Latency-Aware Scheduling for Kubernetes: A Custom Approach for Cloud Environments

Painuly, Himanshi (2024) Latency-Aware Scheduling for Kubernetes: A Custom Approach for Cloud Environments. Masters thesis, Dublin, National College of Ireland.

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Abstract

Cloud computing has made the microservices architecture a popular choice as it scales and offers flexibility, but maintaining these interconnected components efficiently is difficult. Current scheduling strategies in system such as Kubernetes lead to poor performance since they do not properly handle microservice dependencies leading to higher resource consumption and latency. In this research the focus is on these inefficiencies, which are crucial for organisations that are aiming to optimize network latency and reduce operational costs. Conventional Kubernetes schedulers often fails to look the dynamic nature of network performance, focusing primarily on static resource constraints like CPU and memory. Testing the Latency-Aware Scheduler(LAS) in this research introduces a custom latency-aware scheduler(LAS) that integrates real-time network latency measurements into the scheduling process when it is compared to the default Kubernetes scheduler. This custom scheduler calculates latency and available pods using custom node affinity rules, with the goal of minimising network delay by placing dependent pods on the same node. By modifying node affinity rules, incorporating a unique node scoring function, and converting network latency measurements into scheduling decisions it prioritises low network latency for maximum performance and user experience in latency-sensitive applications. Real-world testing in various cloud environments will reveal scalability and flexibility. This is essential for large-scale deployments.

Item Type:	Thesis (Masters)
Supervisors:	Name Email Kumar Sharma, Jitendra UNSPECIFIED
Uncontrolled Keywords:	Kuberentes; Scheduling policies; Network Latency; AWS; Latency Aware Scheduler
Subjects:	Q Science > QA Mathematics > Electronic computers. Computer science T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science T Technology > T Technology (General) > Information Technology > Cloud computing
Divisions:	School of Computing > Master of Science in Cloud Computing
Depositing User:	Ciara O'Brien
Date Deposited:	04 Jul 2025 09:31
Last Modified:	04 Jul 2025 09:31
URI:	https://norma.ncirl.ie/id/eprint/8045

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