Alberto Gómez-González, Carmen Carrión, M. Blanca Caminero
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This paper presents Chronos <em>(Customizable Heuristic Resource Orchestrator for Node Optimization Scheduling)</em>, a customizable scheduling framework for Kubernetes designed to overcome existing architectural limitations. The framework features a programmable scheduling operator with advanced monitoring capabilities, enabling system administrators to adapt scheduling policies to application-specific needs. It operates by harnessing real-time telemetry and time-series performance data. Chronos was deployed on a realistic SBC testbed with representative services and synthetic workloads simulating user behavior. Experimental results show that Chronos improves performance and resource utilization over baseline scheduling algorithms. In particular, when compared to the default Kubernetes scheduler, Chronos customized scheduler was able to reduce network latency up to 23 % for network-intensive workloads, disk write operations up to 42 % for disk-intensive workloads, and response time up to 20 % for CPU-intensive workloads, while maintaining low overhead.</div></div>","PeriodicalId":55132,"journal":{"name":"Future Generation Computer Systems-The International Journal of Escience","volume":"176 ","pages":"Article 108195"},"PeriodicalIF":6.2000,"publicationDate":"2025-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Enhancing fog IoT container deployment: A customizable Kubernetes scheduler\",\"authors\":\"Alberto Gómez-González, Carmen Carrión, M. 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The framework features a programmable scheduling operator with advanced monitoring capabilities, enabling system administrators to adapt scheduling policies to application-specific needs. It operates by harnessing real-time telemetry and time-series performance data. Chronos was deployed on a realistic SBC testbed with representative services and synthetic workloads simulating user behavior. Experimental results show that Chronos improves performance and resource utilization over baseline scheduling algorithms. 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Enhancing fog IoT container deployment: A customizable Kubernetes scheduler
In an era where the Internet of Things (IoT) is becoming integral to daily life, the demand for efficient computing solutions is rapidly increasing, and the orchestration of containers in fog computing environments has emerged as a critical area of research. Single-Board Computers (SBCs) are particularly well-suited to fog computing environments due to their low cost, energy efficiency, and local processing capabilities. However, the efficient orchestration of containerized applications on these resource-constrained machines at the cost of performance is still an open issue. This paper presents Chronos (Customizable Heuristic Resource Orchestrator for Node Optimization Scheduling), a customizable scheduling framework for Kubernetes designed to overcome existing architectural limitations. The framework features a programmable scheduling operator with advanced monitoring capabilities, enabling system administrators to adapt scheduling policies to application-specific needs. It operates by harnessing real-time telemetry and time-series performance data. Chronos was deployed on a realistic SBC testbed with representative services and synthetic workloads simulating user behavior. Experimental results show that Chronos improves performance and resource utilization over baseline scheduling algorithms. In particular, when compared to the default Kubernetes scheduler, Chronos customized scheduler was able to reduce network latency up to 23 % for network-intensive workloads, disk write operations up to 42 % for disk-intensive workloads, and response time up to 20 % for CPU-intensive workloads, while maintaining low overhead.
期刊介绍:
Computing infrastructures and systems are constantly evolving, resulting in increasingly complex and collaborative scientific applications. To cope with these advancements, there is a growing need for collaborative tools that can effectively map, control, and execute these applications.
Furthermore, with the explosion of Big Data, there is a requirement for innovative methods and infrastructures to collect, analyze, and derive meaningful insights from the vast amount of data generated. This necessitates the integration of computational and storage capabilities, databases, sensors, and human collaboration.
Future Generation Computer Systems aims to pioneer advancements in distributed systems, collaborative environments, high-performance computing, and Big Data analytics. It strives to stay at the forefront of developments in grids, clouds, and the Internet of Things (IoT) to effectively address the challenges posed by these wide-area, fully distributed sensing and computing systems.