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. To deal with this challenge, Kubernetes provides two kinds of scaling mode: vertical and horizontal.
Dinh-Dai Vu +2 more
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An Autoscaling System Based on Predicting the Demand for Resources and Responding to Failure in Forecasting. [PDF]
Sensors (Basel), 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
Park J, Jeong J.
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ScalableDigitalHealth (SDH): An IoT-Based Scalable Framework for Remote Patient Monitoring. [PDF]
Sensors (Basel)Addressing the increasing demand for remote patient monitoring, especially among the elderly and mobility-impaired, this study proposes the “ScalableDigitalHealth” (SDH) framework.
Alasmary H.
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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.
Abeer Abdel Khaleq, Ilkyeun Ra
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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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Traffic-Aware Horizontal Pod Autoscaler in Kubernetes-Based Edge Computing Infrastructure
IEEE Access, 2022 Container-based Internet of Things (IoT) applications in an edge computing environment require autoscaling to dynamically adapt to fluctuations in IoT device requests.
Le Hoang Phuc, Linh-An Phan, Taehong Kim
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EdgeX over Kubernetes: Enabling Container Orchestration in EdgeX
Applied Sciences, 2021 With the exponential growth of the Internet of Things (IoT), edge computing is in the limelight for its ability to quickly and efficiently process numerous data generated by IoT devices.
Seunghwan Lee +4 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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Proactive automatic up-scaling for Kubernetes
Adaptivni Sistemi Avtomatičnogo Upravlinnâ, 2023 Modern challenges in microservice architecture requires autoscaling (upscaling and down-scaling). As an example Black Friday can be considered when load to system increases significantly and obviously more computing capabilities are need to serve it ...
D. Gutman, O. Syrota
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Deep Learning-Based Autoscaling Using Bidirectional Long Short-Term Memory for Kubernetes
Applied Sciences, 2021 Presently, the cloud computing environment attracts many application developers to deploy their web applications on cloud data centers. Kubernetes, a well-known container orchestration for deploying web applications on cloud systems, offers an automatic ...
Nhat-Minh Dang-Quang, Myungsik Yoo
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