Pub Date : 2021-11-11DOI: 10.1109/I-SMAC52330.2021.9640967
K. P. N. V. Satya Sree, J. Karthik, Chava Niharika, P. Srinivas, N. Ravinder, Chitturi Prasad
While some data have an explicit, numerical form, many other data, such as gender or nationality, do not typically use numbers and are referred to as categorical data. Thus, machine learning algorithms need a way of representing categorical information numerically in order to be able to analyze them. Our project specifically focuses on optimizing the conversion of categorical features to a numerical form in order to maximize the effectiveness of various machine learning models. From the methods utilized, it has been observed that wide and deep is the most effective model for datasets that contain high-cardinality features, as opposed to learn embedding and one-hot encoding.
{"title":"Optimized Conversion of Categorical and Numerical Features in Machine Learning Models","authors":"K. P. N. V. Satya Sree, J. Karthik, Chava Niharika, P. Srinivas, N. Ravinder, Chitturi Prasad","doi":"10.1109/I-SMAC52330.2021.9640967","DOIUrl":"https://doi.org/10.1109/I-SMAC52330.2021.9640967","url":null,"abstract":"While some data have an explicit, numerical form, many other data, such as gender or nationality, do not typically use numbers and are referred to as categorical data. Thus, machine learning algorithms need a way of representing categorical information numerically in order to be able to analyze them. Our project specifically focuses on optimizing the conversion of categorical features to a numerical form in order to maximize the effectiveness of various machine learning models. From the methods utilized, it has been observed that wide and deep is the most effective model for datasets that contain high-cardinality features, as opposed to learn embedding and one-hot encoding.","PeriodicalId":178783,"journal":{"name":"2021 Fifth International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127563437","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 : 2021-11-11DOI: 10.1109/I-SMAC52330.2021.9641059
Shahida M, Sonam Choudhury, Vishnupriya Venugopal, Chinmayee Parhi, S. J
With an alarmingly increasing rate of air pollution levels and drop in the air quality due to various factors like increased demands in private transportation, need for more buildings, clearing of green patches due to booming population growth and the globalization reaching every corner of the world, the prevalence of asthma, especially in crowded cities with highly polluted air, has dramatically increased. There is a global peak in the number of people being a victim due to this chronic disease which blocks the airways of the lungs rendering them breathless. This can prove to be quite fatal if a person suffering from an asthma attack can’t get any help promptly, and hence the patients need constant monitoring. Consequently, a monitoring system that will be able to detect various factors such as high carbon dioxide levels in the air, increased humidity and temperature levels in the patient’s neighboring environment that triggers an asthma attack, in addition to monitoring the patient’s condition has become an important requirement. This paper presents a prototype of such a monitoring system that enables patients suffering from asthma or their care-takers to monitor the environment, keep an eye on the trigger factors and managing their medication, as well alerting the medics, in case of an emergency where the patient requires immediate attention as in case of a sudden asthma attack.
{"title":"A Smart Monitoring System for Asthma Patients using IoT","authors":"Shahida M, Sonam Choudhury, Vishnupriya Venugopal, Chinmayee Parhi, S. J","doi":"10.1109/I-SMAC52330.2021.9641059","DOIUrl":"https://doi.org/10.1109/I-SMAC52330.2021.9641059","url":null,"abstract":"With an alarmingly increasing rate of air pollution levels and drop in the air quality due to various factors like increased demands in private transportation, need for more buildings, clearing of green patches due to booming population growth and the globalization reaching every corner of the world, the prevalence of asthma, especially in crowded cities with highly polluted air, has dramatically increased. There is a global peak in the number of people being a victim due to this chronic disease which blocks the airways of the lungs rendering them breathless. This can prove to be quite fatal if a person suffering from an asthma attack can’t get any help promptly, and hence the patients need constant monitoring. Consequently, a monitoring system that will be able to detect various factors such as high carbon dioxide levels in the air, increased humidity and temperature levels in the patient’s neighboring environment that triggers an asthma attack, in addition to monitoring the patient’s condition has become an important requirement. This paper presents a prototype of such a monitoring system that enables patients suffering from asthma or their care-takers to monitor the environment, keep an eye on the trigger factors and managing their medication, as well alerting the medics, in case of an emergency where the patient requires immediate attention as in case of a sudden asthma attack.","PeriodicalId":178783,"journal":{"name":"2021 Fifth International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129207405","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 : 2021-11-11DOI: 10.1109/I-SMAC52330.2021.9640688
C. Thirumarai Selvi, N. Anishviswa, G. A. Karthi, K. Darshan, M. G. Balaji
This paper focuses on voice controlled car with camera, which is constructed by using major components called Arduino Uno, bluetooth module, motor driver circuit, camera and microsd card module. This automation provides a convenient way to control voice-controlled robot. This automation can aid people, who cannot walk. Voice Controlled car is controlled by using specific commands, which are recognized by mike with the mobile application. The mobile application recognize six commands and they are LEFT, RIGHT, FORWARD, BACK, STOP, KEEP WATCH IN ALL DIRECTION. This mobile application can be used in android or IOS cellphones. Here, the Bluetooth module is used for controlling the voice-controlled car wirelessly and utilizes MicroSD card for storing the video from the camera.
{"title":"Automated Voice Controlled Car Using Arduino with Camera","authors":"C. Thirumarai Selvi, N. Anishviswa, G. A. Karthi, K. Darshan, M. G. Balaji","doi":"10.1109/I-SMAC52330.2021.9640688","DOIUrl":"https://doi.org/10.1109/I-SMAC52330.2021.9640688","url":null,"abstract":"This paper focuses on voice controlled car with camera, which is constructed by using major components called Arduino Uno, bluetooth module, motor driver circuit, camera and microsd card module. This automation provides a convenient way to control voice-controlled robot. This automation can aid people, who cannot walk. Voice Controlled car is controlled by using specific commands, which are recognized by mike with the mobile application. The mobile application recognize six commands and they are LEFT, RIGHT, FORWARD, BACK, STOP, KEEP WATCH IN ALL DIRECTION. This mobile application can be used in android or IOS cellphones. Here, the Bluetooth module is used for controlling the voice-controlled car wirelessly and utilizes MicroSD card for storing the video from the camera.","PeriodicalId":178783,"journal":{"name":"2021 Fifth International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132840158","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 : 2021-11-11DOI: 10.1109/I-SMAC52330.2021.9640957
Rocky S Upadhyay, P. Tanwar, S. Degadwala
Bones are used to construct the human body. Every movement of the body necessitates the support of both small and long bones. When a human body is subjected to significant weight or a potentially dangerous incident, this bone structure may provide numerous challenges. In this urgent situation, a timely and appropriate remedy is always recommended. There are many techniques which are available in medical world to solve it. In the advanced medical era, mostly use the digital way to resolve the fracture location that includes some manual support from human that may not be accurate sometimes. So, in order to overcome all of the issues that arise during the determination of fracture type and the subsequent process of identifying the location or locations, a digital system is required that not only assists the medical supervisor but also provides an accurate solution so that the medical supervisor can decide on further treatment for resolving fracture issues in the human body and return the situation to normal.
{"title":"Fracture Type Identification Using Extra Tree Classifier","authors":"Rocky S Upadhyay, P. Tanwar, S. Degadwala","doi":"10.1109/I-SMAC52330.2021.9640957","DOIUrl":"https://doi.org/10.1109/I-SMAC52330.2021.9640957","url":null,"abstract":"Bones are used to construct the human body. Every movement of the body necessitates the support of both small and long bones. When a human body is subjected to significant weight or a potentially dangerous incident, this bone structure may provide numerous challenges. In this urgent situation, a timely and appropriate remedy is always recommended. There are many techniques which are available in medical world to solve it. In the advanced medical era, mostly use the digital way to resolve the fracture location that includes some manual support from human that may not be accurate sometimes. So, in order to overcome all of the issues that arise during the determination of fracture type and the subsequent process of identifying the location or locations, a digital system is required that not only assists the medical supervisor but also provides an accurate solution so that the medical supervisor can decide on further treatment for resolving fracture issues in the human body and return the situation to normal.","PeriodicalId":178783,"journal":{"name":"2021 Fifth International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122246715","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 : 2021-11-11DOI: 10.1109/I-SMAC52330.2021.9641053
Paras Jain, M. Murali, Amaan Ali
Facial emotion recognition is an emerging research field in detecting Facial Expression. Deep learning algorithms have gained immense success in different areas of implementation such as classification, recommendation models, object recognition etc. The various types of modules that are brought together in this technique for the betterment of the working of the model is mainly contributed by the progress in the field of Deep Learning. The main focus of this work is to create a Neural Network model which is capable of classifying human emotions in a set of 7 different classes. Image data is used for testing, validation, and training of the model.
{"title":"Face Emotion Detection Using Deep Learning","authors":"Paras Jain, M. Murali, Amaan Ali","doi":"10.1109/I-SMAC52330.2021.9641053","DOIUrl":"https://doi.org/10.1109/I-SMAC52330.2021.9641053","url":null,"abstract":"Facial emotion recognition is an emerging research field in detecting Facial Expression. Deep learning algorithms have gained immense success in different areas of implementation such as classification, recommendation models, object recognition etc. The various types of modules that are brought together in this technique for the betterment of the working of the model is mainly contributed by the progress in the field of Deep Learning. The main focus of this work is to create a Neural Network model which is capable of classifying human emotions in a set of 7 different classes. Image data is used for testing, validation, and training of the model.","PeriodicalId":178783,"journal":{"name":"2021 Fifth International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128300252","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 : 2021-11-11DOI: 10.1109/I-SMAC52330.2021.9640928
M. Surendar, P. Pradeepa
Battery surveillance is critical for the majority of battery-powered vehicles, for the benefit of the lead-acid battery's safety , functioning, and even to extend its life. Due to the development of EVs and HEVs, battery technology has made tremendous progress in recent years. However, the estimate of the state of charge (SOC) remains a battery engineering challenge. The remaining load ratio to the maximum load battery capacity is defined as the SOC. In terms of battery safety and maintenance, the SOC estimate is of prime importance. Artificial intelligence, notably machine learning-based systems, has recently been used to estimate battery state, both as part of adaptive systems and as stand-alone systems. The use of data-driven algorithms to estimate battery conditions with high precision is a potential approach. The purpose of this study is to offer a novel and highly accurate approach for predicting the state of charge (SOC) of a Li-ion battery cell that requires little conceptualization and modeling work. The battery aging process can be slowed down by properly treating the battery, including restricting frequent charge and deep drain cycles. This study presents an analysis based on IoT with an ultimate wireless battery surveillance system (WBSS) to determine the relationship between journey distance and discharge cycle. The proposed system's methodology has been tested and found to be effective.
{"title":"An IOT-based Battery Surveillance System For E-Vehicles","authors":"M. Surendar, P. Pradeepa","doi":"10.1109/I-SMAC52330.2021.9640928","DOIUrl":"https://doi.org/10.1109/I-SMAC52330.2021.9640928","url":null,"abstract":"Battery surveillance is critical for the majority of battery-powered vehicles, for the benefit of the lead-acid battery's safety , functioning, and even to extend its life. Due to the development of EVs and HEVs, battery technology has made tremendous progress in recent years. However, the estimate of the state of charge (SOC) remains a battery engineering challenge. The remaining load ratio to the maximum load battery capacity is defined as the SOC. In terms of battery safety and maintenance, the SOC estimate is of prime importance. Artificial intelligence, notably machine learning-based systems, has recently been used to estimate battery state, both as part of adaptive systems and as stand-alone systems. The use of data-driven algorithms to estimate battery conditions with high precision is a potential approach. The purpose of this study is to offer a novel and highly accurate approach for predicting the state of charge (SOC) of a Li-ion battery cell that requires little conceptualization and modeling work. The battery aging process can be slowed down by properly treating the battery, including restricting frequent charge and deep drain cycles. This study presents an analysis based on IoT with an ultimate wireless battery surveillance system (WBSS) to determine the relationship between journey distance and discharge cycle. The proposed system's methodology has been tested and found to be effective.","PeriodicalId":178783,"journal":{"name":"2021 Fifth International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128593086","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 : 2021-11-11DOI: 10.1109/I-SMAC52330.2021.9640676
R. K N, Rohitha Pasumarty
Object localization is a computer vision technique to identify real-world objects such as birds, cats, flowers, cars in images or videos. The algorithm is based on a feature extraction and learning algorithm to recognize instances of an object category. Bird’s species are the most amazing creature exist on earth. They are sensitive to changes in the environment and hence acts as bioindicator species. The main aim of this project is to identify bird species from a high-resolution digital image of Himalayan birds which would help beginner bird watchers or general people for identification. The data sets for the identification of birds are provided by Kaggle which consists of 16 species of birds. For the reduction of the overfitting problem, a data augmentation process is implemented. The model achieves an accuracy of 50.64 or 0.5064% on the dataset of Kaggle.
{"title":"Recognition of Bird Species Using Multistage Training with Transmission Learning","authors":"R. K N, Rohitha Pasumarty","doi":"10.1109/I-SMAC52330.2021.9640676","DOIUrl":"https://doi.org/10.1109/I-SMAC52330.2021.9640676","url":null,"abstract":"Object localization is a computer vision technique to identify real-world objects such as birds, cats, flowers, cars in images or videos. The algorithm is based on a feature extraction and learning algorithm to recognize instances of an object category. Bird’s species are the most amazing creature exist on earth. They are sensitive to changes in the environment and hence acts as bioindicator species. The main aim of this project is to identify bird species from a high-resolution digital image of Himalayan birds which would help beginner bird watchers or general people for identification. The data sets for the identification of birds are provided by Kaggle which consists of 16 species of birds. For the reduction of the overfitting problem, a data augmentation process is implemented. The model achieves an accuracy of 50.64 or 0.5064% on the dataset of Kaggle.","PeriodicalId":178783,"journal":{"name":"2021 Fifth International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132021532","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 : 2021-11-11DOI: 10.1109/I-SMAC52330.2021.9640687
Jianli Jiao
In recent years, the complex relationship between power supply and demand and economic growth has caused governments, enterprises and a large number of scientific researchers to pay attention to the internal relationship of power supply and demand forecasting. Power load forecasting has become a management task, science and power science, computer science and other fields. Research hotspots. This paper constructs a mathematical model and application prototype system based on intelligent optimization algorithms, predicts short-term and mid-to-long-term power load trends in my country, and analyzes the supply and demand situation of the power industry.
{"title":"Analysis of Power Load Forecasting Model Based on Intelligent Optimization Algorithm","authors":"Jianli Jiao","doi":"10.1109/I-SMAC52330.2021.9640687","DOIUrl":"https://doi.org/10.1109/I-SMAC52330.2021.9640687","url":null,"abstract":"In recent years, the complex relationship between power supply and demand and economic growth has caused governments, enterprises and a large number of scientific researchers to pay attention to the internal relationship of power supply and demand forecasting. Power load forecasting has become a management task, science and power science, computer science and other fields. Research hotspots. This paper constructs a mathematical model and application prototype system based on intelligent optimization algorithms, predicts short-term and mid-to-long-term power load trends in my country, and analyzes the supply and demand situation of the power industry.","PeriodicalId":178783,"journal":{"name":"2021 Fifth International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC)","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124309679","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 : 2021-11-11DOI: 10.1109/I-SMAC52330.2021.9640782
Xiangdong Guo, Caixia He
With the continuous development of Big Data information resources, the resources that people can obtain through the Internet have also increased. At present, there are about 3,000 known languages in the world. Research on machine translation and automatic acquisition of machine translation knowledge has strong practical significance for people to break through language barriers and make use of Internet information. Based on the description of the whole process of bilingual corpus construction, this essay proposes the flexibility of the information change model and quantum translation platform of the English-Chinese corpus based on big data.
{"title":"English-Chinese Corpus Information Collection and Quantum Translation Based on Big Data","authors":"Xiangdong Guo, Caixia He","doi":"10.1109/I-SMAC52330.2021.9640782","DOIUrl":"https://doi.org/10.1109/I-SMAC52330.2021.9640782","url":null,"abstract":"With the continuous development of Big Data information resources, the resources that people can obtain through the Internet have also increased. At present, there are about 3,000 known languages in the world. Research on machine translation and automatic acquisition of machine translation knowledge has strong practical significance for people to break through language barriers and make use of Internet information. Based on the description of the whole process of bilingual corpus construction, this essay proposes the flexibility of the information change model and quantum translation platform of the English-Chinese corpus based on big data.","PeriodicalId":178783,"journal":{"name":"2021 Fifth International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC)","volume":"83 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125631562","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 : 2021-11-11DOI: 10.1109/I-SMAC52330.2021.9640743
P. Charles, U. L. Stanislaus
Heuristic algorithms are used solve the VM consolidation. One of the well-known approaches to solve this issue is bin packing method. This method is considered as NP-hard. There are quite number of other heuristics algorithm available to address this issue. This problem can be handled effortlessly with one dimensional bin packing m. In Bin Packing there are few methods used to pack the empty bins efficiently. First Fit (FF) Bin packing algorithm is one of most popular and efficient method to pack the empty bins. Here, FF algorithm packs available items into first bin where it aptly accommodates. Best Fit algorithm places an item into a maximum load instead of placing in the first bin like First Fit algorithm. These two methods are enhanced by making small alteration in the algorithm which are named as First Fit Decreasing (FFD) method and Best Fit Decreasing (BFD) method. These methods could not be implemented straightaway for VM consolidation. This must me altered appropriately before applying for this issue. In addition, consider that physical machines (PM) don’t have any virtual machines (VM) before executing the migration algorithm and also the utilization of the datacenters is managed by the number of VM’s request and server’s request. The energy consumption of the VM’s is majorly considers the CPU utilization instead of bandwidth and memory. The authors concentrate on CPU utilization rate to minimize the energy consumption.
采用启发式算法解决虚拟机整合问题。解决这个问题的一个众所周知的方法是装箱法。这种方法被认为是NP-hard。还有很多其他的启发式算法可以解决这个问题。这个问题可以毫不费力地处理一维箱包装m。在箱包装中，很少有方法用来有效地包装空箱。首次拟合(First Fit, FF)装箱算法是目前最流行、最有效的空箱装箱方法之一。在这里，FF算法将可用的物品打包到第一个适合容纳的箱子中。最佳匹配算法将物品放入最大负载中，而不是像第一匹配算法那样放在第一个箱子中。这两种方法通过对算法进行小的修改而得到增强，分别被命名为First Fit reduction (FFD) method和Best Fit reduction (BFD) method。这些方法不能直接用于VM整合。在申请这个问题之前，我必须进行适当的修改。此外，在执行迁移算法之前，考虑物理机(PM)没有任何虚拟机(VM)，并且数据中心的利用率由VM请求的数量和服务器请求的数量来管理。虚拟机的能耗主要考虑的是CPU利用率，而不是带宽和内存。作者着重于CPU利用率，以尽量减少能耗。
{"title":"Secure Virtual Machine Migration using Ant Colony Algorithm","authors":"P. Charles, U. L. Stanislaus","doi":"10.1109/I-SMAC52330.2021.9640743","DOIUrl":"https://doi.org/10.1109/I-SMAC52330.2021.9640743","url":null,"abstract":"Heuristic algorithms are used solve the VM consolidation. One of the well-known approaches to solve this issue is bin packing method. This method is considered as NP-hard. There are quite number of other heuristics algorithm available to address this issue. This problem can be handled effortlessly with one dimensional bin packing m. In Bin Packing there are few methods used to pack the empty bins efficiently. First Fit (FF) Bin packing algorithm is one of most popular and efficient method to pack the empty bins. Here, FF algorithm packs available items into first bin where it aptly accommodates. Best Fit algorithm places an item into a maximum load instead of placing in the first bin like First Fit algorithm. These two methods are enhanced by making small alteration in the algorithm which are named as First Fit Decreasing (FFD) method and Best Fit Decreasing (BFD) method. These methods could not be implemented straightaway for VM consolidation. This must me altered appropriately before applying for this issue. In addition, consider that physical machines (PM) don’t have any virtual machines (VM) before executing the migration algorithm and also the utilization of the datacenters is managed by the number of VM’s request and server’s request. The energy consumption of the VM’s is majorly considers the CPU utilization instead of bandwidth and memory. The authors concentrate on CPU utilization rate to minimize the energy consumption.","PeriodicalId":178783,"journal":{"name":"2021 Fifth International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC)","volume":"95 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123990600","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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