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Cloud autoscaling for HTTP/2 workloads [PDF]
, 2017 loud computing provides cost effective solutions for deploying services and applications. Even though resources can be provisioned on demand, they need to adapt quickly and in a seamless way to the workload intensity and
Maria Carla Calzarossa +7 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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A Simulation-based Comparison between Industrial Autoscaling Solutions and COCOS for Cloud Applications [PDF]
, 2020 Dynamic resource allocation is the mechanism that allows one to change the resources associated with applications at runtime and match their actual needs.
Baresi L., Quattrocchi G.
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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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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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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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An Experimental Performance Evaluation of Autoscaling Policies for Complex Workflows [PDF]
, 2017 Simplifying the task of resource management and scheduling for customers, while still delivering complex Quality-of-Service (QoS), is key to cloud computing.
Ghit, B.I. +34 more
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Improved Q Network Auto-Scaling in Microservice Architecture
Applied Sciences, 2022 Microservice architecture has emerged as a powerful paradigm for cloud computing due to its high efficiency in infrastructure management as well as its capability of largescale user service.
Yeonggwang Kim +3 more
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Model-Driven Autoscaling for Hadoop Clusters [PDF]
2015 IEEE International Conference on Autonomic Computing, 2015 In this paper, we present the design and implementation of a model-driven auto scaling solution for Hadoop clusters. We first develop novel performance models for Hadoop workloads that relate job completion times to various workload and system parameters such as input size and resource allocation.
Anshul Gandhi +3 more
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