In this work we implemented Cloudburst to generate realistic workload in Infrastructure as a Service cloud testbeds. Our goal was to minimize the memory footprint of such workload generators by leveraging alternative programming paradigms for highly concurrent applications. In contrast to many existing we leverage the concurrency model of the Go programming language instead of threads. Initial benchmarks with Cloudburst and Rain suggest that Cloudburst consumes significantly less memory at the cost of a higher CPU footprint. In our experimental testbed memory is a more critical resource. Leveraging Cloudburst allows us to run larger benchmarks with the same hardware.
{"title":"Cloudburst - Simulating Workload for IaaS Clouds","authors":"J. Kross, A. Wolke","doi":"10.1109/CLOUD.2014.116","DOIUrl":"https://doi.org/10.1109/CLOUD.2014.116","url":null,"abstract":"In this work we implemented Cloudburst to generate realistic workload in Infrastructure as a Service cloud testbeds. Our goal was to minimize the memory footprint of such workload generators by leveraging alternative programming paradigms for highly concurrent applications. In contrast to many existing we leverage the concurrency model of the Go programming language instead of threads. Initial benchmarks with Cloudburst and Rain suggest that Cloudburst consumes significantly less memory at the cost of a higher CPU footprint. In our experimental testbed memory is a more critical resource. Leveraging Cloudburst allows us to run larger benchmarks with the same hardware.","PeriodicalId":288542,"journal":{"name":"2014 IEEE 7th International Conference on Cloud Computing","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121043784","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}
The fast growth of cloud applications highlights the requirement of appropriate security controls to restrict access to shared resources limited to authorized users. Existing authorization systems are not primarily designed for cloud environments and do not provide the required flexibility, adaptability, elasticity, scalability, or fine-grainedness of cloud applications. This paper outlines an ongoing effort in development of a flexible fine-grained access control system for modern cloud-based applications. Modern cloud applications are distinctive in that the required authorization rules are defined by the organizations owning data and resources, before the application logic can be developed by their programmers. Although this simplifies cloud application development and provides flexibility and adaptability to potential future policy changes, it highlights the need for an adaptive flexible authorization system.
{"title":"Towards a Flexible Fine-Grained Access Control System for Modern Cloud Applications","authors":"Reza Shiftehfar, K. Mechitov, G. Agha","doi":"10.1109/CLOUD.2014.144","DOIUrl":"https://doi.org/10.1109/CLOUD.2014.144","url":null,"abstract":"The fast growth of cloud applications highlights the requirement of appropriate security controls to restrict access to shared resources limited to authorized users. Existing authorization systems are not primarily designed for cloud environments and do not provide the required flexibility, adaptability, elasticity, scalability, or fine-grainedness of cloud applications. This paper outlines an ongoing effort in development of a flexible fine-grained access control system for modern cloud-based applications. Modern cloud applications are distinctive in that the required authorization rules are defined by the organizations owning data and resources, before the application logic can be developed by their programmers. Although this simplifies cloud application development and provides flexibility and adaptability to potential future policy changes, it highlights the need for an adaptive flexible authorization system.","PeriodicalId":288542,"journal":{"name":"2014 IEEE 7th International Conference on Cloud Computing","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125946383","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}
Mohan Baruwal Chhetri, S. Chichin, Quoc Bao Vo, R. Kowalczyk
Migration to the cloud offers several benefits including reduced operational costs, flexibility, scalability, and a greater focus on business goals, but it also has a flip side reduced visibility. Organizations only have a blackbox view of cloud servers and while pricing and specification information is publicly available, there is limited information about cloud performance. This necessitates the need for tools that can provide greater visibility into cloud insfrastructure performance so that consumers can objectively compare and contrast the offerings from different providers. Smart CloudBench [1][2][3] is a system that allows users to run automated, ondemand, real-time and customized benchmark tests on cloud infrastructure. In this paper, we present Smart CloudMonitor - a performance monitoring tool that provides multi-layer performance monitoring capabilities to Smart CloudBench. It provides greater visibility and insight into cloud performance by monitoring both application performance as well as the corresponding resource consumption. Experiments conducted on cloud infrastructure using Smart CloudBench show the add value that Smart CloudMonitor provides to the process of cloud performance evaluation.
{"title":"Smart CloudMonitor - Providing Visibility into Performance of Black-Box Clouds","authors":"Mohan Baruwal Chhetri, S. Chichin, Quoc Bao Vo, R. Kowalczyk","doi":"10.1109/CLOUD.2014.108","DOIUrl":"https://doi.org/10.1109/CLOUD.2014.108","url":null,"abstract":"Migration to the cloud offers several benefits including reduced operational costs, flexibility, scalability, and a greater focus on business goals, but it also has a flip side reduced visibility. Organizations only have a blackbox view of cloud servers and while pricing and specification information is publicly available, there is limited information about cloud performance. This necessitates the need for tools that can provide greater visibility into cloud insfrastructure performance so that consumers can objectively compare and contrast the offerings from different providers. Smart CloudBench [1][2][3] is a system that allows users to run automated, ondemand, real-time and customized benchmark tests on cloud infrastructure. In this paper, we present Smart CloudMonitor - a performance monitoring tool that provides multi-layer performance monitoring capabilities to Smart CloudBench. It provides greater visibility and insight into cloud performance by monitoring both application performance as well as the corresponding resource consumption. Experiments conducted on cloud infrastructure using Smart CloudBench show the add value that Smart CloudMonitor provides to the process of cloud performance evaluation.","PeriodicalId":288542,"journal":{"name":"2014 IEEE 7th International Conference on Cloud Computing","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126057659","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}
For archiving large datasets in high-performance computing facilities, tape technology has a long history of providing inexpensive capacity. However, as the memory-size of supercomputers continues to grow geometrically, the cost of tape bandwidth is becoming more important. The projected costs for tape-drives, robotics, and maintenance, are creating challenges for tape-based archives. The advent of erasure-coded object storage, driven by the "cloud storage" industry, might make it practical to implement archives using disks, or hybrid disk-and-tape systems. We used linear optimization techniques to investigate when and how this transition might best be made, taking into consideration our significant investment in tape technology. Our models introduce a technique to systematically relax constraints on the relationship between tape-capacity and tape-bandwidth, which governs a trade-off between cost and performance. We ran parameter studies that support some preliminary conclusions about paths forward for archive infrastructure at LANL.
{"title":"Cost of Tape versus Disk for Archival Storage","authors":"Jeff T. Inman, G. Grider, Hsing-bung Chen","doi":"10.1109/CLOUD.2014.37","DOIUrl":"https://doi.org/10.1109/CLOUD.2014.37","url":null,"abstract":"For archiving large datasets in high-performance computing facilities, tape technology has a long history of providing inexpensive capacity. However, as the memory-size of supercomputers continues to grow geometrically, the cost of tape bandwidth is becoming more important. The projected costs for tape-drives, robotics, and maintenance, are creating challenges for tape-based archives. The advent of erasure-coded object storage, driven by the \"cloud storage\" industry, might make it practical to implement archives using disks, or hybrid disk-and-tape systems. We used linear optimization techniques to investigate when and how this transition might best be made, taking into consideration our significant investment in tape technology. Our models introduce a technique to systematically relax constraints on the relationship between tape-capacity and tape-bandwidth, which governs a trade-off between cost and performance. We ran parameter studies that support some preliminary conclusions about paths forward for archive infrastructure at LANL.","PeriodicalId":288542,"journal":{"name":"2014 IEEE 7th International Conference on Cloud Computing","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126064590","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}
D. Che, J. Fairfield, P. Ghodous, Jean-Patrick Gelas
We are motivated to study (and eventually deliver) a cloud facility that has its "back door" open for incorporating voluntary computing resources, and such implemented cloud infrastructures have the potential to harmoniously serve both on-demand and non on-demand requests/jobs at possibly the lowest cost because of involvement of the low or no cost of using voluntary computing resources. This short paper presents our idea of enhancing the "backfill computing" model originally proposed by Marshall, et al. by incorporating voluntary computing resources into the resource pools of clouds, and summarizes the simulation results we had recently obtained.
{"title":"Enhanced Backfill Computing","authors":"D. Che, J. Fairfield, P. Ghodous, Jean-Patrick Gelas","doi":"10.1109/CLOUD.2014.140","DOIUrl":"https://doi.org/10.1109/CLOUD.2014.140","url":null,"abstract":"We are motivated to study (and eventually deliver) a cloud facility that has its \"back door\" open for incorporating voluntary computing resources, and such implemented cloud infrastructures have the potential to harmoniously serve both on-demand and non on-demand requests/jobs at possibly the lowest cost because of involvement of the low or no cost of using voluntary computing resources. This short paper presents our idea of enhancing the \"backfill computing\" model originally proposed by Marshall, et al. by incorporating voluntary computing resources into the resource pools of clouds, and summarizes the simulation results we had recently obtained.","PeriodicalId":288542,"journal":{"name":"2014 IEEE 7th International Conference on Cloud Computing","volume":"80 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126906829","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}
MapReduce, proposed as a programming model, has been widely adopted in the field of text processing over large datasets with the capability of exploiting the distributed resources and processing the large-scale data. Attributed to its simplicity and scalability, the success seems to have the potential to make Big Data processing by cloud computing available. Nevertheless, such promise is accompanied by the difficulty of fitting the applications into MapReduce. This is because MapReduce is limited to the kind of applications that every input key-value pair is independent of each other. In this paper, we extend the general applicability of MapReduce by allowing the dependence within a set of input key-value pairs but preserving independence among all sets. Such this new modeling paradigm intends MapReduce to shift processing the independent input key-value pairs to processing the independent sets. However, the advancement in the applicability brings the intricate problem of how two-stage processing structure, inherent in MapReduce, handles the dependence within a set of input key-value pairs. To tackle this problem, we propose the design pattern called two-phase data processing. It expresses the application in two phases not only to match the two-stage processing structure but to exploit the power of MapReduce through the cooperation between the mappers and reducers. In addition, we present the design methodology-multiple two-phase data processing-to offer advice on processing the independent sets. The experiment of background subtraction, a part of video surveillance, proves that the new modeling paradigm broadens the possibilities of MapReduce and demonstrates how our design methodology guides the applications to the implementation.
{"title":"Multiple Two-Phase Data Processing with MapReduce","authors":"Hsiang-Huang Wu, Tse-Chen Yeh, Chien-Min Wang","doi":"10.1109/CLOUD.2014.55","DOIUrl":"https://doi.org/10.1109/CLOUD.2014.55","url":null,"abstract":"MapReduce, proposed as a programming model, has been widely adopted in the field of text processing over large datasets with the capability of exploiting the distributed resources and processing the large-scale data. Attributed to its simplicity and scalability, the success seems to have the potential to make Big Data processing by cloud computing available. Nevertheless, such promise is accompanied by the difficulty of fitting the applications into MapReduce. This is because MapReduce is limited to the kind of applications that every input key-value pair is independent of each other. In this paper, we extend the general applicability of MapReduce by allowing the dependence within a set of input key-value pairs but preserving independence among all sets. Such this new modeling paradigm intends MapReduce to shift processing the independent input key-value pairs to processing the independent sets. However, the advancement in the applicability brings the intricate problem of how two-stage processing structure, inherent in MapReduce, handles the dependence within a set of input key-value pairs. To tackle this problem, we propose the design pattern called two-phase data processing. It expresses the application in two phases not only to match the two-stage processing structure but to exploit the power of MapReduce through the cooperation between the mappers and reducers. In addition, we present the design methodology-multiple two-phase data processing-to offer advice on processing the independent sets. The experiment of background subtraction, a part of video surveillance, proves that the new modeling paradigm broadens the possibilities of MapReduce and demonstrates how our design methodology guides the applications to the implementation.","PeriodicalId":288542,"journal":{"name":"2014 IEEE 7th International Conference on Cloud Computing","volume":"50 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129203756","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}
Huihong He, Zhiyi Ma, Hongjie Chen, Chih-Yi Yeh, W. Shao
As more and more multi-tenant applications emerge in clouds, people increasingly recognize the importance of multi-tenant applications in realizing cloud benefit maximization. Service Level Agreement (SLA) is advocated widely to monitor these applications for multiple tenants to meet their service quality requirements. However, so far these applications provide limited multi-tenant monitoring supports, which prevents the applications from guaranteeing tenants' interests efficiently. In this paper, we propose an aspect-oriented approach to monitor multi-tenant applications based on tenant SLAs. Our approach includes monitoring code generation and runtime management. During code generation, the approach proposes an SLA feature model for tenants to specify variable requirements. Based on the requirements the approach selects code snippets, which are implemented as templates in advance, and splices them into an monitoring aspect. During runtime, the approach prioritizes aspects to determine execution order and updates monitoring status in term of tenant. An implemented prototype is used to evaluate the approach by case studies, which demonstrate the approach effectiveness in common situations.
{"title":"An Aspect-Oriented Approach to SLA-Driven Monitoring Multi-tenant Cloud Application","authors":"Huihong He, Zhiyi Ma, Hongjie Chen, Chih-Yi Yeh, W. Shao","doi":"10.1109/CLOUD.2014.118","DOIUrl":"https://doi.org/10.1109/CLOUD.2014.118","url":null,"abstract":"As more and more multi-tenant applications emerge in clouds, people increasingly recognize the importance of multi-tenant applications in realizing cloud benefit maximization. Service Level Agreement (SLA) is advocated widely to monitor these applications for multiple tenants to meet their service quality requirements. However, so far these applications provide limited multi-tenant monitoring supports, which prevents the applications from guaranteeing tenants' interests efficiently. In this paper, we propose an aspect-oriented approach to monitor multi-tenant applications based on tenant SLAs. Our approach includes monitoring code generation and runtime management. During code generation, the approach proposes an SLA feature model for tenants to specify variable requirements. Based on the requirements the approach selects code snippets, which are implemented as templates in advance, and splices them into an monitoring aspect. During runtime, the approach prioritizes aspects to determine execution order and updates monitoring status in term of tenant. An implemented prototype is used to evaluate the approach by case studies, which demonstrate the approach effectiveness in common situations.","PeriodicalId":288542,"journal":{"name":"2014 IEEE 7th International Conference on Cloud Computing","volume":"115 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123996452","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}
Minseok Kwon, Zuochao Dou, W. Heinzelman, T. Soyata, He Ba, Jiye Shi
As servers are placed in diverse locations in networked services today, it becomes vital to direct a client's request to the best server(s) to achieve both high performance and reliability. In this distributed setting, non-negligible latency and server availability become two major concerns, especially for highly-interactive applications. Profiling latencies and sending redundant data have been investigated as solutions to these issues. The notion of a cloudlet in mobile-cloud computing is also relevant in this context, as the cloudlet can supply these solution approaches on behalf of the mobile. In this paper, we investigate the effects of profiling and redundancy on latency when a client has a choice of multiple servers to connect to, using measurements from real experiments and simulations. We devise and test different server selection and data partitioning strategies in terms of profiling and redundancy. Our key findings are summarized as follows. First, intelligent server selection algorithms help find the optimal group of servers that minimize latency with profiling. Second, we can achieve good performance with relatively simple approaches using redundancy. Our analysis of profiling and redundancy provides insight to help designers determine how many servers and which servers to select to reduce latency.
{"title":"Use of Network Latency Profiling and Redundancy for Cloud Server Selection","authors":"Minseok Kwon, Zuochao Dou, W. Heinzelman, T. Soyata, He Ba, Jiye Shi","doi":"10.1109/CLOUD.2014.114","DOIUrl":"https://doi.org/10.1109/CLOUD.2014.114","url":null,"abstract":"As servers are placed in diverse locations in networked services today, it becomes vital to direct a client's request to the best server(s) to achieve both high performance and reliability. In this distributed setting, non-negligible latency and server availability become two major concerns, especially for highly-interactive applications. Profiling latencies and sending redundant data have been investigated as solutions to these issues. The notion of a cloudlet in mobile-cloud computing is also relevant in this context, as the cloudlet can supply these solution approaches on behalf of the mobile. In this paper, we investigate the effects of profiling and redundancy on latency when a client has a choice of multiple servers to connect to, using measurements from real experiments and simulations. We devise and test different server selection and data partitioning strategies in terms of profiling and redundancy. Our key findings are summarized as follows. First, intelligent server selection algorithms help find the optimal group of servers that minimize latency with profiling. Second, we can achieve good performance with relatively simple approaches using redundancy. Our analysis of profiling and redundancy provides insight to help designers determine how many servers and which servers to select to reduce latency.","PeriodicalId":288542,"journal":{"name":"2014 IEEE 7th International Conference on Cloud Computing","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121310726","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}
Abu Awal Md Shoeb, Ragib Hasan, Munirul M. Haque, Meng-Zeng Hu
Cloud computing infrastructure helps users to minimize cost by outsourcing data and computation on-demand. Due to the varying user needs in terms of computation power, storage capacity, etc., cloud providers offer various machines to choose from, to maximize the intended need. In this paper, we disprove several common conceptions regarding the performance and cost of cloud by experimenting on instances of two different families (compute and storage optimized) of the most popular cloud platform, Amazon Elastic Compute Cloud (EC2). Our analysis shows the interesting finding that, for the machines of the same configuration, storage optimized instances have lower disk read-write speed than compute optimized, which does not completely reflect the claim made by Amazon in all cases. Additionally, storage optimized instances have notable performance difference among them. We also identify that the I/O performance of same instance type varies over different time periods.
{"title":"A Comparative Study on I/O Performance between Compute and Storage Optimized Instances of Amazon EC2","authors":"Abu Awal Md Shoeb, Ragib Hasan, Munirul M. Haque, Meng-Zeng Hu","doi":"10.1109/CLOUD.2014.146","DOIUrl":"https://doi.org/10.1109/CLOUD.2014.146","url":null,"abstract":"Cloud computing infrastructure helps users to minimize cost by outsourcing data and computation on-demand. Due to the varying user needs in terms of computation power, storage capacity, etc., cloud providers offer various machines to choose from, to maximize the intended need. In this paper, we disprove several common conceptions regarding the performance and cost of cloud by experimenting on instances of two different families (compute and storage optimized) of the most popular cloud platform, Amazon Elastic Compute Cloud (EC2). Our analysis shows the interesting finding that, for the machines of the same configuration, storage optimized instances have lower disk read-write speed than compute optimized, which does not completely reflect the claim made by Amazon in all cases. Additionally, storage optimized instances have notable performance difference among them. We also identify that the I/O performance of same instance type varies over different time periods.","PeriodicalId":288542,"journal":{"name":"2014 IEEE 7th International Conference on Cloud Computing","volume":"105 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121381131","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}
Data-as-a-Service (DaaS) mashup enables data providers to dynamically integrate their data on demand depending on consumers' requests. Utilizing DaaS mashup, however, involves some challenges. Mashing up data from multiple sources to answer a consumer's request might reveal sensitive information and thereby compromise the privacy of individuals. Moreover, data integration of arbitrary DaaS providers might not always be sufficient to answer incoming requests. In this paper, we provide a cloud-based framework for privacy-preserving DaaS mashup that enables secure collaboration between DaaS providers for the purpose of generating an anonymous dataset to support data mining. Experiments on real-life data demonstrate that our DaaS mashup framework is scalable and can efficiently and effectively satisfy the data privacy and data mining requirements specified by the DaaS providers and the data consumers.
{"title":"D-Mash: A Framework for Privacy-Preserving Data-as-a-Service Mashups","authors":"M. Arafati, Gaby G. Dagher, B. Fung, P. Hung","doi":"10.1109/CLOUD.2014.73","DOIUrl":"https://doi.org/10.1109/CLOUD.2014.73","url":null,"abstract":"Data-as-a-Service (DaaS) mashup enables data providers to dynamically integrate their data on demand depending on consumers' requests. Utilizing DaaS mashup, however, involves some challenges. Mashing up data from multiple sources to answer a consumer's request might reveal sensitive information and thereby compromise the privacy of individuals. Moreover, data integration of arbitrary DaaS providers might not always be sufficient to answer incoming requests. In this paper, we provide a cloud-based framework for privacy-preserving DaaS mashup that enables secure collaboration between DaaS providers for the purpose of generating an anonymous dataset to support data mining. Experiments on real-life data demonstrate that our DaaS mashup framework is scalable and can efficiently and effectively satisfy the data privacy and data mining requirements specified by the DaaS providers and the data consumers.","PeriodicalId":288542,"journal":{"name":"2014 IEEE 7th International Conference on Cloud Computing","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114836690","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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