Container Scheduling in Co-Located Environments Using QoE Awareness

Carvalho, Marcos; Macedo, Daniel F.

doi:10.1109/tnsm.2023.3244090

Cited by 4 publications

(2 citation statements)

References 48 publications

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“…Carvalho et al [15] proposed a quality of experience (QoE)-aware container scheduler to solve the problem of the reduced user QoE that results from the interdependencies between multiple applications. The proposed scheduler analyzed the Spearman correlation between the QoE and various metrics extracted by the application to extract metrics that had a high impact on QoE.…”

Section: Related Workmentioning

confidence: 99%

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%

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 Carvalho et al [39], The authors offer an addition to the Kubernetes scheduler that uses Quality of Experience (QoE) measurements to help cloud management Service Level Objectives (SLOs) be more accurate. In the context of video streaming services that are co-located with other services, the authors assess the suggested architecture using the QoE metric from the ITU P.1203 standard.…”

Section: Scheduling Using Multi-objective Optimizationmentioning

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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Energy-aware Scheduling Algorithm for Microservices in Kubernetes Clouds

Rao,

2024

J Grid Computing

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Container Scheduling in Co-Located Environments Using QoE Awareness

Cited by 4 publications

References 48 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

Energy-aware Scheduling Algorithm for Microservices in Kubernetes Clouds

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