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Vertical Pod Autoscaling in Kubernetes for Elastic Container Collaborative Framework 被引量:1
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作者 Mushtaq Niazi Sagheer Abbas +3 位作者 Abdel-Hamid Soliman Tahir Alyas Shazia Asif Tauqeer Faiz 《Computers, Materials & Continua》 SCIE EI 2023年第1期591-606,共16页
Kubernetes is an open-source container management tool which automates container deployment,container load balancing and container(de)scaling,including Horizontal Pod Autoscaler(HPA),Vertical Pod Autoscaler(VPA).HPA e... Kubernetes is an open-source container management tool which automates container deployment,container load balancing and container(de)scaling,including Horizontal Pod Autoscaler(HPA),Vertical Pod Autoscaler(VPA).HPA enables flawless operation,interactively scaling the number of resource units,or pods,without downtime.Default Resource Metrics,such as CPU and memory use of host machines and pods,are monitored by Kubernetes.Cloud Computing has emerged as a platform for individuals beside the corporate sector.It provides cost-effective infrastructure,platform and software services in a shared environment.On the other hand,the emergence of industry 4.0 brought new challenges for the adaptability and infusion of cloud computing.As the global work environment is adapting constituents of industry 4.0 in terms of robotics,artificial intelligence and IoT devices,it is becoming eminent that one emerging challenge is collaborative schematics.Provision of such autonomous mechanism that can develop,manage and operationalize digital resources like CoBots to perform tasks in a distributed and collaborative cloud environment for optimized utilization of resources,ensuring schedule completion.Collaborative schematics are also linked with Bigdata management produced by large scale industry 4.0 setups.Different use cases and simulation results showed a significant improvement in Pod CPU utilization,latency,and throughput over Kubernetes environment. 展开更多
关键词 Autoscaling query optimization PODS kubernetes CONTAINER ORCHESTRATION
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Innovative Fungal Disease Diagnosis System Using Convolutional Neural Network
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作者 Tahir Alyas Khalid Alissa +3 位作者 Abdul Salam Mohammad Shazia Asif Tauqeer Faiz Gulzar Ahmed 《Computers, Materials & Continua》 SCIE EI 2022年第12期4869-4883,共15页
Fungal disease affects more than a billion people worldwide,resulting in different types of fungus diseases facing life-threatening infections.The outer layer of your body is called the integumentary system.Your skin,... Fungal disease affects more than a billion people worldwide,resulting in different types of fungus diseases facing life-threatening infections.The outer layer of your body is called the integumentary system.Your skin,hair,nails,and glands are all part of it.These organs and tissues serve as your first line of defence against bacteria while protecting you from harm and the sun.The It serves as a barrier between the outside world and the regulated environment inside our bodies and a regulating effect.Heat,light,damage,and illness are all protected by it.Fungi-caused infections are found in almost every part of the natural world.When an invasive fungus takes over a body region and overwhelms the immune system,it causes fungal infections in people.Another primary goal of this study was to create a Convolutional Neural Network(CNN)-based technique for detecting and classifying various types of fungal diseases.There are numerous fungal illnesses,but only two have been identified and classified using the proposed Innovative Fungal Disease Diagnosis(IFDD)system of Candidiasis and Tinea Infections.This paper aims to detect infected skin issues and provide treatment recommendations based on proposed system findings.To identify and categorize fungal infections,deep machine learning techniques are utilized.A CNN architecture was created,and it produced a promising outcome to improve the proposed system accuracy.The collected findings demonstrated that CNN might be used to identify and classify numerous species of fungal spores early and estimate all conceivable fungus hazards.Our CNN-Based can detect fungal diseases through medical images;earmarked IFDD system has a predictive performance of 99.6%accuracy. 展开更多
关键词 Deep machine learning CNN ReLU skin disease FUNGAL
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