2014 International Conference on Parallel, Distributed and Grid Computing最新文献_第6页

Performance analysis of trust based geographic routing protocols for Wireless Sensor Networks 基于信任的无线传感器网络地理路由协议性能分析

2014 International Conference on Parallel, Distributed and Grid Computing Pub Date : 2014-12-01 DOI: 10.1109/PDGC.2014.7030763

P. Vamsi, K. Kant

引用次数: 3

A state level policy framework for integrating DFaaS with E-Governance 用于将DFaaS与电子治理集成的州级政策框架

2014 International Conference on Parallel, Distributed and Grid Computing Pub Date : 2014-12-01 DOI: 10.1109/PDGC.2014.7030733

S. Sarkar, Suvrojit Das

引用次数: 4

Effect of parallelization on conditional summation 并行化对条件求和的影响

2014 International Conference on Parallel, Distributed and Grid Computing Pub Date : 2014-12-01 DOI: 10.1109/PDGC.2014.7030765

Shipra Pradhan, Sanjay Kumar, V. Patle

引用次数: 0

Comparative analysis of segmentation algorithms for leukocyte extraction in the acute Lymphoblastic Leukemia images 急性淋巴细胞白血病图像中白细胞提取分割算法的比较分析

2014 International Conference on Parallel, Distributed and Grid Computing Pub Date : 2014-12-01 DOI: 10.1109/PDGC.2014.7030750

Jyoti Rawat, A. Singh, H. Bhadauria, I. Kumar

引用次数: 20

A modified low power auto-adaptation unit for reconfigurable analog to digital converter in wireless applications 一种改进的低功耗自适应单元，用于无线应用中可重构的模数转换器

2014 International Conference on Parallel, Distributed and Grid Computing Pub Date : 2014-12-01 DOI: 10.1109/PDGC.2014.7030776

R. Singh, Ritesh Rampal, Piyush Pradhan, A. Puri, R. Chandel

引用次数: 0

Segregated receive and relay scheme for communication in wireless sensor networks 无线传感器网络中分离的通信接收与中继方案

2014 International Conference on Parallel, Distributed and Grid Computing Pub Date : 2014-12-01 DOI: 10.1109/PDGC.2014.7030718

Swati Saini, Surinder Singh Khurana

引用次数: 0

Secure communication using memristor based chaotic circuit 利用基于忆阻器的混沌电路进行安全通信

2014 International Conference on Parallel, Distributed and Grid Computing Pub Date : 2014-12-01 DOI: 10.1109/PDGC.2014.7030734

S. Saini, J. Saini

引用次数: 19

Energy efficient modified SPIN protocol with high security in Wireless Sensor Networks using TOSSIM 在使用TOSSIM的无线传感器网络中，节能且高安全性的改进SPIN协议

2014 International Conference on Parallel, Distributed and Grid Computing Pub Date : 2014-12-01 DOI: 10.1109/PDGC.2014.7030758

R. Dutta, Shishir Gupta, Debraj Paul

引用次数: 11

Enhancing cloud computing reliability using efficient scheduling by providing reliability as a service 通过提供可靠性即服务，利用高效调度提高云计算可靠性

2014 International Conference on Parallel, Distributed and Grid Computing Pub Date : 2014-12-01 DOI: 10.1109/PDGC.2014.7030723

Abishi Chowdhury, Priyanka Tripathi

{"title":"Enhancing cloud computing reliability using efficient scheduling by providing reliability as a service","authors":"Abishi Chowdhury, Priyanka Tripathi","doi":"10.1109/PDGC.2014.7030723","DOIUrl":"https://doi.org/10.1109/PDGC.2014.7030723","url":null,"abstract":"Cloud computing is one of the prime need of today's IT world. Organizations are shifting to the cloud rapidly in order to increase their overall benefits. Cloud computing has virtualization as its backbone. Cloud resources are provided on demand using the Internet and an on-running migration of resources is done by the cloud service provider. Failure is the nature of hardware and same for the software. Therefore, cloud resources can also fail at any time and leave impact on the performance of the other resources. That is why; it is required for cloud computing to incorporate a monitoring system. In a dynamic computing environment like cloud computing, it is hard to maintain and analyse the reliability of the resources. As cloud is a combination of different resources acting together to provide services to the end users, Virtual machines are the key to provide the Infrastructure as a Service (IaaS) to these end users. In this paper a novel attempt is made to propose a reliability computing technique to calculate the reliability of a Cloud data centre. Further, in this paper we have proposed a mechanism for continuous updating of cloud resources' reliability and providing a reliable scheduling of the resources to the cloud users in a cloud computing environment.","PeriodicalId":311953,"journal":{"name":"2014 International Conference on Parallel, Distributed and Grid Computing","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134407156","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: 23

Data analyzing using Map-Join-Reduce in cloud storage 基于Map-Join-Reduce的云存储数据分析

2014 International Conference on Parallel, Distributed and Grid Computing Pub Date : 2014-12-01 DOI: 10.1109/PDGC.2014.7030773

R. Bhardwaj, Neetesh Mishra, Rajiv Kumar

{"title":"Data analyzing using Map-Join-Reduce in cloud storage","authors":"R. Bhardwaj, Neetesh Mishra, Rajiv Kumar","doi":"10.1109/PDGC.2014.7030773","DOIUrl":"https://doi.org/10.1109/PDGC.2014.7030773","url":null,"abstract":"Data analysis and maintenance in cloud computing is a challenging task which allows large volume of data to be processed in large clusters. Recent days Map Reduce Model have shown great value in processing huge amount of data on very large clusters. Map Reduce paradigm consists of two phases, mapper and reducer. Mapper performs filtering criteria and Reducer performs aggregation task, but Map Reduce supports a homogenous data set that signifies the same filtering logic is applied by mapper function on each tuple in the data set. However these techniques do not performed well in case of complex data analysis that may require the joining of multiple data sets. In order to improve these problems a CloudView framework has been proposed for data storage, processing and analyzing the massive machine data which are collected from cloud environment in which Case Based Reasoning (CBR) approach is used for fault prediction. In this paper, an Enhanced CloudView (ECV) framework has been proposed for data processing, maintenance and analyzing the massive machine data. CloudView is formulated by Map Reduce model whereas ECV framework will use Map-Join-Reduce model. This model will performs mapping-join-reduction task in two successive Map Reduce jobs. First it will filter the logic to all the datasets in parallel, joins the resulted tuple and further reduces for final aggregation and finally, it combines all partial aggregation results and produce the final result. This additional joiner model will perform a fast processing in a heterogeneous data set by using join reduce function, which will improve the efficiency and scalability of the system.","PeriodicalId":311953,"journal":{"name":"2014 International Conference on Parallel, Distributed and Grid Computing","volume":"60 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132074401","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