based on LSTM and Grey Model

Cited by 11 publications

References 2 publications

Efficient Container Scheduling With Hybrid Deep Learning Model for Improved Service Reliability in Cloud Computing

Efficient Container Scheduling With Hybrid Deep Learning Model for Improved Service Reliability in Cloud Computing

A survey of Kubernetes scheduling algorithms

Deployment of Applications Using Nginx Ingress Controller

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