Results 21 to 30 of about 1,278 (112)
Sensors, 2023 In recent years, the convergence of edge computing and sensor technologies has become a pivotal frontier revolutionizing real-time data processing. In particular, the practice of data acquisition—which encompasses the collection of sensory information in
Jieun Park, Junho Jeong
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Optimized Autoscaling of Cloud Native Applications
, 2021 Software containers are changing the way distributed applications are executedand managed on cloud computing resources. Autoscaling allows containerizedapplications and services to run resiliently with high availability without the demandof user ...
Åsberg, Niklas
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Self-Adaptive Data Processing to Improve SLOs for Dynamic IoT Workloads
Computers, 2020 Internet of Things (IoT) covers scenarios of cyber−physical interaction of smart devices with humans and the environment and, such as applications in smart city, smart manufacturing, predictive maintenance, and smart home. Traditional scenarios are
Peeranut Chindanonda +2 more
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Methods of autoscaling and scheduling in Cloud Computing [PDF]
, 2023 Структура дипломної роботи: Загальний об’єм пояснювальної записки: 96 сторінок, 48 рисунків, 6 таблиць, 32 посилання, 9 додаток. Актуальність теми. Найбільш популярною перевагою у використанні автоматичного масштабування є потенційно велика економія ...
Тринус, Нікіта Вячеславович
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The new ionospheric station of Tucumán: first results
Annals of Geophysics, 2007 An Advanced Ionospheric Sounder, built at the Istituto Nazionale di Geofisica e Vulcanologia, Rome, Italy, was installed at Tucumán, Argentina, particularly interesting for its location, near the southern peak of the ionospheric equatorial ...
M. A. Cabrera +6 more
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Experimentally Evaluating the Resource Efficiency of Big Data Autoscaling [PDF]
Distributed dataflow systems like Spark and Flink enable data-parallel processing of large datasets on clusters. Yet, selecting appropriate computational resources for dataflow jobs is often challenging.
Thamsen, Lauritz +3 more
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IEEE AccessAutoscaling in Kubernetes is typically driven by infrastructure-level signals such as CPU utilization or external event triggers. While these approaches work well in many cases, they often fail to reflect application-level service pressure and business ...
Pallavi Priya Patharlagadda
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ATOM: Model-driven autoscaling for microservices
, 2019 Microservices based architectures are increasingly widespread in the cloud software industry. Still, there is a shortage of auto-scaling methods designed to leverage the unique features of these architectures, such as the ability to independently scale a
Woodside, M., Casale, G., Gias, A.U.
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Performance trade-offs between predictive and reactive autoscaling in stateful microservices
Machinery & EnergeticsThe relevance of studying the trade-offs between reactive and predictive autoscaling of stateful microservices was due to the need to improve stability, resource performance and reliability of cloud systems in dynamic conditions. The aim of the study was
Kh. Terletska
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Dynamic Autoscaling and Scheduling in Kubernetes Clusters with LSTM and ILP
Journal of Communications Software and SystemsContainerized applications provide benefits such as portability, security, and faster deployment, enabling organizations to adapt swiftly to dynamic business needs.
Somashekar Patil +2 more
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