2013 8th ChinaGrid Annual Conference最新文献

A Cloud Resource Allocation Scheme Based on Microeconomics and Wind Driven Optimization 基于微观经济学和风驱动优化的云资源分配方案

2013 8th ChinaGrid Annual Conference Pub Date : 2013-08-22 DOI: 10.1109/CHINAGRID.2013.11

Jiajia Sun, Xingwei Wang, Min Huang, Chengxi Gao

{"title":"A Cloud Resource Allocation Scheme Based on Microeconomics and Wind Driven Optimization","authors":"Jiajia Sun, Xingwei Wang, Min Huang, Chengxi Gao","doi":"10.1109/CHINAGRID.2013.11","DOIUrl":"https://doi.org/10.1109/CHINAGRID.2013.11","url":null,"abstract":"As a new model of distributed computing, all kinds of distributed resources are virtualized to establish a shared resource pool through cloud computing. The target of cloud computing is to provide convenient and configurable resource for users with pay-per-usage charging model. Therefore, the reasonable and efficient mechanism for resource allocating is becoming a hot spot in research. According to the features of cloud resource allocation, methods of auction model, neural network and intelligent optimization are comprehensively applied in this paper for proposing a double multi-attribute auction based cloud resource allocation mechanism. In the mechanism, non-price attributes like quality of experience, level of delivery, level of payment and level of spiteful quote are described for better satisfying the requirements of users, and these attributes are transferred to an index of quality through BP neural network. Based on the history information of auction, support vector machine algorithm is utilized to predict the price in advance. In the end, using satisfaction extracted from index of quality and price as optimization goal, wind driven optimization algorithm is adopted to get the optimized allocation scheme. Simulation results have shown that the mechanism is feasible and effective.","PeriodicalId":251153,"journal":{"name":"2013 8th ChinaGrid Annual Conference","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121458697","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: 9

A Multiple Attribute Decision and Bidding Based Cloud Resource Dynamic Allocation Method 基于多属性决策和竞价的云资源动态分配方法

2013 8th ChinaGrid Annual Conference Pub Date : 2013-08-22 DOI: 10.1109/CHINAGRID.2013.15

Xueyi Wang, Xingwei Wang, Min Huang

{"title":"A Multiple Attribute Decision and Bidding Based Cloud Resource Dynamic Allocation Method","authors":"Xueyi Wang, Xingwei Wang, Min Huang","doi":"10.1109/CHINAGRID.2013.15","DOIUrl":"https://doi.org/10.1109/CHINAGRID.2013.15","url":null,"abstract":"Cloud computing is on demand as it offers dynamic flexible resource allocation for reliable and guaranteed services in a pay-as-you-use manner to public. In cloud computing, a cloud resource consumer can request a number of cloud resources simultaneously. So there must be a provision that all resources are made available to requesting cloud resource consumers in an efficient manner to satisfy their need. In this paper, an intelligent combinatorial auction based resource allocation mechanism is proposed in a cloud environment. First, the system frameworks of resource allocation mechanism are constructed, and then tender descriptions are given for four kinds of common resources. Second, a support vector regression (SVM) based method is adopted to convert a combination resource demand into multiple single resource demands. Third, emotional parameter is introduced to bidding strategy and tender evaluation mechanism is designed to determine an optimal tender of cloud resource provider. Finally, a reputation system is introduced to deal with potential malicious behaviors in the auction market. Simulation results show that the mechanism is feasible and effective.","PeriodicalId":251153,"journal":{"name":"2013 8th ChinaGrid Annual Conference","volume":"68 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115749011","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: 4

An Incremental Crawler for Web Video Based on Content Longevity 基于内容寿命的网络视频增量爬虫

2013 8th ChinaGrid Annual Conference Pub Date : 2013-08-22 DOI: 10.1109/CHINAGRID.2013.16

Feng Lu, Zaiyang Tang, Xiaofei Liao, Hai Jin

引用次数: 0

Alovera: A Fast Stream Processing System for Large-Scale Data Alovera:大规模数据的快速流处理系统

2013 8th ChinaGrid Annual Conference Pub Date : 2013-08-22 DOI: 10.1109/CHINAGRID.2013.9

Zhen'an Zhang, Dongjie Zhang, Xiaopeng Yu, Jing Wang, Chunjiang He, Pingpeng Yuan, Hai Jin

引用次数: 1

A Conceptual Prototype for Digital Media Cloud 数字媒体云的概念原型

2013 8th ChinaGrid Annual Conference Pub Date : 2013-08-22 DOI: 10.1109/CHINAGRID.2013.22

W. Tan, Xiaoshi Wang, Yu Zhang, Bin Zhou, Xiaowu Chen

引用次数: 1

A New Schedule Strategy for Heterogenous Workload-aware in Hadoop Hadoop异构工作负载感知的一种新的调度策略

2013 8th ChinaGrid Annual Conference Pub Date : 2013-08-22 DOI: 10.1109/CHINAGRID.2013.21

Zhe Wang, Zhengdong Zhu, Pengfei Zheng, Qiang Liu, Xiaoshe Dong

{"title":"A New Schedule Strategy for Heterogenous Workload-aware in Hadoop","authors":"Zhe Wang, Zhengdong Zhu, Pengfei Zheng, Qiang Liu, Xiaoshe Dong","doi":"10.1109/CHINAGRID.2013.21","DOIUrl":"https://doi.org/10.1109/CHINAGRID.2013.21","url":null,"abstract":"Demand for large-scale data mining and data analysis has led both industry and academia to design highly scalable data-intensive computing platforms. MapReduce is a well-known programming model to process large amount of data. However, current implementations perform poorly and are inefficient, even to run a single MapReduce job. To manage and process enormous data, multi-jobs instead of single job, running in the platform. Different research and different Job processing, there are different characters in request and utilization of resources. Most schedule strategy applied in Hadoop ignores these differences, so resources utilization rate and job processing efficiencies may be impaired. As to this problem, we put forward a schedule strategy based on job type classification. In this paper, we put forward a schedule strategy based on job type classification. This schedule strategy includes two parts. 1) Divide the job dynamically into two types based on cluster historical operating data: CPU-intensive and I/O-intensive. 2) To remove the influence of noise data on the reliability of historical data, we put forward a schedule strategy-- CICS (CPU and I/O Characteristic Estimation Strategy. That is mainly based on classical FCFS and has been modified intensively on Fairness.","PeriodicalId":251153,"journal":{"name":"2013 8th ChinaGrid Annual Conference","volume":"92 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123317144","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: 4

Online Redundancy Mining in Enterprise WLAN Traffic 企业WLAN流量的在线冗余挖掘

2013 8th ChinaGrid Annual Conference Pub Date : 2013-08-22 DOI: 10.1109/CHINAGRID.2013.14

Zhiqi Bian, Hongzi Zhu, Guangtao Xue, Minglu Li

{"title":"Online Redundancy Mining in Enterprise WLAN Traffic","authors":"Zhiqi Bian, Hongzi Zhu, Guangtao Xue, Minglu Li","doi":"10.1109/CHINAGRID.2013.14","DOIUrl":"https://doi.org/10.1109/CHINAGRID.2013.14","url":null,"abstract":"As various types of mobile devices (e.g., smart phones, laptops and tablets) get connected via Wireless Local Area Networks (WLANs), the dramatic demands for wireless bandwidth have posed new challenges for efficient operation and maintenance of enterprise WLANs. Previous studies have found certain degree of redundancy embedded in user data, which stimulates new redundancy elimination schemes implemented on gateways to restrain redundant data being transmitted within one enterprise WLAN. Due to both the computation and storage limitations of gateways, it is very hard to process all user data online and sampling methods are adopted to shrink the size of data streams. Existing methods simply sample the original user data in a random way, leading to low efficiency of finding redundant data. In this paper, we conduct an empirical study on effective sampling strategies using real user trace collected from a university WLAN. We first investigate the extent to which WLAN traffic can be redundant. We then further analyze the distribution characteristics of redundant blocks and find that the position of redundant chunks exhibit strong spatial correlations with previous ones. Our observations thus provide solid foundation for designing new sampling schemes which can capture more redundant data embedded in WLAN data and improve the performance of redundancy elimination schemes.","PeriodicalId":251153,"journal":{"name":"2013 8th ChinaGrid Annual Conference","volume":"58 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126263932","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

Feasibility of Deploying Biometric Encryption in Mobile Cloud Computing 在移动云计算中部署生物特征加密的可行性

2013 8th ChinaGrid Annual Conference Pub Date : 2013-08-22 DOI: 10.1109/CHINAGRID.2013.10

Kao Zhao, Hai Jin, Deqing Zou, Gang Chen, Weiqi Dai

{"title":"Feasibility of Deploying Biometric Encryption in Mobile Cloud Computing","authors":"Kao Zhao, Hai Jin, Deqing Zou, Gang Chen, Weiqi Dai","doi":"10.1109/CHINAGRID.2013.10","DOIUrl":"https://doi.org/10.1109/CHINAGRID.2013.10","url":null,"abstract":"Biometric Encryption (BE) is a new issue in the information security field which is based on biometric identification and cryptosystems and it will be a key research subject in the future. At the same time the development of cloud computing is not restricted to PC anymore, some cloud computing services for mobile terminals such as cell phones have already appeared due to the booming of mobile internet. However, security problems still exists in mobile cloud computing (MCC) inherited from cloud computing and mechanisms are needed to cope with these problems. In this paper, we examine various aspects of using BE in MCC to solve security problems: first, we describe the background and related work of BE and MCC, and second we demonstrate several scenarios of deploying BE in MCC. Specifically, we analyze the new critical issues in these situations that can be used by an adversary to disable the operation of cloud computing environment. We then discuss different BE methods that applying in MCC, and explore scenarios where each method shows its strengths and weaknesses. In particular, we address the problem of using BE to protect privacy for users in MCC. Further, we propose an advanced protocol that employ BE for transferring private data in cloud computing environment, and design a conceptual cloud platform supporting biometric authentication to serve as future data center.","PeriodicalId":251153,"journal":{"name":"2013 8th ChinaGrid Annual Conference","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128033313","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: 16

Enabling Data Recommendation in Scientific Workflow Based on Provenance 基于来源的科学工作流数据推荐实现

2013 8th ChinaGrid Annual Conference Pub Date : 2013-08-22 DOI: 10.1109/CHINAGRID.2013.25

Xing Huang, T. Lu, X. Ding, Ning Gu

引用次数: 3

An Efficient Public Batch Auditing Protocol for Data Security in Multi-cloud Storage 面向多云存储数据安全的高效公共批处理审计协议

2013 8th ChinaGrid Annual Conference Pub Date : 2013-08-22 DOI: 10.1109/CHINAGRID.2013.13

He Kai, Chuanhe Huang, Jinhai Wang, Zhou Hao, Chen Xi, Yilong Lu, Lianzhen Zhang, Wang Bin

引用次数: 37