2020 IEEE International Conference on Cloud Computing in Emerging Markets (CCEM)最新文献

Sizing and Coordinated Scaling of Microservices 微服务的规模和协调扩展

2020 IEEE International Conference on Cloud Computing in Emerging Markets (CCEM) Pub Date : 2020-11-01 DOI: 10.1109/CCEM50674.2020.00013

Preyashi Agarwal, J. Lakshmi

{"title":"Sizing and Coordinated Scaling of Microservices","authors":"Preyashi Agarwal, J. Lakshmi","doi":"10.1109/CCEM50674.2020.00013","DOIUrl":"https://doi.org/10.1109/CCEM50674.2020.00013","url":null,"abstract":"Microservice(MS) based architecture is recently drawing attention for its ability to offer ease of deployment, agile independent scaling, and fault-tolerance. This nature of architecting an application has been promoted with the idea of scaling. Containers enable faster MSes instantiation aiding agility in deployment and scaling. The ability of MS independent scaling can be exploited to find the right-size of the MS to scale with, where the size of the MS is with respect to the resources allocated to it. As a result, any sizing strategy used in scaling needs to be quick to aid in decision making. Apart from this, another interesting and important fact with MSes is that while they benefit from independent scaling, they still belong to a single application. Each functionality of the application is inter- related to fulfill a business goal. Also, the business logic is realized using different MSes, often creating a workflow. This introduces correlations among MSes for scaling. Intuitively, such correlations can provide early indications of workload variations which can be used during scaling to enhance application performance. This work focuses on designing scaling algorithm which explores such correlations to auto-scale the MS based applications. The paper also proposes a low computational complexity MS sizing algorithm that finds the right-size of the MSes, using heuristics based on workload variation trends observed from past usage history. The proposed sizing algorithm is compared with several published sizing strategies and the results indicate that the cost benefits achieved are comparable to other algorithms in the state-of-art, while being sufficiently faster than others. And, the coordinated scaling algorithm proposed in the paper provides significant benefits yielding to more than 50% reduction in SLA violations as compared to independent MS scaling approaches.","PeriodicalId":160355,"journal":{"name":"2020 IEEE International Conference on Cloud Computing in Emerging Markets (CCEM)","volume":"61 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128512419","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}

引用次数: 1

Conference Schedule 会议日程安排

2020 IEEE International Conference on Cloud Computing in Emerging Markets (CCEM) Pub Date : 2020-11-01 DOI: 10.1109/ccem50674.2020.00011

引用次数: 0

Real-time adaptable resource allocation for distributed data-intensive applications over cloud and edge environments 用于云和边缘环境上的分布式数据密集型应用程序的实时可适应资源分配

2020 IEEE International Conference on Cloud Computing in Emerging Markets (CCEM) Pub Date : 2020-11-01 DOI: 10.1109/CCEM50674.2020.00026

Jean-Didier Totow Tom-Ata, D. Kyriazis

引用次数: 1

R Peak Detection in ECG signals using Chebfun 用Chebfun检测心电信号中的R峰值

2020 IEEE International Conference on Cloud Computing in Emerging Markets (CCEM) Pub Date : 2020-11-01 DOI: 10.1109/CCEM50674.2020.00023

Yamini Madan, Shamika Ganesan, B. G. Gowri, V. Sowmya, K. Soman

引用次数: 0

[Copyright notice] (版权)

2020 IEEE International Conference on Cloud Computing in Emerging Markets (CCEM) Pub Date : 2020-11-01 DOI: 10.1109/ccem50674.2020.00003

引用次数: 0

Optimizing Liver disease prediction with Random Forest by various Data balancing Techniques 通过各种数据平衡技术优化随机森林肝病预测

2020 IEEE International Conference on Cloud Computing in Emerging Markets (CCEM) Pub Date : 2020-11-01 DOI: 10.1109/CCEM50674.2020.00030

Sateesh Ambesange, V. A, Rashmi Uppin, Shruthi Patil, Vilaskumar Patil

{"title":"Optimizing Liver disease prediction with Random Forest by various Data balancing Techniques","authors":"Sateesh Ambesange, V. A, Rashmi Uppin, Shruthi Patil, Vilaskumar Patil","doi":"10.1109/CCEM50674.2020.00030","DOIUrl":"https://doi.org/10.1109/CCEM50674.2020.00030","url":null,"abstract":"Liver disease is a prominent disease other than heart attack, which is taking a lot of lives. Since most of the time liver disease is detected at a later stage leading to death. Number of liver patients is increasing because of several reasons like over consumption of alcohol, breathing in injurious gas, consuming polluted water and so on which can affect health parameters. Using a machine learning prediction models, liver diseases can be predicted using those health parameters in early stages. In this work to build the machine-learning model, Indian Liver Patient Dataset (ILPD) hosted at UCI.edu [1] is used, which is based on Indian patient and Random Forest (RF) algorithm is used to predict the disease with different preprocessing techniques. Data set is checked for skewness, outliers and imbalance using univariate and bivariate analysis and then suitable algorithms used to remove outliers and various oversampling and under sampling techniques are used to balance the data. Further refinement of model is done through hyper parameter tuning using grid search and feature selection. The final model provides 100% accuracy and also good score across different metrics.","PeriodicalId":160355,"journal":{"name":"2020 IEEE International Conference on Cloud Computing in Emerging Markets (CCEM)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127526731","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}

引用次数: 8

Performance sensitivity of Operating System parameters in Microservice environments 微服务环境下操作系统参数的性能敏感性

2020 IEEE International Conference on Cloud Computing in Emerging Markets (CCEM) Pub Date : 2020-11-01 DOI: 10.1109/CCEM50674.2020.00025

K. Goutham, Bharani Ujjaini Kempaiah, Ruben John Mampilli, Subramaniam Kalambur

引用次数: 1

Managing Congregations of People by Predicting Likelihood of a Person being Infected by a Contagious Disease like the COVID Virus 通过预测一个人被COVID病毒等传染病感染的可能性来管理人群

2020 IEEE International Conference on Cloud Computing in Emerging Markets (CCEM) Pub Date : 2020-11-01 DOI: 10.1109/CCEM50674.2020.00017

Pranav Gupta, Manish Gupta

{"title":"Managing Congregations of People by Predicting Likelihood of a Person being Infected by a Contagious Disease like the COVID Virus","authors":"Pranav Gupta, Manish Gupta","doi":"10.1109/CCEM50674.2020.00017","DOIUrl":"https://doi.org/10.1109/CCEM50674.2020.00017","url":null,"abstract":"Pandemics such as Covid-19 change the status quo. The things we take for granted become no longer true. When the disease is contagious then in-person communication is fraught with danger for all parties. Despite all these dangers people still required to meet in person for various reasons that could range from personal to official to government business. Can we have a way to understand the risk of a person being infected as compared to another person so that we can make decisions of segregating the two people or to decline entry to a person? In this paper we provide an architecture and an approach that caters to estimating a score for each person based on GPS (Global Positioning System) locations. A score is computed by an application running in cloud and receiving as input the GPS trajectory information from each person's device (like a mobile or a smart watch). With the scores of any two people in hand one can predict who is more likely to be infected. We complement this approach with extensive simulation results to validate our approach. Our results show that we can achieve a high accuracy (80% to 90%) of predicting which person (of the two being compared) has an infection.","PeriodicalId":160355,"journal":{"name":"2020 IEEE International Conference on Cloud Computing in Emerging Markets (CCEM)","volume":"2020 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123435598","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}

引用次数: 0

Landmark Detection in 3D Medical Images Using Reinforcement Learning 基于强化学习的三维医学图像的地标检测

2020 IEEE International Conference on Cloud Computing in Emerging Markets (CCEM) Pub Date : 2020-11-01 DOI: 10.1109/CCEM50674.2020.00019

Srinivasa Rao Kundeti, Dhaval Parmar, A. Mv, Deepam Gautam

引用次数: 3

[Title page] (标题页)

2020 IEEE International Conference on Cloud Computing in Emerging Markets (CCEM) Pub Date : 2020-11-01 DOI: 10.1109/ccem50674.2020.00002

引用次数: 0