Pub Date : 2017-01-01DOI: 10.1109/CONFLUENCE.2017.7943203
Yifan Lu, Lu Yang, V. Bhavsar, Neetesh Kumar
In order to reduce the computing time for processing large tree-structured data sets, parallel processing has been used. Recently, research has been done on parallel computing of tree-structured data on Graphics Processing Units (GPUs). GPU device cannot directly access the tree structured data on hard disks which is commonly stored as objects or linked-lists. So, it is required to copying this tree structured data from hard disk to device memory for the computation and copying tree structured data in its normal structure is very costly because of lots of pointers overhead. Existing tree data structures on GPUs are commonly applied to storing a particular kind of tree, and support limited types of tree traversals. In this work, a tree data structure is proposed to store different kind of trees as a linear data structure (fast in copying). The proposed data structure is applied on general trees and binary trees and supports four common types of tree traversals: pre-order, post-order, in-order and breadth-first traversals. Therefore, most of the tree algorithms can be implemented on GPUs by using this proposed data structure. The results show that the proposed data structure is successfully implemented for all the traversals for binary as well as general trees.
{"title":"Tree structured data processing on GPUs","authors":"Yifan Lu, Lu Yang, V. Bhavsar, Neetesh Kumar","doi":"10.1109/CONFLUENCE.2017.7943203","DOIUrl":"https://doi.org/10.1109/CONFLUENCE.2017.7943203","url":null,"abstract":"In order to reduce the computing time for processing large tree-structured data sets, parallel processing has been used. Recently, research has been done on parallel computing of tree-structured data on Graphics Processing Units (GPUs). GPU device cannot directly access the tree structured data on hard disks which is commonly stored as objects or linked-lists. So, it is required to copying this tree structured data from hard disk to device memory for the computation and copying tree structured data in its normal structure is very costly because of lots of pointers overhead. Existing tree data structures on GPUs are commonly applied to storing a particular kind of tree, and support limited types of tree traversals. In this work, a tree data structure is proposed to store different kind of trees as a linear data structure (fast in copying). The proposed data structure is applied on general trees and binary trees and supports four common types of tree traversals: pre-order, post-order, in-order and breadth-first traversals. Therefore, most of the tree algorithms can be implemented on GPUs by using this proposed data structure. The results show that the proposed data structure is successfully implemented for all the traversals for binary as well as general trees.","PeriodicalId":6651,"journal":{"name":"2017 7th International Conference on Cloud Computing, Data Science & Engineering - Confluence","volume":"178 1","pages":"498-505"},"PeriodicalIF":0.0,"publicationDate":"2017-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81629568","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 : 2017-01-01DOI: 10.1109/CONFLUENCE.2017.7943133
Barjinder Kaur, D. Singh
Electroencephalography (EEG) have been receiving a lot of attention due to its recent use in the field of biometrics. Signals traced from the different parts of the brain has become an upsurge area of interest for the researchers. Evidences have been provided by the research communities where the uniqueness of neuro-signals can possibly be used for building a robust biometric identification system. In this paper, we investigate the robustness of EEG signals in two different scenario of data collection, namely, Eyes Open (EO) and Eyes Closed (EC) for building a person identification system. For this, a publicly available EEG signals dataset of 109 users have been used. The EEG signals have been modeled using two different classifier, namely, Support Vector Machine (SVM) and Random Forest (RF). Next, a feature selection approach has been applied to reduce the number of features and results have been computed to find optimal feature dimension. From experiments, person identification rates of 97.64% (EO) and 96.02% (EC) using SVM, and 98.16% (EO) and 97.30% (EC) have been recorded using RF classifiers.
{"title":"Neuro signals: A future biomertic approach towards user identification","authors":"Barjinder Kaur, D. Singh","doi":"10.1109/CONFLUENCE.2017.7943133","DOIUrl":"https://doi.org/10.1109/CONFLUENCE.2017.7943133","url":null,"abstract":"Electroencephalography (EEG) have been receiving a lot of attention due to its recent use in the field of biometrics. Signals traced from the different parts of the brain has become an upsurge area of interest for the researchers. Evidences have been provided by the research communities where the uniqueness of neuro-signals can possibly be used for building a robust biometric identification system. In this paper, we investigate the robustness of EEG signals in two different scenario of data collection, namely, Eyes Open (EO) and Eyes Closed (EC) for building a person identification system. For this, a publicly available EEG signals dataset of 109 users have been used. The EEG signals have been modeled using two different classifier, namely, Support Vector Machine (SVM) and Random Forest (RF). Next, a feature selection approach has been applied to reduce the number of features and results have been computed to find optimal feature dimension. From experiments, person identification rates of 97.64% (EO) and 96.02% (EC) using SVM, and 98.16% (EO) and 97.30% (EC) have been recorded using RF classifiers.","PeriodicalId":6651,"journal":{"name":"2017 7th International Conference on Cloud Computing, Data Science & Engineering - Confluence","volume":"21 1","pages":"112-117"},"PeriodicalIF":0.0,"publicationDate":"2017-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81959302","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 : 2017-01-01DOI: 10.1109/CONFLUENCE.2017.7943153
J. Desai, Aditya Bhanje, S. Biradar, Dion Fernandes
The main objective of this project is to design a solution for overcoming the parking issues that exist in public places such as malls, multiplexes etc. especially on weekends. The aim is to achieve this by using the concept of Internet of Things (IoT), wherein an Android Application is created for the customer, whose details are constantly updated by the hardware/server at the location. The features include unique identification for each vehicle, display of avaliable parking slots on the mobile application, possibility of making reservations for the same, maintenance of a database (for the management).
{"title":"IoT based vehicle parking manager","authors":"J. Desai, Aditya Bhanje, S. Biradar, Dion Fernandes","doi":"10.1109/CONFLUENCE.2017.7943153","DOIUrl":"https://doi.org/10.1109/CONFLUENCE.2017.7943153","url":null,"abstract":"The main objective of this project is to design a solution for overcoming the parking issues that exist in public places such as malls, multiplexes etc. especially on weekends. The aim is to achieve this by using the concept of Internet of Things (IoT), wherein an Android Application is created for the customer, whose details are constantly updated by the hardware/server at the location. The features include unique identification for each vehicle, display of avaliable parking slots on the mobile application, possibility of making reservations for the same, maintenance of a database (for the management).","PeriodicalId":6651,"journal":{"name":"2017 7th International Conference on Cloud Computing, Data Science & Engineering - Confluence","volume":"50 1","pages":"222-225"},"PeriodicalIF":0.0,"publicationDate":"2017-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87954133","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 : 2017-01-01DOI: 10.1109/CONFLUENCE.2017.7943147
Vidushi Vashishth, Anshuman Chhabra, A. Sood
There have been many recent developments in integrating the Cloud with the Internet of Τhings (IoT) which comprise of up and coming technologies such as Smart Cities and Smart devices. This federation has resulted in research being directed towards further integration of Big Data with the Cloud, as IoT devices consisting of such technologies generate a continuous stream of sensor data. Thus, in this paper, we seek to present a predictive approach to task scheduling with the aim of reducing the overhead incurred when Big Data is processed on the Cloud. Subsequently, we wish to increase both the efficiency and reliability of the Cloud network while handling Big Data. We present a method of using classification in Machine Learning as a tool for scheduling tasks and assigning them to Virtual Machines (VMs) in the Cloud environment. A comparative study is undertaken to observe which brand of classifiers perform optimally in the given scenario. Particle Swarm Optimization (PSO) is used to generate the dataset which is used to train the classifiers. A number of classification algorithms such as Naive Bayes, Random Forest and Κ Nearest Neighbor are then used to predict the VM best suited to a task in the test dataset.
{"title":"A predictive approach to task scheduling for Big Data in cloud environments using classification algorithms","authors":"Vidushi Vashishth, Anshuman Chhabra, A. Sood","doi":"10.1109/CONFLUENCE.2017.7943147","DOIUrl":"https://doi.org/10.1109/CONFLUENCE.2017.7943147","url":null,"abstract":"There have been many recent developments in integrating the Cloud with the Internet of Τhings (IoT) which comprise of up and coming technologies such as Smart Cities and Smart devices. This federation has resulted in research being directed towards further integration of Big Data with the Cloud, as IoT devices consisting of such technologies generate a continuous stream of sensor data. Thus, in this paper, we seek to present a predictive approach to task scheduling with the aim of reducing the overhead incurred when Big Data is processed on the Cloud. Subsequently, we wish to increase both the efficiency and reliability of the Cloud network while handling Big Data. We present a method of using classification in Machine Learning as a tool for scheduling tasks and assigning them to Virtual Machines (VMs) in the Cloud environment. A comparative study is undertaken to observe which brand of classifiers perform optimally in the given scenario. Particle Swarm Optimization (PSO) is used to generate the dataset which is used to train the classifiers. A number of classification algorithms such as Naive Bayes, Random Forest and Κ Nearest Neighbor are then used to predict the VM best suited to a task in the test dataset.","PeriodicalId":6651,"journal":{"name":"2017 7th International Conference on Cloud Computing, Data Science & Engineering - Confluence","volume":"65 1","pages":"188-192"},"PeriodicalIF":0.0,"publicationDate":"2017-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76519963","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 : 2017-01-01DOI: 10.1109/CONFLUENCE.2017.7943232
Mohamad Firham Efendy Md. Senan, S. Abdullah, Wafa’ Mohd Kharudin, Nur Afifah Mohd Saupi
Closed-circuit television (CCTV) is used to perform surveillance recordings, and it is one of the most common digital devices that provide digital evidence for the purpose of forensic analysis. In video forensic analysis, the footage with the target subject or object is extracted out from the CCTV recordings for further analysis. However, the quality of these recordings are often poor due to several factors, such as the type of the camera, the configuration, and also the position of the camera. The results of forensic face recognition depend highly on the quality of the CCTV recordings. Poor quality of CCTV recordings would reduce the confidence level of the face recognition result, thus would not make a strong evidence to be presented in a court of law. The objective of this research is to conceptualise a framework for quality assessment in CCTV evidence to be used in forensic face recognition analysis. The method of this research was divided into two phases. Initial phase covered CCTV evidence testing phase where the experiment was done based on different types of CCTV camera with different resolutions, and distances between the subject and the camera. In the second phase, the face of the subjects were compared to the face taken during the enrolment phase. The score obtained from the forensic face recognition system would be based on the camera resolutions, types of camera, distances, and also the changes of ranking score after applying the enhancement process such as Bicubic to the facial images. The results were analyzed for quality assessment towards these parameters. In general, the evaluation of scoring and ranking decreased as the distance increased. The face also could not be detected by the system when they were taken more than 5 meters distance from the camera. The highest score of 5.95 was obtained by using resolution 1280 × 720 at distance of 3 meters taken by camera model ACTI E62. The Bicubic enhancement method improved the scoring and ranking especially with the camera model that have low resolution modes.
{"title":"CCTV quality assessment for forensics facial recognition analysis","authors":"Mohamad Firham Efendy Md. Senan, S. Abdullah, Wafa’ Mohd Kharudin, Nur Afifah Mohd Saupi","doi":"10.1109/CONFLUENCE.2017.7943232","DOIUrl":"https://doi.org/10.1109/CONFLUENCE.2017.7943232","url":null,"abstract":"Closed-circuit television (CCTV) is used to perform surveillance recordings, and it is one of the most common digital devices that provide digital evidence for the purpose of forensic analysis. In video forensic analysis, the footage with the target subject or object is extracted out from the CCTV recordings for further analysis. However, the quality of these recordings are often poor due to several factors, such as the type of the camera, the configuration, and also the position of the camera. The results of forensic face recognition depend highly on the quality of the CCTV recordings. Poor quality of CCTV recordings would reduce the confidence level of the face recognition result, thus would not make a strong evidence to be presented in a court of law. The objective of this research is to conceptualise a framework for quality assessment in CCTV evidence to be used in forensic face recognition analysis. The method of this research was divided into two phases. Initial phase covered CCTV evidence testing phase where the experiment was done based on different types of CCTV camera with different resolutions, and distances between the subject and the camera. In the second phase, the face of the subjects were compared to the face taken during the enrolment phase. The score obtained from the forensic face recognition system would be based on the camera resolutions, types of camera, distances, and also the changes of ranking score after applying the enhancement process such as Bicubic to the facial images. The results were analyzed for quality assessment towards these parameters. In general, the evaluation of scoring and ranking decreased as the distance increased. The face also could not be detected by the system when they were taken more than 5 meters distance from the camera. The highest score of 5.95 was obtained by using resolution 1280 × 720 at distance of 3 meters taken by camera model ACTI E62. The Bicubic enhancement method improved the scoring and ranking especially with the camera model that have low resolution modes.","PeriodicalId":6651,"journal":{"name":"2017 7th International Conference on Cloud Computing, Data Science & Engineering - Confluence","volume":"10 1","pages":"649-655"},"PeriodicalIF":0.0,"publicationDate":"2017-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75735227","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 : 2017-01-01DOI: 10.1109/CONFLUENCE.2017.7943225
N. Srivastav, S. Agrwal, S. Gupta, Saurabh R. Srivastava, Blessy Chacko, Hema Sharma
Object Detection and Tracking in video has applied in robotics, video-surveillance; human-computer interaction etc. and different approach of object detection e.g. Background subtraction, frame differencing. Motion based recognition is one of the methods to detect objects in sequence of image. In this method, a video sequence containing a large number of images is used to extract motion information. Two frame differencing is very easy but there is problem of holes. Three frame differencing and background subtraction have solved the problem of holes of two frames till a limit. Background subtraction is used for stable background video but Dynamic Background subtraction is capable to detect object in video with gradual background changes. So there is scope of work such that holes problem should be reduced more and object should be detected better in dynamic changes in background. In this paper, the proposed technique is able to reduce the holes problem in dynamic background updating video.
{"title":"Hybrid object detection using improved three frame differencing and background subtraction","authors":"N. Srivastav, S. Agrwal, S. Gupta, Saurabh R. Srivastava, Blessy Chacko, Hema Sharma","doi":"10.1109/CONFLUENCE.2017.7943225","DOIUrl":"https://doi.org/10.1109/CONFLUENCE.2017.7943225","url":null,"abstract":"Object Detection and Tracking in video has applied in robotics, video-surveillance; human-computer interaction etc. and different approach of object detection e.g. Background subtraction, frame differencing. Motion based recognition is one of the methods to detect objects in sequence of image. In this method, a video sequence containing a large number of images is used to extract motion information. Two frame differencing is very easy but there is problem of holes. Three frame differencing and background subtraction have solved the problem of holes of two frames till a limit. Background subtraction is used for stable background video but Dynamic Background subtraction is capable to detect object in video with gradual background changes. So there is scope of work such that holes problem should be reduced more and object should be detected better in dynamic changes in background. In this paper, the proposed technique is able to reduce the holes problem in dynamic background updating video.","PeriodicalId":6651,"journal":{"name":"2017 7th International Conference on Cloud Computing, Data Science & Engineering - Confluence","volume":"4 1","pages":"613-617"},"PeriodicalIF":0.0,"publicationDate":"2017-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81198726","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 : 2017-01-01DOI: 10.1109/CONFLUENCE.2017.7943194
Rohit Chourasia, R. Boghey
The MANET that is Mobile Ad hoc Network are forming a group of many nodes. They can interact with each other in limited area. All the Malicious nodes present in the MANET always disturb the usual performance of routing and that cause the degradation of dynamic performance of the network. Nodes which are malicious continuously try to stump the neighbor nodes during the process of routing as all neighbor nodes in the network merely forward the reply and response of neighboring. The intermediate nodes work is very responsible in routing procedure with continuous movement. During the work we have recommended one security scheme against the attack of packet dropping by malicious node in the network. The scheme which is recommended here will work to find attacker by using the concept of detection of link to forward the data or information between sender and receiver. The packet dropping on link, through node is detected and prevented by IDS security system. The scheme not only works to identify the nodes performing malicious activity however prevent them also. The identification of attacker is noticed by dropping of data packets in excsssessive quantity. The prevention of it can be done via choosing the alternate route somewhere the attacker performing malicious activity not available among the senders to receivers. The neighbor nodes or intermediary identify the malicious activity performer by the way of reply of malicious nodes which is confirmed. The recommended IDS system secures the network and also increases the performance after blocking malicious nodes that perform malicious activity in the network. The network performance measures in the presence of attack and secure IDS with the help of performance metrics like PDR, throughput etc. Planned secure routing improves data receiving and minimizes dropping data in network.
{"title":"Novel IDS security against attacker routing misbehavior of packet dropping in MANET","authors":"Rohit Chourasia, R. Boghey","doi":"10.1109/CONFLUENCE.2017.7943194","DOIUrl":"https://doi.org/10.1109/CONFLUENCE.2017.7943194","url":null,"abstract":"The MANET that is Mobile Ad hoc Network are forming a group of many nodes. They can interact with each other in limited area. All the Malicious nodes present in the MANET always disturb the usual performance of routing and that cause the degradation of dynamic performance of the network. Nodes which are malicious continuously try to stump the neighbor nodes during the process of routing as all neighbor nodes in the network merely forward the reply and response of neighboring. The intermediate nodes work is very responsible in routing procedure with continuous movement. During the work we have recommended one security scheme against the attack of packet dropping by malicious node in the network. The scheme which is recommended here will work to find attacker by using the concept of detection of link to forward the data or information between sender and receiver. The packet dropping on link, through node is detected and prevented by IDS security system. The scheme not only works to identify the nodes performing malicious activity however prevent them also. The identification of attacker is noticed by dropping of data packets in excsssessive quantity. The prevention of it can be done via choosing the alternate route somewhere the attacker performing malicious activity not available among the senders to receivers. The neighbor nodes or intermediary identify the malicious activity performer by the way of reply of malicious nodes which is confirmed. The recommended IDS system secures the network and also increases the performance after blocking malicious nodes that perform malicious activity in the network. The network performance measures in the presence of attack and secure IDS with the help of performance metrics like PDR, throughput etc. Planned secure routing improves data receiving and minimizes dropping data in network.","PeriodicalId":6651,"journal":{"name":"2017 7th International Conference on Cloud Computing, Data Science & Engineering - Confluence","volume":"35 1","pages":"456-460"},"PeriodicalIF":0.0,"publicationDate":"2017-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80319633","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}
Researches in the area of environmental niche modeling has been using climatic parameters in modeling niches of bird species. However, local experts believe that human activity is a great cont ributor to the birds' habitat status — a condition not often tested on niche model accuracy. Genetic Algorithm for Rule-set Production (GARP) and Maximum Entropy (MaxEnt) are two of the most commonly used and efficient methods in niche modeling using climatic data. In conjunction, this study aims to test the accuracy of the bird niche models produced by both GARP and MaxEnt when dealing with human-related parameters. Bird sightings of six endangered Philippine bird species found in Negros were used for the study. Niche models/prediction models from GARP and MaxEnt underwent partial-area ROC analysis for model evaluation. Results of the tests show that the prediction models of the two niche modeling algorithms are mostly good and positive predictions with GARP showing more accurate results than MaxEnt. In addition, GARP showed lower accuracy results when human-related parameters were introduced as compared to having no human-related parameters during the modeling phase. MaxEnt, on the other hand, showed accuracy improvements when the parameters were used. MaxEnt was also proven to be an ideal algorithm than GARP in dealing with species with very few occurrences.
{"title":"Niche modelling of endangered philippine birds using GARP and MAXENT","authors":"Chuchi Montenegro, Lorraine Allie Solitario, Samantha Faye Manglar, Daphne Danica Guinto","doi":"10.1109/CONFLUENCE.2017.7943211","DOIUrl":"https://doi.org/10.1109/CONFLUENCE.2017.7943211","url":null,"abstract":"Researches in the area of environmental niche modeling has been using climatic parameters in modeling niches of bird species. However, local experts believe that human activity is a great cont ributor to the birds' habitat status — a condition not often tested on niche model accuracy. Genetic Algorithm for Rule-set Production (GARP) and Maximum Entropy (MaxEnt) are two of the most commonly used and efficient methods in niche modeling using climatic data. In conjunction, this study aims to test the accuracy of the bird niche models produced by both GARP and MaxEnt when dealing with human-related parameters. Bird sightings of six endangered Philippine bird species found in Negros were used for the study. Niche models/prediction models from GARP and MaxEnt underwent partial-area ROC analysis for model evaluation. Results of the tests show that the prediction models of the two niche modeling algorithms are mostly good and positive predictions with GARP showing more accurate results than MaxEnt. In addition, GARP showed lower accuracy results when human-related parameters were introduced as compared to having no human-related parameters during the modeling phase. MaxEnt, on the other hand, showed accuracy improvements when the parameters were used. MaxEnt was also proven to be an ideal algorithm than GARP in dealing with species with very few occurrences.","PeriodicalId":6651,"journal":{"name":"2017 7th International Conference on Cloud Computing, Data Science & Engineering - Confluence","volume":"169 1","pages":"547-551"},"PeriodicalIF":0.0,"publicationDate":"2017-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77933587","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 : 2017-01-01DOI: 10.1109/CONFLUENCE.2017.7943159
N. Panwar, M. Rauthan
Cloud computing is an advanced and nascent technology, which permits convenient access to data, software and IT services over the web. It allows users to pay on the basis of use and has the high performance. Virtualization technology segregates the elementary functions of computers from the hardware resources and the physical infrastructure. Virtualization technology considered principal characteristic of cloud computing. Cloud computing is a conglomerate system which uses more than one kind of system processors and grasps large volume of application data. With the increasing number of cloud users, it becomes difficult to schedule user tasks effectively. The performance of cloud depends on the task scheduling algorithms. Since cloud computing systems have an abundance of uncertainty with respect to network bandwidth and resource availability, scheduling algorithms which are being used in cloud computing environment should consolidate the dormancy caused by uncertain resource availability. The task scheduling problem can be specified as the process of fin ding an ideal mapping amongst subtasks of different tasks and available set of resources, with the intention of achieving the desired objectives. This paper is aimed to perform comparative study of different existing task scheduling algorithms by categorizing each into the different scheduling techniques, i.e., Heuristic, Deadline, Priority and Optimization based in order to find their suitability, feasibility and adaptability.
{"title":"Analysis of various task scheduling algorithms in cloud environment: Review","authors":"N. Panwar, M. Rauthan","doi":"10.1109/CONFLUENCE.2017.7943159","DOIUrl":"https://doi.org/10.1109/CONFLUENCE.2017.7943159","url":null,"abstract":"Cloud computing is an advanced and nascent technology, which permits convenient access to data, software and IT services over the web. It allows users to pay on the basis of use and has the high performance. Virtualization technology segregates the elementary functions of computers from the hardware resources and the physical infrastructure. Virtualization technology considered principal characteristic of cloud computing. Cloud computing is a conglomerate system which uses more than one kind of system processors and grasps large volume of application data. With the increasing number of cloud users, it becomes difficult to schedule user tasks effectively. The performance of cloud depends on the task scheduling algorithms. Since cloud computing systems have an abundance of uncertainty with respect to network bandwidth and resource availability, scheduling algorithms which are being used in cloud computing environment should consolidate the dormancy caused by uncertain resource availability. The task scheduling problem can be specified as the process of fin ding an ideal mapping amongst subtasks of different tasks and available set of resources, with the intention of achieving the desired objectives. This paper is aimed to perform comparative study of different existing task scheduling algorithms by categorizing each into the different scheduling techniques, i.e., Heuristic, Deadline, Priority and Optimization based in order to find their suitability, feasibility and adaptability.","PeriodicalId":6651,"journal":{"name":"2017 7th International Conference on Cloud Computing, Data Science & Engineering - Confluence","volume":"114 1","pages":"255-261"},"PeriodicalIF":0.0,"publicationDate":"2017-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88130694","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 : 2017-01-01DOI: 10.1109/CONFLUENCE.2017.7943134
P. Agarwalla, S. Mukhopadhyay
Particle swarm optimization (PSO) is a stochastic optimization algorithm which usually suffers from local confinement losing its diversity. In this paper, we have proposed an efficient coordinator guided PSO (ECG-PSO), which provides a good diversity to the swarms maintaining good convergence speed and hence improves the fitness and robustness of the technique. We comprehensively evaluate the performance of the ECG-PSO by applying it on real-parameter benchmark optimization functions. Again, the result of comparison shows that ECG-PSO is more efficient compared to other PSO variants for solving complex problems.
{"title":"Efficient coordinator guided particle swarm optimization for real-parameter optimization","authors":"P. Agarwalla, S. Mukhopadhyay","doi":"10.1109/CONFLUENCE.2017.7943134","DOIUrl":"https://doi.org/10.1109/CONFLUENCE.2017.7943134","url":null,"abstract":"Particle swarm optimization (PSO) is a stochastic optimization algorithm which usually suffers from local confinement losing its diversity. In this paper, we have proposed an efficient coordinator guided PSO (ECG-PSO), which provides a good diversity to the swarms maintaining good convergence speed and hence improves the fitness and robustness of the technique. We comprehensively evaluate the performance of the ECG-PSO by applying it on real-parameter benchmark optimization functions. Again, the result of comparison shows that ECG-PSO is more efficient compared to other PSO variants for solving complex problems.","PeriodicalId":6651,"journal":{"name":"2017 7th International Conference on Cloud Computing, Data Science & Engineering - Confluence","volume":"15 1","pages":"118-123"},"PeriodicalIF":0.0,"publicationDate":"2017-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84327169","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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