The skyline query is an efficient data analysis tool for multi-criteria decision making that has received significant attention in the database community. As multi-core architectures have gone mainstream, we present a new parallel skyline query algorithm that can be applied to multi-core and multiprocessor systems, to progressively return skyline points as they are identified efficiently. In this paper, we proposed a parallel skyline algorithm which can eliminate redundant computations and improve parallelism of the skyline query. Experimental results show that our algorithm successfully exploits the features of multiple cores to improve the performance of skyline computation for large high-dimensional datasets.
{"title":"Parallel Skyline Queries on Multi-core Systems","authors":"Meng-Zong Liou, Y. Shu, Wei-Mei Chen","doi":"10.1109/PDCAT.2013.51","DOIUrl":"https://doi.org/10.1109/PDCAT.2013.51","url":null,"abstract":"The skyline query is an efficient data analysis tool for multi-criteria decision making that has received significant attention in the database community. As multi-core architectures have gone mainstream, we present a new parallel skyline query algorithm that can be applied to multi-core and multiprocessor systems, to progressively return skyline points as they are identified efficiently. In this paper, we proposed a parallel skyline algorithm which can eliminate redundant computations and improve parallelism of the skyline query. Experimental results show that our algorithm successfully exploits the features of multiple cores to improve the performance of skyline computation for large high-dimensional datasets.","PeriodicalId":187974,"journal":{"name":"2013 International Conference on Parallel and Distributed Computing, Applications and Technologies","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121114696","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}
Traffic matrix describes the data flow between each pair of Origin-Destination (OD) over a measured period. However, it is very hard to be obtained in a large scale network. This paper compares two available diffusion operators. Based on the selection of good diffusion operator, we conduct multi-resolution analysis (MRA) on traffic matrices by diffusion wavelets. We also propose a method to detect the anomaly of the traffic matrix during a continuous period of time based on diffusion wavelet analysis.
{"title":"Multi-resolution Analysis on Traffic Matrix by Different Diffusion Operators","authors":"Binze Zhong, Hui Tian","doi":"10.1109/PDCAT.2013.26","DOIUrl":"https://doi.org/10.1109/PDCAT.2013.26","url":null,"abstract":"Traffic matrix describes the data flow between each pair of Origin-Destination (OD) over a measured period. However, it is very hard to be obtained in a large scale network. This paper compares two available diffusion operators. Based on the selection of good diffusion operator, we conduct multi-resolution analysis (MRA) on traffic matrices by diffusion wavelets. We also propose a method to detect the anomaly of the traffic matrix during a continuous period of time based on diffusion wavelet analysis.","PeriodicalId":187974,"journal":{"name":"2013 International Conference on Parallel and Distributed Computing, Applications and Technologies","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125043541","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}
Cloud computing is becoming increasingly popular due to its power in providing high-performance and flexible service capabilities. More and more internet users have accepted this innovative service model and been using various cloud-based services every day. However, these service-using data is quite valuable for marketing purposes, as it can reflect a user's interest and service-using pattern. Therefore, the privacy issues have been brought out. Recently, many studies focus on access control and other traditional security problems in cloud, and little studied on the topic of the private service data publishing. In this paper, we study the private service data publishing problem by representing the data with a hyper graph, which is quite efficient to illustrate complex relationships among users. We first formulate the problem with a popular background knowledge attack model named rank attack, and then provide an anonymization-based method to prevent the released data from such attacks. We also take data utility into consideration by defining specific information loss metrics. The performances of the methods have been validated by two sets of synthetic data.
{"title":"Preserving Private Cloud Service Data Based on Hypergraph Anonymization","authors":"Yuechuan Li, Yidong Li, Baopeng Zhang, Hong Shen","doi":"10.1109/PDCAT.2013.37","DOIUrl":"https://doi.org/10.1109/PDCAT.2013.37","url":null,"abstract":"Cloud computing is becoming increasingly popular due to its power in providing high-performance and flexible service capabilities. More and more internet users have accepted this innovative service model and been using various cloud-based services every day. However, these service-using data is quite valuable for marketing purposes, as it can reflect a user's interest and service-using pattern. Therefore, the privacy issues have been brought out. Recently, many studies focus on access control and other traditional security problems in cloud, and little studied on the topic of the private service data publishing. In this paper, we study the private service data publishing problem by representing the data with a hyper graph, which is quite efficient to illustrate complex relationships among users. We first formulate the problem with a popular background knowledge attack model named rank attack, and then provide an anonymization-based method to prevent the released data from such attacks. We also take data utility into consideration by defining specific information loss metrics. The performances of the methods have been validated by two sets of synthetic data.","PeriodicalId":187974,"journal":{"name":"2013 International Conference on Parallel and Distributed Computing, Applications and Technologies","volume":"64 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124428721","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}
Cloud computing has been developed in response to demand from companies seeking to deal with the execution cost of their complex distributed applications. Introducing the notion of hybrid clouds to the cloud computing paradigm brings out many challenges in resource provisioning for workflows. Hybrid clouds encounter the following two main obstacles in reaching their full potential: (1) customers' dissatisfaction due to the conflicting nature of the constraints (budget and deadline), and (2) exposure of customers' private data/jobs in hybrid cloud infrastructures. We believe that too little attention is paid to privacy issues for workflow scheduling under deadline constraint. Many algorithms exist to address the cost and time trade-off, however, they suffer from insufficient consideration of privacy. In this study, we present an algorithm that preserves privacy in scheduling of workflows, whilst still considering customers' deadlines and cost. We evaluated our approach using real workflows running on a private HTCondor-based hybrid cloud. Results were promising and demonstrated the efficiency of our approach in not only reducing the cost of executing workflows, but also satisfying both the privacy and deadline constraints of the submitted workflows.
{"title":"MPHC: Preserving Privacy for Workflow Execution in Hybrid Clouds","authors":"S. Sharif, J. Taheri, Albert Y. Zomaya, S. Nepal","doi":"10.1109/PDCAT.2013.49","DOIUrl":"https://doi.org/10.1109/PDCAT.2013.49","url":null,"abstract":"Cloud computing has been developed in response to demand from companies seeking to deal with the execution cost of their complex distributed applications. Introducing the notion of hybrid clouds to the cloud computing paradigm brings out many challenges in resource provisioning for workflows. Hybrid clouds encounter the following two main obstacles in reaching their full potential: (1) customers' dissatisfaction due to the conflicting nature of the constraints (budget and deadline), and (2) exposure of customers' private data/jobs in hybrid cloud infrastructures. We believe that too little attention is paid to privacy issues for workflow scheduling under deadline constraint. Many algorithms exist to address the cost and time trade-off, however, they suffer from insufficient consideration of privacy. In this study, we present an algorithm that preserves privacy in scheduling of workflows, whilst still considering customers' deadlines and cost. We evaluated our approach using real workflows running on a private HTCondor-based hybrid cloud. Results were promising and demonstrated the efficiency of our approach in not only reducing the cost of executing workflows, but also satisfying both the privacy and deadline constraints of the submitted workflows.","PeriodicalId":187974,"journal":{"name":"2013 International Conference on Parallel and Distributed Computing, Applications and Technologies","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134245343","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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