2019 International Conference on Advances in Computing and Communication Engineering (ICACCE)最新文献_第3页

Performance Analysis of Bellman Ford, AODV, DSR, ZRP and DYMO Routing Protocol in MANET using EXATA 基于EXATA的MANET中Bellman Ford、AODV、DSR、ZRP和DYMO路由协议的性能分析

2019 International Conference on Advances in Computing and Communication Engineering (ICACCE) Pub Date : 2019-04-01 DOI: 10.1109/ICACCE46606.2019.9079958

K. P. Sampoornam, G. R. Darshini

引用次数: 4

Spectral Expansion Method for Cloud Reliability Analysis 云可靠性分析的谱展开方法

2019 International Conference on Advances in Computing and Communication Engineering (ICACCE) Pub Date : 2019-04-01 DOI: 10.1109/ICACCE46606.2019.9080012

K. Karthikeyan, A. Bharathi

{"title":"Spectral Expansion Method for Cloud Reliability Analysis","authors":"K. Karthikeyan, A. Bharathi","doi":"10.1109/ICACCE46606.2019.9080012","DOIUrl":"https://doi.org/10.1109/ICACCE46606.2019.9080012","url":null,"abstract":"Cloud Computing is a computing hypothesis, where a huge group of systems linked together in private, public or hybrid network, to offer dynamically amendable infrastructure for data storage, file storage and application. With this emerging technology, application hosting, delivery, content storage, and reduced computation cost, and it acts as an essential module for backbone of the Internet of Things (IOT). The efficiency of cloud Service providers (CSP) could be improved by considering significant factors such as availability, reliability, usability, security, responsiveness, and elasticity. Assessment of these factors leads to efficiency in designing scheduler for CSP. This metrics also improved the Quality of Service (QoS) in cloud. Many existing model and approaches evaluate this metrics. But these existing approaches doesn't offer efficient outcome. In this paper, a prominent performance model named as Spectral Expansion Method (SPM) evaluates cloud reliability. Spectral expansion Method (SPM) is a huge technique useful in reliability and performance modelling of computing system. This approach solves the Markov model of Cloud service Provider (CSP) to predict the reliability. The SPM is better compared to matrix geometric methods.","PeriodicalId":317123,"journal":{"name":"2019 International Conference on Advances in Computing and Communication Engineering (ICACCE)","volume":"172 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133584895","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

Big Data retrieval techniques based on Hash Indexing and MapReduce approach with NoSQL Database 基于哈希索引和MapReduce方法的NoSQL数据库大数据检索技术

2019 International Conference on Advances in Computing and Communication Engineering (ICACCE) Pub Date : 2019-04-01 DOI: 10.1109/ICACCE46606.2019.9079964

N. Gayathiri, D. D. Jaspher, A. Natarajan

引用次数: 1

An Efficient K-Means Clustering Initialization Using Optimization Algorithm 基于优化算法的高效k -均值聚类初始化

2019 International Conference on Advances in Computing and Communication Engineering (ICACCE) Pub Date : 2019-04-01 DOI: 10.1109/ICACCE46606.2019.9079998

V. Divya, R. Deepika, C. Yamini, P. Sobiyaa

{"title":"An Efficient K-Means Clustering Initialization Using Optimization Algorithm","authors":"V. Divya, R. Deepika, C. Yamini, P. Sobiyaa","doi":"10.1109/ICACCE46606.2019.9079998","DOIUrl":"https://doi.org/10.1109/ICACCE46606.2019.9079998","url":null,"abstract":"In data mining has a lot of technique for knowledge discovery. In this Clustering method is very well technique for unsupervised learning. It's important objective is to find a high-quality cluster where the distance between clusters are maximal and the distance in the cluster is minimal. K-means algorithm is applied in this paper for its simplicity. It has been widely discussed and applied in pattern recognition and machine learning. However, the K-means algorithm could not guarantee unique clustering results for the same dataset because its initial cluster centers are select randomly. To avoid such issues a new initialization method is proposed in the Improved K-means algorithm with Cuckoo Search algorithm. The proposed method uses different numerical datasets like iris, wine and solar datasets (Ames, Chariton stations). The K-means clustering solutions are comparable with cuckoo search initialization methods using different measures such as Accuracy, Precision and Recall, F1-score, Silhouette value and MSE (Mean Square Error). The experimental solution represents the effectiveness of the proposed method.","PeriodicalId":317123,"journal":{"name":"2019 International Conference on Advances in Computing and Communication Engineering (ICACCE)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126064407","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: 3

Electromagnetic Band Gap Structure Applications in Modern Wireless Perspective: A Review 电磁带隙结构在现代无线中的应用综述

2019 International Conference on Advances in Computing and Communication Engineering (ICACCE) Pub Date : 2019-04-01 DOI: 10.1109/ICACCE46606.2019.9079992

Priyanka Dalal, S. Dhull

引用次数: 4

DCSim: Cooling Energy Aware VM Allocation Framework for a Cloud Data Center DCSim:云数据中心的散热节能虚拟机分配框架

2019 International Conference on Advances in Computing and Communication Engineering (ICACCE) Pub Date : 2019-04-01 DOI: 10.1109/ICACCE46606.2019.9079962

Priyank Bhandia, R. S. Anupindi, Pavan Yekbote, N. Singh, H. L. Phalachandra, D. Sitaram

{"title":"DCSim: Cooling Energy Aware VM Allocation Framework for a Cloud Data Center","authors":"Priyank Bhandia, R. S. Anupindi, Pavan Yekbote, N. Singh, H. L. Phalachandra, D. Sitaram","doi":"10.1109/ICACCE46606.2019.9079962","DOIUrl":"https://doi.org/10.1109/ICACCE46606.2019.9079962","url":null,"abstract":"Explosion of digital content has resulted in large amounts of resources being provisioned and managed for various applications in cloud Data Centers. Energy consumption in these large Cloud Data Centers is a rising concern, accounting for 1.3% of the worlds electricity consumption [1]. Data Center cooling accounts for 40% of this energy consumption [2]. Of the various mechanisms available for studying the energy consumption in Data Centers, a simulation based approach is quite popular. In this paper, we propose DCSim, a configurable extension to CloudSim, a popularly used cloud infrastructure and simulation framework. CloudSim provides coarse power models to calculate total energy consumption in a Data Center for a given workload, but has no provision to factor in the Data Center topology and current cooled area into this power model. This makes building intelligent cooling energy aware allocation policies in CloudSim difficult. In DCSim, we introduce a novel Data Center model that addresses the shortcomings of CloudSim by encapsulating concepts of Racks, Aisles, Sectors and Zones (collectively referred to as DCObjects). We also provide the capability to model the cooling of these DCObjects. This makes the study of cooling aware resource provisioning for workloads easier. The DCObjects and the Data Center model presented are designed to be fully extensible to support future developments in this area. In this work we also implement a Cooling aware VM allocation policy, and demonstrate using multiple algorithms, that this VM allocation policy will effectively reduce the total Data Center power consumption by 18.18% over an algorithm which does not factor in the cooled DCObjects.","PeriodicalId":317123,"journal":{"name":"2019 International Conference on Advances in Computing and Communication Engineering (ICACCE)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131375110","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: 2

A Novel Noise Removal in Digital Mammograms based on Statistical Algorithms 一种新的基于统计算法的数字乳房x线照片去噪方法

2019 International Conference on Advances in Computing and Communication Engineering (ICACCE) Pub Date : 2019-04-01 DOI: 10.1109/ICACCE46606.2019.9079990

S. Chakravarthy, H. Rajaguru

引用次数: 0

Scaling and Parallelization of Big Data Analysis on HPC and Cloud Systems HPC和云系统上大数据分析的扩展和并行化

2019 International Conference on Advances in Computing and Communication Engineering (ICACCE) Pub Date : 2019-04-01 DOI: 10.1109/ICACCE46606.2019.9079987

Mike Mikailov, N. Petrick, Yasameen Azarbaijani, Fu-Jyh Luo, Lohit Valleru, Stephen Whitney, Yelizaveta Torosyan

{"title":"Scaling and Parallelization of Big Data Analysis on HPC and Cloud Systems","authors":"Mike Mikailov, N. Petrick, Yasameen Azarbaijani, Fu-Jyh Luo, Lohit Valleru, Stephen Whitney, Yelizaveta Torosyan","doi":"10.1109/ICACCE46606.2019.9079987","DOIUrl":"https://doi.org/10.1109/ICACCE46606.2019.9079987","url":null,"abstract":"Big data analysis can exhibit significant scaling problems when migrated to High Performance Computing (HPC) clusters and/or cloud computing platforms if traditional software parallelization techniques such as POSIX multi-threading and Message Passing Interface (MPI) are used. This paper introduces a novel scaling technique based on a-well-known array job mechanism to enable a team of FDA researchers to validate a method for identifying evidence of possible adverse events in very large sets of patient medical records. The analysis employed the widely-used basic Statistical Analysis Software (SAS) package, and the proposed parallelization approach dramatically increased the scaling and thus the speed of job completion for this application and is applicable to any similar software written in any other programming language. The new scaling technique offers O(T) theoretical speedup in comparison to multi-threading and MPI techniques. Here T is the number of array job tasks. The basis of the new approach is the segmentation of both (a) the big data set being analyzed and (b) the large number of SAS analysis types applied to each data segment. The large number of unique pairs of data set segment and analysis type segment are then each processed by a separate computing node (core) in pseudo-parallel manner. As a result, a SAS big data analysis which required more than 10 days to complete and consumed more than a terabyte of RAM on a single multi-core computing node completed in less than an hour parallelized over a large number of nodes, none of which needed more than 50 GB of RAM. The massive increase in the number of tasks when running an analysis job with this degree of segmentation reduces the size, scope and execution time of each task. Besides the significant speed improvement, additional benefits include fine-grained checkpointing and increased flexibility of job submission.","PeriodicalId":317123,"journal":{"name":"2019 International Conference on Advances in Computing and Communication Engineering (ICACCE)","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114745920","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

Comparison of Yolo, SSD, Faster RCNN for Real Time Tennis Ball Tracking for Action Decision Networks Yolo, SSD, Faster RCNN在动作决策网络中实时网球跟踪的比较

2019 International Conference on Advances in Computing and Communication Engineering (ICACCE) Pub Date : 2019-04-01 DOI: 10.1109/ICACCE46606.2019.9079965

R. Deepa, E. Tamilselvan, ES Abrar, Shrinivas Sampath

引用次数: 21

Solar Radiation Forecasting Using Support Vector Regression 基于支持向量回归的太阳辐射预报

2019 International Conference on Advances in Computing and Communication Engineering (ICACCE) Pub Date : 2019-04-01 DOI: 10.1109/ICACCE46606.2019.9080008

Subham Shaw, M. Prakash

引用次数: 5