scholarly journals High Reliable and Efficient Three Layer Cloud Dispatching Architecture in the Heterogeneous Cloud Computing Environment

2018 ◽  
Author(s):  
Mao-Lun Chiang ◽  
Yung-Fa Huang ◽  
Hui-Ching Hsieh ◽  
Wen-Chung Tsai
Keyword(s):  
Cloud Computing ◽  
Scheduling Algorithm ◽  
Rapid Development ◽  
Task Completion ◽  
Computing Environment ◽  
Cloud Computing Environment ◽  
Application Service ◽  
Different Types ◽  
Heterogeneous Cloud ◽  
Layer Cloud

Due to the rapid development and popularity of the Internet, cloud computing has become an indispensable application service. However, how to assign various tasks to the appropriate service nodes is an important issue. Based on the reason above, an efficient scheduling algorithm is necessary to enhance the performance of system. Therefore, a Three-Layer Cloud Dispatching (TLCD) architecture is proposed to enhance the performance of task scheduling. In first layer, the tasks need to be distinguished to different types by their characters. Subsequently, the Cluster Selection Algorithm is proposed to dispatch the task to appropriately service cluster in the secondly layer. Besides, a new scheduling algorithm is proposed to dispatch the task to a suitable server in a server cluster to improve the dispatching efficiency in the thirdly layer. Basically, the TLCD architecture can obtain better task completion time than previous works. Besides, our algorithm and can achieve load-balancing and reliability in cloud computing network.

scholarly journals Highly Reliable and Efficient Three-Layer Cloud Dispatching Architecture in the Heterogeneous Cloud Computing Environment †

2018 ◽  
Vol 8 (8) ◽  
pp. 1385 ◽  
Author(s):  
Mao-Lun Chiang ◽  
Yung-Fa Huang ◽  
Hui-Ching Hsieh ◽  
Wen-Chung Tsai
Keyword(s):  
Cloud Computing ◽  
Scheduling Algorithm ◽  
Rapid Development ◽  
Task Completion ◽  
Computing Environment ◽  
Cloud Computing Environment ◽  
Application Service ◽  
Different Types ◽  
Heterogeneous Cloud ◽  
Layer Cloud

Due to the rapid development and popularity of the Internet, cloud computing has become an indispensable application service. However, how to assign various tasks to the appropriate service nodes is an important issue. Based on the reason above, an efficient scheduling algorithm is necessary to enhance the performance of the system. Therefore, a Three-Layer Cloud Dispatching (TLCD) architecture is proposed to enhance the performance of task scheduling. In the first layer, the tasks need to be distinguished into different types by their characters. Subsequently, the Cluster Selection Algorithm is proposed to dispatch the tasks to appropriate service clusters in the second layer. Besides this, a new scheduling algorithm is proposed in the third layer to dispatch the task to a suitable server in a server cluster to enhance the scheduling efficiency. Basically, the best task completion time can be obtained in our TLCD architecture. Furthermore, load balancing and reliability can be achieved under a cloud computing network environment.

scholarly journals Cloud-based intelligent self-diagnosis and department recommendation service using Chinese medical BERT

2021 ◽  
Vol 10 (1) ◽  
Author(s):  
Junshu Wang ◽  
Guoming Zhang ◽  
Wei Wang ◽  
Ka Zhang ◽  
Yehua Sheng
Keyword(s):  
Cloud Computing ◽  
Large Scale ◽  
Medical Service ◽  
Rapid Development ◽  
Medical Knowledge ◽  
Language Models ◽  
Computing Environment ◽  
Computing Power ◽  
Cloud Computing Environment ◽  
Proposed Model

AbstractWith the rapid development of hospital informatization and Internet medical service in recent years, most hospitals have launched online hospital appointment registration systems to remove patient queues and improve the efficiency of medical services. However, most of the patients lack professional medical knowledge and have no idea of how to choose department when registering. To instruct the patients to seek medical care and register effectively, we proposed CIDRS, an intelligent self-diagnosis and department recommendation framework based on Chinese medical Bidirectional Encoder Representations from Transformers (BERT) in the cloud computing environment. We also established a Chinese BERT model (CHMBERT) trained on a large-scale Chinese medical text corpus. This model was used to optimize self-diagnosis and department recommendation tasks. To solve the limited computing power of terminals, we deployed the proposed framework in a cloud computing environment based on container and micro-service technologies. Real-world medical datasets from hospitals were used in the experiments, and results showed that the proposed model was superior to the traditional deep learning models and other pre-trained language models in terms of performance.

Impact of Cloud of Clouds in Enterprises Applications

2019 ◽  
pp. 21-33
Author(s):  
Lavanya S. ◽  
Susila N. ◽  
Venkatachalam K.
Keyword(s):  
Cloud Computing ◽  
Scheduling Algorithm ◽  
Business Environment ◽  
Cloud Environment ◽  
Computing Environment ◽  
Cloud Computing Environment ◽  
Network Bandwidth ◽  
Enterprise Application ◽  
Major Disruption ◽  
Utmost Care

In recent times, the cloud has become a leading technology demanding its functionality in every business. According to research firm IDC and Gartner study, nearly one-third of the worldwide enterprise application market will be SaaS-based by 2018, driving annual SaaS revenue to $50.8 billion, from $22.6 billion in 2013. Downtime is treated as the primary drawback which may affect great deals in businesses. The service unavailability leads to a major disruption affecting the business environment. Hence, utmost care should be taken to scale the availability of services. As cloud computing has plenty of uncertainty with respect to network bandwidth and resources accessibility, delegating the computing resources as services should be scheduled accordingly. This chapter proposes a study on cloud of clouds and its impact on a business enterprise. It is also decided to propose a suitable scheduling algorithm to the cloud of cloud environment so as to trim the downtime problem faced by the cloud computing environment.

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