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Frontiers in Computer ScienceThe advancement of cloud computing technologies has led to increased usage in application deployment in recent years. Kubernetes, a widely used container orchestration platform for deploying applications on cloud systems, provides benefits such as ...
Pasan Bhanu Guruge
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Intelligent Autoscaling of Microservices in the Cloud for Real-Time Applications
IEEE Access, 2021 Cloud applications are becoming more containerized in nature. Developing a cloud application based on a microservice architecture imposes different challenges including scalability at the container level.
Ilkyeun Ra, Abeer Abdel Khaleq
exaly +3 more sources
An Autoscaling System Based on Predicting the Demand for Resources and Responding to Failure in Forecasting [PDF]
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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ScalableDigitalHealth (SDH): An IoT-Based Scalable Framework for Remote Patient Monitoring [PDF]
SensorsAddressing the increasing demand for remote patient monitoring, especially among the elderly and mobility-impaired, this study proposes the “ScalableDigitalHealth” (SDH) framework.
Hisham Alasmary
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Predictive Hybrid Autoscaling for Containerized Applications
IEEE Access, 2022 One of the main challenges in deploying container service is providing the scalability to satisfy the service performance and avoid resource wastage.
Dinh-Dai Vu +2 more
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EncyclopediaThe deployment of virtual machines (VMs) within the Infrastructure as a Service (IaaS) layer across public, private, or hybrid cloud infrastructures is prevalent in various organisational settings for hosting essential business services.
Kamran Munir, Zaheer Khan
exaly +3 more sources
Proactive Random-Forest Autoscaler for Microservice Resource Allocation
IEEE Access, 2023 Cloud service providers have been shifting their workloads to microservices to take advantage of their modularity, flexibility, agility, and scalability. However, numerous obstacles remain to achieving the most out of microservice deployments, especially
Lamees M. Al Qassem +3 more
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Online Workload Burst Detection for Efficient Predictive Autoscaling of Applications
IEEE Access, 2020 Autoscaling methods are employed to ensure the scalability of cloud-hosted applications. The public-facing applications are prone to receive sudden workload bursts, and the existing autoscaling methods do not handle the bursty workloads gracefully. It is
Fatima Tahir +4 more
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An Efficient Multivariate Autoscaling Framework Using Bi-LSTM for Cloud Computing
Applied Sciences, 2022 With the rapid development of 5G technology, the need for a flexible and scalable real-time system for data processing has become increasingly important.
Nhat-Minh Dang-Quang, Myungsik Yoo
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Toward Optimal Load Prediction and Customizable Autoscaling Scheme for Kubernetes
Mathematics, 2023 Most enterprise customers now choose to divide a large monolithic service into large numbers of loosely-coupled, specialized microservices, which can be developed and deployed separately.
Subrota Kumar Mondal +6 more
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