With the continuous penetration and integration of the Internet in the field of education, the availability of educational resources has been continuously improved, which has provided strong support for strengthening ...
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This study delves into the utilization of Virtual Reality (VR) technology for teaching traffic signs to young children, emphasizing a developmental interaction approach. It goes beyond previous VR education studies by...
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Diabetic Retinopathy is a major cause of blindness, especially among working-age adults globally. Early detection is crucial to prevent vision loss. However, diagnosing DR through color fundus images is challenging be...
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Cardiovascular Disease (CVDs) is among the most the deadly diseases. So, it is important to tackle and diagnose the disease in its earlier stage such that it can be curable in its later stage. If researcher is success...
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This study aims to identify critical predictors of suicide and inform targeted interventions using data from 27,014 college students. It examines a comprehensive range of mental health indicators, including psychiatri...
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The research illustrates use of ML algos in the field of housing price prediction. The models have been analyzed on real datasets downloaded from Kaggle created by Amitabha Chakraborty. We know that the source on the ...
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Solar photo voltaic (PV) energy system backbone of the renewable energy system. Energy system is depended on weather conditions such as temperature and radiation intensity. The role of machine learning (ML) for solar ...
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Worldwide, anemia is a prevalent medical ailment that is defined by a lack of hemoglobin or red blood cells. Effective treatment and the avoidance of consequences depend on the early recognition and management of anem...
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Freshness is a key factor in determining a fruit or vegetable's quality, and it directly influences the physical health and coping provocation of consumers. It ascertains the nutritional value of the specified fru...
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Runtime auto-remediation is crucial for ensuring the reliability and efficiency of distributed systems, especially within complex microservice-based applications. However, the complexity of modern microservice deploym...
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ISBN:
(纸本)9798400706585
Runtime auto-remediation is crucial for ensuring the reliability and efficiency of distributed systems, especially within complex microservice-based applications. However, the complexity of modern microservice deployments often surpasses the capabilities of traditional manual remediation and existing autonomic computing methods. Our proposed solution harnesses large language models (LLMs) to generate and execute Ansible playbooks automatically to address issues within these complex environments. Ansible playbooks, a widely adopted markup language for IT task automation, facilitate critical actions such as addressing network failures, resource constraints, configuration errors, and application bugs prevalent in managing microservices. We apply in-context learning on pre-trained LLMs using our custom-made Ansible-based remediation dataset, equipping these models to comprehend diverse remediation tasks within microservice environments. Then, these tuned LLMs efficiently generate precise Ansible scripts tailored to specific issues encountered, surpassing current state-of-the-art techniques with high functional correctness (95.45%) and average correctness (98.86%).
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