Diagnosing Alzheimer's disease has a major challenge for neurologists due to the time-consuming and sometimes imprecise manual methods. Given Alzheimer's substantial impact on the brain, employing an automatic...
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Depression is the mental health condition that affects many people around the world. In order to find and treat depression and other mental health conditions, it is important to find those who are at risk so that they...
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Many deep learning models are capable of performing various image segmentation tasks with excellent performance on well-annotated datasets. 2D panoramic dental image segmentation, as a specific task within the field o...
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This study addresses the challenges of real-time data synchronization and big data processing in the construction of digital twin workshops under the background of intelligent manufacturing. A solution that integrates...
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In recent years, with the rapid development of computer and network technology, people have become increasingly concerned about health. In order to ensure health and human health, more and more people have begun to pa...
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In dynamic software development, ensuring high reliability and quality is an important goal. The biggest challenge in this field is the early detection and correction of errors, which if not addressed in time can lead...
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Accurate segmentation of brain tumors is crucial for diagnosis and treatment planning in neuroimaging. Using labeled and unlabeled data and leveraging semi-supervised learning approaches can enhance segmentation perfo...
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Abnormal reproduction of cancerous cell growths in the lungs leads to highly fatal lung cancer. Early detection is essential for reducing death rates of lung cancer. New research has shown that machine learning algori...
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Distributed Denial of Service (DDoS) attacks are a critical threat to the security and reliability of Software-Defined Networking (SDN) environments. Existing datasets for training machine learning (ML) models, such a...
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Autoscaling functions provide the foundation for achieving elasticity in the modern cloud computing paradigm. It enables dynamic provisioning or de-provisioning resources for cloud software services and applications w...
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ISBN:
(纸本)9798350329964
Autoscaling functions provide the foundation for achieving elasticity in the modern cloud computing paradigm. It enables dynamic provisioning or de-provisioning resources for cloud software services and applications without human intervention to adapt to workload fluctuations. However, autoscaling microservice is challenging due to various factors. In particular, complex, time-varying service dependencies are difficult to quantify accurately and can lead to cascading effects when allocating resources. This paper presents DeepScaler, a deep learning-based holistic autoscaling approach for microservices that focus on coping with service dependencies to optimize service-level agreements (SLA) assurance and cost efficiency. DeepScaler employs (i) an expectation-maximization-based learning method to adaptively generate affinity matrices revealing service dependencies and (ii) an attention-based graph convolutional network to extract spatio-temporal features of microservices by aggregating neighbors' information of graph-structural data. Thus DeepScaler can capture more potential service dependencies and accurately estimate the resource requirements of all services under dynamic workloads. It allows DeepScaler to reconfigure the resources of the interacting services simultaneously in one resource provisioning operation, avoiding the cascading effect caused by service dependencies. Experimental results demonstrate that our method implements a more effective autoscaling mechanism for microservice that not only allocates resources accurately but also adapts to dependencies changes, significantly reducing SLA violations by an average of 41% at lower costs.
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