Pub Date : 2021-09-23DOI: 10.1109/CCGE50943.2021.9776474
Anitha. K Jyoti, Bhanu Prakash, Ramesh.L Dean, M. Marsaline beno
At present reduced energy consumption at industrial site has prominent importance in the economy of the industry. Finding the areas of the energy wastes at different levels and estimating cost effective recommendations are the research challenges. With reference to past three years, various issues presently available in the industry are collected for analysis. With reference to the collected information, suitable recommendations are suggested for saving energy through suitable recommendation without investment, Recommendations suggested with Investment and Recommendations suggested with implementation of renewable power
{"title":"Comparative Energy Performance Analysis at Dyes and Coating Industry","authors":"Anitha. K Jyoti, Bhanu Prakash, Ramesh.L Dean, M. Marsaline beno","doi":"10.1109/CCGE50943.2021.9776474","DOIUrl":"https://doi.org/10.1109/CCGE50943.2021.9776474","url":null,"abstract":"At present reduced energy consumption at industrial site has prominent importance in the economy of the industry. Finding the areas of the energy wastes at different levels and estimating cost effective recommendations are the research challenges. With reference to past three years, various issues presently available in the industry are collected for analysis. With reference to the collected information, suitable recommendations are suggested for saving energy through suitable recommendation without investment, Recommendations suggested with Investment and Recommendations suggested with implementation of renewable power","PeriodicalId":130452,"journal":{"name":"2021 International Conference on Computing, Communication and Green Engineering (CCGE)","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128859671","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Modern day software applications are required to have high availability and performance capabilities to ensure highly productive features and a smooth user experience. It becomes increasingly difficult for organizations to innovate with rapid building, testing and deployment of systems in static, monolithic environments. In order to ascertain the development of resilient applications, Kubernetes is widely used for distributed systems for workload scalability and orchestration of containers. The management of the system using Kubernetes becomes progressively inconvenient with increasing size and complexity. In order to make the process of Kubernetes configuration simpler and faster, Helm charts are used to preconfigure applications and automate the processes of development, testing and production. This paper proposes a method to ease the deployment of the enterprise application in Kubernetes using Helm charts. Our study shows that deployment of Kubernetes resources is simplified using Helm such that applications can be defined as a set of components in the minikube Kubernetes cluster. The experimental results of the proposed method show that there is 6.185 times speed improvement in the deployment process by using Helm. This makes it extremely influential for DevOps teams to improve their cluster management.
{"title":"Creating Helm Charts to ease deployment of Enterprise Application and its related Services in Kubernetes","authors":"Shivani Gokhale, Reetika Poosarla, Sanjeevani Tikar, Swapnali Gunjawate, Aparna Hajare, Shilpa Deshpande, Sourabh Gupta, Kanchan Karve","doi":"10.1109/CCGE50943.2021.9776450","DOIUrl":"https://doi.org/10.1109/CCGE50943.2021.9776450","url":null,"abstract":"Modern day software applications are required to have high availability and performance capabilities to ensure highly productive features and a smooth user experience. It becomes increasingly difficult for organizations to innovate with rapid building, testing and deployment of systems in static, monolithic environments. In order to ascertain the development of resilient applications, Kubernetes is widely used for distributed systems for workload scalability and orchestration of containers. The management of the system using Kubernetes becomes progressively inconvenient with increasing size and complexity. In order to make the process of Kubernetes configuration simpler and faster, Helm charts are used to preconfigure applications and automate the processes of development, testing and production. This paper proposes a method to ease the deployment of the enterprise application in Kubernetes using Helm charts. Our study shows that deployment of Kubernetes resources is simplified using Helm such that applications can be defined as a set of components in the minikube Kubernetes cluster. The experimental results of the proposed method show that there is 6.185 times speed improvement in the deployment process by using Helm. This makes it extremely influential for DevOps teams to improve their cluster management.","PeriodicalId":130452,"journal":{"name":"2021 International Conference on Computing, Communication and Green Engineering (CCGE)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129433225","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-09-23DOI: 10.1109/CCGE50943.2021.9776433
Amruta S Suryawanshi, M. J. Khurjekar
Agriculture plays an important role by contributing to the economy of India. 75% of the population has agriculture as their major occupation and only source of income. There are various parts in the process of production where we need to pay more attention to the higher productivity of crops. Many farmers face loss in yields every year due to diseases affecting the crops. A fast and automated system to detect the diseases on crops in the early stage can be very helpful in such situations. Having such a vast variety of types of crops grown in India, we will focus on cotton and turmeric crops in the Marathwada region, Maharashtra, India. Our proposed system aims to develop an auto-guided drone that can take the images of crop leaves as input. These images will then be processed by applying Convolutional Neural Network (CNN) to detect the diseases which are affecting the crops. This system will also help mark the most affected regions of fields. By using this system, we can increase the productivity of the crop
{"title":"Aerial Imagery for Plant Disease Detection by Using Machine Learning of Typical Crops in Marathwada","authors":"Amruta S Suryawanshi, M. J. Khurjekar","doi":"10.1109/CCGE50943.2021.9776433","DOIUrl":"https://doi.org/10.1109/CCGE50943.2021.9776433","url":null,"abstract":"Agriculture plays an important role by contributing to the economy of India. 75% of the population has agriculture as their major occupation and only source of income. There are various parts in the process of production where we need to pay more attention to the higher productivity of crops. Many farmers face loss in yields every year due to diseases affecting the crops. A fast and automated system to detect the diseases on crops in the early stage can be very helpful in such situations. Having such a vast variety of types of crops grown in India, we will focus on cotton and turmeric crops in the Marathwada region, Maharashtra, India. Our proposed system aims to develop an auto-guided drone that can take the images of crop leaves as input. These images will then be processed by applying Convolutional Neural Network (CNN) to detect the diseases which are affecting the crops. This system will also help mark the most affected regions of fields. By using this system, we can increase the productivity of the crop","PeriodicalId":130452,"journal":{"name":"2021 International Conference on Computing, Communication and Green Engineering (CCGE)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129594301","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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