Pub Date : 2014-12-15DOI: 10.1109/CloudCom.2014.11
Thomas Pasquier, J. Bacon, D. Eyers
Security concerns are widely seen as an obstacle to the adoption of cloud computing solutions and although a wealth of law and regulation has emerged, the technical basis for enforcing and demonstrating compliance lags behind. Our Cloud Safety Net project aims to show that Information Flow Control (IFC) can augment existing security mechanisms and provide continuous enforcement of extended. Finer-grained application-level security policy in the cloud. We present FlowK, a loadable kernel module for Linux, as part of a proof of concept that IFC can be provided for cloud computing. Following the principle of policy-mechanism separation, IFC policy is assumed to be expressed at application level and FlowK provides mechanisms to enforce IFC policy at runtime. FlowK's design minimises the changes required to existing software when IFC is provided. To show how FlowK can be integrated with cloud software we have designed and evaluated a framework for deploying IFC-aware web applications, suitable for use in a PaaS cloud.
{"title":"FlowK: Information Flow Control for the Cloud","authors":"Thomas Pasquier, J. Bacon, D. Eyers","doi":"10.1109/CloudCom.2014.11","DOIUrl":"https://doi.org/10.1109/CloudCom.2014.11","url":null,"abstract":"Security concerns are widely seen as an obstacle to the adoption of cloud computing solutions and although a wealth of law and regulation has emerged, the technical basis for enforcing and demonstrating compliance lags behind. Our Cloud Safety Net project aims to show that Information Flow Control (IFC) can augment existing security mechanisms and provide continuous enforcement of extended. Finer-grained application-level security policy in the cloud. We present FlowK, a loadable kernel module for Linux, as part of a proof of concept that IFC can be provided for cloud computing. Following the principle of policy-mechanism separation, IFC policy is assumed to be expressed at application level and FlowK provides mechanisms to enforce IFC policy at runtime. FlowK's design minimises the changes required to existing software when IFC is provided. To show how FlowK can be integrated with cloud software we have designed and evaluated a framework for deploying IFC-aware web applications, suitable for use in a PaaS cloud.","PeriodicalId":249306,"journal":{"name":"2014 IEEE 6th International Conference on Cloud Computing Technology and Science","volume":"118 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127724729","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}
Pub Date : 2014-12-15DOI: 10.1109/CloudCom.2014.54
Seungwoo Jeon, B. Hong, Byungsoo Kim
To predict future traffic conditions in each road with unique spatiotemporal pattern, it is necessary to analyze the conditions based on historical traffic data and select time series forecasting methods which can be predicting next pattern for each road according to the analyzed results. Our goal is to create a new statistical model and a new system for predictive graphs of traffic times based on big data processing tools. First, we suggest a vertical data arrangement, gathering past traffic times in the same time slot for long-term prediction. Second, we analyze each traffic pattern to select time-series variables because a time-series forecasting method for a location and a time will be selected according to the variables that are available. Third, we suggest a spatiotemporal prediction map, which is a two-dimensional map with time and location. Each element in the map represents a time-series forecasting method and an R-squared value as indicator of prediction accuracy. Finally, we introduce a new system including RHive as a middle point between R and Hadoop clusters for generating predicted data efficiently from big historical data.
{"title":"Big Data Processing for Prediction of Traffic Time Based on Vertical Data Arrangement","authors":"Seungwoo Jeon, B. Hong, Byungsoo Kim","doi":"10.1109/CloudCom.2014.54","DOIUrl":"https://doi.org/10.1109/CloudCom.2014.54","url":null,"abstract":"To predict future traffic conditions in each road with unique spatiotemporal pattern, it is necessary to analyze the conditions based on historical traffic data and select time series forecasting methods which can be predicting next pattern for each road according to the analyzed results. Our goal is to create a new statistical model and a new system for predictive graphs of traffic times based on big data processing tools. First, we suggest a vertical data arrangement, gathering past traffic times in the same time slot for long-term prediction. Second, we analyze each traffic pattern to select time-series variables because a time-series forecasting method for a location and a time will be selected according to the variables that are available. Third, we suggest a spatiotemporal prediction map, which is a two-dimensional map with time and location. Each element in the map represents a time-series forecasting method and an R-squared value as indicator of prediction accuracy. Finally, we introduce a new system including RHive as a middle point between R and Hadoop clusters for generating predicted data efficiently from big historical data.","PeriodicalId":249306,"journal":{"name":"2014 IEEE 6th International Conference on Cloud Computing Technology and Science","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122418587","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}
Pub Date : 2014-12-15DOI: 10.1109/CloudCom.2014.146
Simeon Veloudis, A. Friesen, I. Paraskakis, Giannis Verginadis, Ioannis Patiniotakis
With the pervasion of cloud computing, enterprises increasingly rely on ecosystems of distributed, task-oriented, modular, and collaborative cloud services. In order to effectively manage the complexity inherent in such ecosystems, enterprises are anticipated to depend upon brokerage mechanisms for performing policy-based governance and for recommending optimal services to consumers. Such mechanisms crucially depend upon the existence of a uniform, platform-independent representation of services, consumer preferences, and policies concerning service delivery. In this paper we propose an ontology-based approach to such a representation.
{"title":"Underpinning a Cloud Brokerage Service Framework for Quality Assurance and Optimization","authors":"Simeon Veloudis, A. Friesen, I. Paraskakis, Giannis Verginadis, Ioannis Patiniotakis","doi":"10.1109/CloudCom.2014.146","DOIUrl":"https://doi.org/10.1109/CloudCom.2014.146","url":null,"abstract":"With the pervasion of cloud computing, enterprises increasingly rely on ecosystems of distributed, task-oriented, modular, and collaborative cloud services. In order to effectively manage the complexity inherent in such ecosystems, enterprises are anticipated to depend upon brokerage mechanisms for performing policy-based governance and for recommending optimal services to consumers. Such mechanisms crucially depend upon the existence of a uniform, platform-independent representation of services, consumer preferences, and policies concerning service delivery. In this paper we propose an ontology-based approach to such a representation.","PeriodicalId":249306,"journal":{"name":"2014 IEEE 6th International Conference on Cloud Computing Technology and Science","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122487938","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}
Pub Date : 2014-12-15DOI: 10.1109/CloudCom.2014.92
Joshua Daniel, T. Dimitrakos, F. El-Moussa, G. Ducatel, P. Pawar, Ali Sajjad
Cloud IaaS and PaaS providers typically hold Cloud consumers accountable for protecting their applications, while Cloud users often find that protecting their proprietary system, application and data stacks on public or hybrid Cloud environments to be complex, expensive and time-consuming. In this paper we demonstrate, how integration of a security solution such as BT Intelligent Protection with the Service Store, results with security operations capability that can scale accordingly to the Cloud use. By enabling "click-to-buy" security services and "click-to-build" secure applications with a few mouse clicks, this integration creates a new paradigm for self-service Cloud-based integrity and security services.
{"title":"Seamless Enablement of Intelligent Protection for Enterprise Cloud Applications through Service Store","authors":"Joshua Daniel, T. Dimitrakos, F. El-Moussa, G. Ducatel, P. Pawar, Ali Sajjad","doi":"10.1109/CloudCom.2014.92","DOIUrl":"https://doi.org/10.1109/CloudCom.2014.92","url":null,"abstract":"Cloud IaaS and PaaS providers typically hold Cloud consumers accountable for protecting their applications, while Cloud users often find that protecting their proprietary system, application and data stacks on public or hybrid Cloud environments to be complex, expensive and time-consuming. In this paper we demonstrate, how integration of a security solution such as BT Intelligent Protection with the Service Store, results with security operations capability that can scale accordingly to the Cloud use. By enabling \"click-to-buy\" security services and \"click-to-build\" secure applications with a few mouse clicks, this integration creates a new paradigm for self-service Cloud-based integrity and security services.","PeriodicalId":249306,"journal":{"name":"2014 IEEE 6th International Conference on Cloud Computing Technology and Science","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130042246","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}
Pub Date : 2014-12-15DOI: 10.1109/CloudCom.2014.106
Chunsheng Zhu, Xiuhua Li, Victor C. M. Leung, Xiping Hu, L. Yang
The powerful data storage and data processing abilities of cloud computing (CC) and the ubiquitous data gathering capability of wireless sensor network (WSN) complement each other in CC-WSN integration, which is attracting growing interest from both academia and industry. However, job scheduling for CC integrated with WSN is a critical and unexplored topic. To fill this gap, this paper first analyzes the characteristics of job scheduling with respect to CC-WSN integration and then studies two traditional and popular job scheduling algorithms (i.e., Min-Min and Max-Min). Further, two novel job scheduling algorithms, namely priority-based two phase Min-Min (PTMM) and priority-based two phase Max-Min (PTAM), are proposed for CC integrated with WSN. Extensive experimental results show that PTMM and PTAM achieve shorter expected completion time than Min-Min and Max-Min, for CC integrated with WSN.
{"title":"Job Scheduling for Cloud Computing Integrated with Wireless Sensor Network","authors":"Chunsheng Zhu, Xiuhua Li, Victor C. M. Leung, Xiping Hu, L. Yang","doi":"10.1109/CloudCom.2014.106","DOIUrl":"https://doi.org/10.1109/CloudCom.2014.106","url":null,"abstract":"The powerful data storage and data processing abilities of cloud computing (CC) and the ubiquitous data gathering capability of wireless sensor network (WSN) complement each other in CC-WSN integration, which is attracting growing interest from both academia and industry. However, job scheduling for CC integrated with WSN is a critical and unexplored topic. To fill this gap, this paper first analyzes the characteristics of job scheduling with respect to CC-WSN integration and then studies two traditional and popular job scheduling algorithms (i.e., Min-Min and Max-Min). Further, two novel job scheduling algorithms, namely priority-based two phase Min-Min (PTMM) and priority-based two phase Max-Min (PTAM), are proposed for CC integrated with WSN. Extensive experimental results show that PTMM and PTAM achieve shorter expected completion time than Min-Min and Max-Min, for CC integrated with WSN.","PeriodicalId":249306,"journal":{"name":"2014 IEEE 6th International Conference on Cloud Computing Technology and Science","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127875413","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}
Pub Date : 2014-12-15DOI: 10.1109/CloudCom.2014.126
F. Indaco, Teng-Sheng Moh
This paper introduces the concept of graphing the size of a level-set against its respective density threshold. This is used to develop a new recursive version of DBSCAN that successfully performs hierarchical clustering, called Level-Set Clustering (LSC).
{"title":"Hierarchical Density-Based Clustering Using Level-Sets","authors":"F. Indaco, Teng-Sheng Moh","doi":"10.1109/CloudCom.2014.126","DOIUrl":"https://doi.org/10.1109/CloudCom.2014.126","url":null,"abstract":"This paper introduces the concept of graphing the size of a level-set against its respective density threshold. This is used to develop a new recursive version of DBSCAN that successfully performs hierarchical clustering, called Level-Set Clustering (LSC).","PeriodicalId":249306,"journal":{"name":"2014 IEEE 6th International Conference on Cloud Computing Technology and Science","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123038942","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}
Pub Date : 2014-12-15DOI: 10.1109/CloudCom.2014.76
Dapeng Dong, J. Herbert
Analysing text-based data has become increasingly important due to the importance of text from sources such as social media, web contents, web searches. The growing volume of such data creates challenges for data analysis including efficient and scalable algorithm, effective computing platforms and energy efficiency. Compression is a standard method for reducing data size but current standard compression algorithms are destructive to the organisation of data contents. This work introduces Content-aware, Partial Compression (CaPC) for text using a dictionary-based approach. We simply use shorter codes to replace strings while maintaining the original data format and structure, so that the compressed contents can be directly consumed by analytic platforms. We evaluate our approach with a set of real-world datasets and several classical MapReduce jobs on Hadoop. We also provide a supplementary utility library for Hadoop, hence, existing MapReduce programs can be used directly on the compressed datasets with little or no modification. In evaluation, we demonstrate that CaPC works well with a wide variety of data analysis scenarios, experimental results show ~30% average data size reduction, and up to ~32% performance increase on some I/O intensive jobs on an in-house Hadoop cluster. While the gains may seem modest, the point is that these gains are 'for free' and act as supplementary to all other optimizations.
{"title":"Content-Aware Partial Compression for Big Textual Data Analysis Acceleration","authors":"Dapeng Dong, J. Herbert","doi":"10.1109/CloudCom.2014.76","DOIUrl":"https://doi.org/10.1109/CloudCom.2014.76","url":null,"abstract":"Analysing text-based data has become increasingly important due to the importance of text from sources such as social media, web contents, web searches. The growing volume of such data creates challenges for data analysis including efficient and scalable algorithm, effective computing platforms and energy efficiency. Compression is a standard method for reducing data size but current standard compression algorithms are destructive to the organisation of data contents. This work introduces Content-aware, Partial Compression (CaPC) for text using a dictionary-based approach. We simply use shorter codes to replace strings while maintaining the original data format and structure, so that the compressed contents can be directly consumed by analytic platforms. We evaluate our approach with a set of real-world datasets and several classical MapReduce jobs on Hadoop. We also provide a supplementary utility library for Hadoop, hence, existing MapReduce programs can be used directly on the compressed datasets with little or no modification. In evaluation, we demonstrate that CaPC works well with a wide variety of data analysis scenarios, experimental results show ~30% average data size reduction, and up to ~32% performance increase on some I/O intensive jobs on an in-house Hadoop cluster. While the gains may seem modest, the point is that these gains are 'for free' and act as supplementary to all other optimizations.","PeriodicalId":249306,"journal":{"name":"2014 IEEE 6th International Conference on Cloud Computing Technology and Science","volume":"os-14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127760292","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}
Pub Date : 2014-12-15DOI: 10.1109/CloudCom.2014.165
B. Duncan, M. Whittington
All Cloud computing standards are dependent upon checklist methodology to implement and then audit the alignment of a company or an operation with the standards that have been set. An investigation of the use of checklists in other academic areas has shown there to be significant weaknesses in the checklist solution to both implementation and audit, these weaknesses will only be exacerbated by the fast-changing and developing nature of clouds. We examine the problems that are inherent with using checklists and seek to identify some mitigating strategies that might be adopted to improve their efficacy.
{"title":"Reflecting on Whether Checklists Can Tick the Box for Cloud Security","authors":"B. Duncan, M. Whittington","doi":"10.1109/CloudCom.2014.165","DOIUrl":"https://doi.org/10.1109/CloudCom.2014.165","url":null,"abstract":"All Cloud computing standards are dependent upon checklist methodology to implement and then audit the alignment of a company or an operation with the standards that have been set. An investigation of the use of checklists in other academic areas has shown there to be significant weaknesses in the checklist solution to both implementation and audit, these weaknesses will only be exacerbated by the fast-changing and developing nature of clouds. We examine the problems that are inherent with using checklists and seek to identify some mitigating strategies that might be adopted to improve their efficacy.","PeriodicalId":249306,"journal":{"name":"2014 IEEE 6th International Conference on Cloud Computing Technology and Science","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127943855","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}
Pub Date : 2014-12-15DOI: 10.1109/CloudCom.2014.153
Shuli Zhang, Yan Zhang, Yifang Qin, Yanni Han, S. Ci
Due to the special topologies and communication pattern, in today's data center networks it is common that a large set of TCP flows and a small set of TCP flows get into different ingress ports of a switch and compete for a same egress port. However, in this case the throughput share of flows in the two sets will not be fair even though all flows have the same RTT. In this paper, we study this problem and find that TCP's fairness in data center networks is related with not only the network capacity but also the number of flows in the two sets. We propose a mathematical model of the average throughput ratio of the large set of flows to the small set of flows. This model can reveal the variation of TCP's fairness along with the change of network parameters (including buffer size, bandwidth, and propagation delay) as well as the number of flows in the two sets. We validate our model by comparing its numerical results with simulation results, finding that they match well.
{"title":"Modeling and Understanding TCP's Fairness Problem in Data Center Networks","authors":"Shuli Zhang, Yan Zhang, Yifang Qin, Yanni Han, S. Ci","doi":"10.1109/CloudCom.2014.153","DOIUrl":"https://doi.org/10.1109/CloudCom.2014.153","url":null,"abstract":"Due to the special topologies and communication pattern, in today's data center networks it is common that a large set of TCP flows and a small set of TCP flows get into different ingress ports of a switch and compete for a same egress port. However, in this case the throughput share of flows in the two sets will not be fair even though all flows have the same RTT. In this paper, we study this problem and find that TCP's fairness in data center networks is related with not only the network capacity but also the number of flows in the two sets. We propose a mathematical model of the average throughput ratio of the large set of flows to the small set of flows. This model can reveal the variation of TCP's fairness along with the change of network parameters (including buffer size, bandwidth, and propagation delay) as well as the number of flows in the two sets. We validate our model by comparing its numerical results with simulation results, finding that they match well.","PeriodicalId":249306,"journal":{"name":"2014 IEEE 6th International Conference on Cloud Computing Technology and Science","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117159751","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}
Pub Date : 2014-12-15DOI: 10.1109/CloudCom.2014.44
Girma Kejela, R. Esteves, Chunming Rong
This work is based on a real-life data-set collected from sensors that monitor drilling processes and equipment in an oil and gas company. The sensor data stream-in at an interval of one second, which is equivalent to 86400 rows of data per day. After studying state-of-the-art Big Data analytics tools including Mahout, RHadoop and Spark, we chose Ox data's H2O for this particular problem because of its fast in-memory processing, strong machine learning engine, and ease of use. Accurate predictive analytics of big sensor data can be used to estimate missed values, or to replace incorrect readings due malfunctioning sensors or broken communication channel. It can also be used to anticipate situations that help in various decision makings, including maintenance planning and operation.
{"title":"Predictive Analytics of Sensor Data Using Distributed Machine Learning Techniques","authors":"Girma Kejela, R. Esteves, Chunming Rong","doi":"10.1109/CloudCom.2014.44","DOIUrl":"https://doi.org/10.1109/CloudCom.2014.44","url":null,"abstract":"This work is based on a real-life data-set collected from sensors that monitor drilling processes and equipment in an oil and gas company. The sensor data stream-in at an interval of one second, which is equivalent to 86400 rows of data per day. After studying state-of-the-art Big Data analytics tools including Mahout, RHadoop and Spark, we chose Ox data's H2O for this particular problem because of its fast in-memory processing, strong machine learning engine, and ease of use. Accurate predictive analytics of big sensor data can be used to estimate missed values, or to replace incorrect readings due malfunctioning sensors or broken communication channel. It can also be used to anticipate situations that help in various decision makings, including maintenance planning and operation.","PeriodicalId":249306,"journal":{"name":"2014 IEEE 6th International Conference on Cloud Computing Technology and Science","volume":"125 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116297323","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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