Pub Date : 2013-06-28DOI: 10.1109/SERVICES.2013.29
Changbing Chen, Xia Yang, Z. Bong, Sivadon Chaisiri, Bu-Sung Lee
This paper studies event recognition in a building based on the patterns of power consumption. It is a big challenge to identify what kinds of events happened in a building without additional devices such as camera and motion sensors, etc. Instead, we learn when and how the events happened from the historical record of power consumption and apply the lesson into the design of an event recognition system (ERS). The ERS will find out abnormal power usage to avoid wasting power, which leads to the energy savings in a building. The ERS involves big data analytics with a large size of dataset collected in a real time. Such a data intensive system is usually viewed as a workflow. A workflow management is a significant task of the system requiring data analysis in terms of the system scalability to maintain high throughput or fast speed analysis. We propose a workflow framework that allows users to perform remote and parallel workflow execution, whose tasks are efficiently scheduled and distributed in cloud computing environment. We run the ERS as a target system for the proposed framework with power consumption data (whose size is approximately 20GB or more) collected from each of over 240 rooms in a building at Dept. of Engineering, Tokyo University in 2011. We show that the proposed framework accelerates the speed of data analysis by providing scaling infrastructure and parallel processing feature utilizing cloud computing technologies. We also share our experience and results on the big data analytics and discuss how the studies contribute to achieve Green Campus.
{"title":"A Workflow Framework for Big Data Analytics: Event Recognition in a Building","authors":"Changbing Chen, Xia Yang, Z. Bong, Sivadon Chaisiri, Bu-Sung Lee","doi":"10.1109/SERVICES.2013.29","DOIUrl":"https://doi.org/10.1109/SERVICES.2013.29","url":null,"abstract":"This paper studies event recognition in a building based on the patterns of power consumption. It is a big challenge to identify what kinds of events happened in a building without additional devices such as camera and motion sensors, etc. Instead, we learn when and how the events happened from the historical record of power consumption and apply the lesson into the design of an event recognition system (ERS). The ERS will find out abnormal power usage to avoid wasting power, which leads to the energy savings in a building. The ERS involves big data analytics with a large size of dataset collected in a real time. Such a data intensive system is usually viewed as a workflow. A workflow management is a significant task of the system requiring data analysis in terms of the system scalability to maintain high throughput or fast speed analysis. We propose a workflow framework that allows users to perform remote and parallel workflow execution, whose tasks are efficiently scheduled and distributed in cloud computing environment. We run the ERS as a target system for the proposed framework with power consumption data (whose size is approximately 20GB or more) collected from each of over 240 rooms in a building at Dept. of Engineering, Tokyo University in 2011. We show that the proposed framework accelerates the speed of data analysis by providing scaling infrastructure and parallel processing feature utilizing cloud computing technologies. We also share our experience and results on the big data analytics and discuss how the studies contribute to achieve Green Campus.","PeriodicalId":169370,"journal":{"name":"2013 IEEE Ninth World Congress on Services","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130322030","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 : 2013-06-28DOI: 10.1109/SERVICES.2013.60
P. Lach, H. Müller
Mobile devices offer an unprecedented amount of context about their users. Management of this context is like trying to find the signal in the noise. Those applications that can find the signal open themselves up to new business opportunities. These business opportunities come about as a result of emergent behavior and are better at satisfying user utility. Applications need to become smart applications and as software engineers we can make this happen by looking at the lessons learned from self-adaptive systems. Data structures, models, and an unfettered resolve to simplifying the user experience will help us get there.
{"title":"Towards Smarter Task Applications","authors":"P. Lach, H. Müller","doi":"10.1109/SERVICES.2013.60","DOIUrl":"https://doi.org/10.1109/SERVICES.2013.60","url":null,"abstract":"Mobile devices offer an unprecedented amount of context about their users. Management of this context is like trying to find the signal in the noise. Those applications that can find the signal open themselves up to new business opportunities. These business opportunities come about as a result of emergent behavior and are better at satisfying user utility. Applications need to become smart applications and as software engineers we can make this happen by looking at the lessons learned from self-adaptive systems. Data structures, models, and an unfettered resolve to simplifying the user experience will help us get there.","PeriodicalId":169370,"journal":{"name":"2013 IEEE Ninth World Congress on Services","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129042593","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 : 2013-06-28DOI: 10.1109/SERVICES.2013.13
Guang Lin, Binh Han, Jian Yin, I. Gorton
This paper explores cloud computing for large-scale data intensive scientific applications. Cloud computing is attractive because it provides hardware and software resources on-demand, which relieves the burden of acquiring and maintaining a huge amount of resources that may be used only once by a scientific application. However, unlike typical commercial applications that often just requires a moderate amount of ordinary resources, large-scale scientific applications often need to process enormous amount of data in the terabyte or even petabyte range and require special high performance hardware with low latency connections to complete computation in a reasonable amount of time. To address these challenges, we build an infrastructure that can dynamically select high performance computing hardware across institutions and dynamically adapt the computation to the selected resources to achieve high performance. We have also demonstrated the effectiveness of our infrastructure by building a system biology application and an uncertainty quantification application for carbon sequestration, which can efficiently utilize data and computation resources across several institutions.
{"title":"Exploring Cloud Computing for Large-Scale Scientific Applications","authors":"Guang Lin, Binh Han, Jian Yin, I. Gorton","doi":"10.1109/SERVICES.2013.13","DOIUrl":"https://doi.org/10.1109/SERVICES.2013.13","url":null,"abstract":"This paper explores cloud computing for large-scale data intensive scientific applications. Cloud computing is attractive because it provides hardware and software resources on-demand, which relieves the burden of acquiring and maintaining a huge amount of resources that may be used only once by a scientific application. However, unlike typical commercial applications that often just requires a moderate amount of ordinary resources, large-scale scientific applications often need to process enormous amount of data in the terabyte or even petabyte range and require special high performance hardware with low latency connections to complete computation in a reasonable amount of time. To address these challenges, we build an infrastructure that can dynamically select high performance computing hardware across institutions and dynamically adapt the computation to the selected resources to achieve high performance. We have also demonstrated the effectiveness of our infrastructure by building a system biology application and an uncertainty quantification application for carbon sequestration, which can efficiently utilize data and computation resources across several institutions.","PeriodicalId":169370,"journal":{"name":"2013 IEEE Ninth World Congress on Services","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130191416","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 : 2013-06-28DOI: 10.1109/SERVICES.2013.24
Bo Jiang, Xiao-xiao Zhang, Weifeng Pan, Bo Hu
Cloud computing is an Internet-based computing. It relies on sharing computing resources which are delivered as services on the Internet. Web service is one of the most important types of services that can be used in cloud computing. But many of them may be similar in some functional or nonfunctional properties, making how to recommend a suitable web service a problem facing many developers. Researchers have taken the QoS attributes into consideration. However, their research is on the premise that all the recommended web services are compatible, i.e., the recommended web services can be composed with existing web services. It may not always be true. In this paper, we only take the compatibility of web services into consideration, and present a BIpartite Graph based Service Recommendation (BIGSIR) method to address the service compatibility problem. BIGSIR uses the historical usage data of web services to recommend web services to developers. Different from existing web service recommendation approaches, BIGSIR adopts a bipartite graph to visual the web services and the relationship between them. Based on the graph model, an effective recommendation algorithm is introduced to recommend the suitable web services. Our approach is evaluated on a dataset constructed from myExperiment, a search engine that contains about 1, 851 web services and 2, 000 workflows. Experimental results demonstrate that apart from some isolated web services or workflows, BIGSIR can obtain promising results. And we also explore the factors that will influence the performance of BIGSIR. This work not only provides a new dataset, but also highlights a new perspective for service recommendation, i.e. services as a bipartite network.
{"title":"BIGSIR: A Bipartite Graph Based Service Recommendation Method","authors":"Bo Jiang, Xiao-xiao Zhang, Weifeng Pan, Bo Hu","doi":"10.1109/SERVICES.2013.24","DOIUrl":"https://doi.org/10.1109/SERVICES.2013.24","url":null,"abstract":"Cloud computing is an Internet-based computing. It relies on sharing computing resources which are delivered as services on the Internet. Web service is one of the most important types of services that can be used in cloud computing. But many of them may be similar in some functional or nonfunctional properties, making how to recommend a suitable web service a problem facing many developers. Researchers have taken the QoS attributes into consideration. However, their research is on the premise that all the recommended web services are compatible, i.e., the recommended web services can be composed with existing web services. It may not always be true. In this paper, we only take the compatibility of web services into consideration, and present a BIpartite Graph based Service Recommendation (BIGSIR) method to address the service compatibility problem. BIGSIR uses the historical usage data of web services to recommend web services to developers. Different from existing web service recommendation approaches, BIGSIR adopts a bipartite graph to visual the web services and the relationship between them. Based on the graph model, an effective recommendation algorithm is introduced to recommend the suitable web services. Our approach is evaluated on a dataset constructed from myExperiment, a search engine that contains about 1, 851 web services and 2, 000 workflows. Experimental results demonstrate that apart from some isolated web services or workflows, BIGSIR can obtain promising results. And we also explore the factors that will influence the performance of BIGSIR. This work not only provides a new dataset, but also highlights a new perspective for service recommendation, i.e. services as a bipartite network.","PeriodicalId":169370,"journal":{"name":"2013 IEEE Ninth World Congress on Services","volume":"82 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127883186","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 : 2013-06-28DOI: 10.1109/SERVICES.2013.31
Shuai Lu, Yan Liu, Da Meng
To integrate wind and solar energy in electric systems, new technologies, such as energy storage and demand response, have been proposed to increase system flexibility. Control approaches and market rules are being developed accordingly to better manage these resources in multiple time scales. Therefore, models and software tools capable of performing hourly scheduling, intra-hour dispatch, and automatic generation control simulations are needed for testing these control approaches and for evaluating new market rules. At Pacific Northwest National Laboratory, we have developed an Electric System Intra-Hour Operation Simulator (ESIOS). Expanding this simulator as a service platform can benefit a larger community involved in exploring new models and controls and reducing the burden of maintaining a computing platform. Moreover the feedback and contribution from community users can help further improve the features of this simulation ecosystem. In this paper, we describe the function of this simulator. Based on our experience, we discuss the architecture design perspectives for transforming this simulator to an integrated collaborative service platform.
{"title":"Towards a Collaborative Simulation Platform for Renewable Energy Systems","authors":"Shuai Lu, Yan Liu, Da Meng","doi":"10.1109/SERVICES.2013.31","DOIUrl":"https://doi.org/10.1109/SERVICES.2013.31","url":null,"abstract":"To integrate wind and solar energy in electric systems, new technologies, such as energy storage and demand response, have been proposed to increase system flexibility. Control approaches and market rules are being developed accordingly to better manage these resources in multiple time scales. Therefore, models and software tools capable of performing hourly scheduling, intra-hour dispatch, and automatic generation control simulations are needed for testing these control approaches and for evaluating new market rules. At Pacific Northwest National Laboratory, we have developed an Electric System Intra-Hour Operation Simulator (ESIOS). Expanding this simulator as a service platform can benefit a larger community involved in exploring new models and controls and reducing the burden of maintaining a computing platform. Moreover the feedback and contribution from community users can help further improve the features of this simulation ecosystem. In this paper, we describe the function of this simulator. Based on our experience, we discuss the architecture design perspectives for transforming this simulator to an integrated collaborative service platform.","PeriodicalId":169370,"journal":{"name":"2013 IEEE Ninth World Congress on Services","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129768216","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 : 2013-06-28DOI: 10.1109/SERVICES.2013.25
P. Berndt, Johannes Watzl
In order to establish a broader market for cloud computing, offers must be made comparable. Several efforts exist to compare performance of products from different providers and convey an idea of what to expect by means of (periodical) reports. Yet, buying IaaS cloud compute resources remains a blind bargain. The actual performance of a customer's deployment may, for various reasons, be substantially different from such third-party reports. Particularly, a cloud user cannot rely on receiving the same performance, be it because of higher load or arbitrary cloud reconfiguration. To render service levels of different cloud products meaningful and comparable within and across providers, these will have to commit themselves to providing performance according to some reference measure that also regards virtualization, resource allocation and isolation. Though the actual benchmarks will likely differ across application and market niches, the methodology to define, measure and guarantee performance remains the same. In this paper we propose a method for quantifying, determining and ensuring performance on the basis of a performance unit that conveys what performance can be expected from a VM deployment and is suitable for use in SLAs. The abstract approach is exemplified and validated by a case study with concrete benchmarks on a KVM-based cloud.
{"title":"Unitizing Performance of IaaS Cloud Deployments","authors":"P. Berndt, Johannes Watzl","doi":"10.1109/SERVICES.2013.25","DOIUrl":"https://doi.org/10.1109/SERVICES.2013.25","url":null,"abstract":"In order to establish a broader market for cloud computing, offers must be made comparable. Several efforts exist to compare performance of products from different providers and convey an idea of what to expect by means of (periodical) reports. Yet, buying IaaS cloud compute resources remains a blind bargain. The actual performance of a customer's deployment may, for various reasons, be substantially different from such third-party reports. Particularly, a cloud user cannot rely on receiving the same performance, be it because of higher load or arbitrary cloud reconfiguration. To render service levels of different cloud products meaningful and comparable within and across providers, these will have to commit themselves to providing performance according to some reference measure that also regards virtualization, resource allocation and isolation. Though the actual benchmarks will likely differ across application and market niches, the methodology to define, measure and guarantee performance remains the same. In this paper we propose a method for quantifying, determining and ensuring performance on the basis of a performance unit that conveys what performance can be expected from a VM deployment and is suitable for use in SLAs. The abstract approach is exemplified and validated by a case study with concrete benchmarks on a KVM-based cloud.","PeriodicalId":169370,"journal":{"name":"2013 IEEE Ninth World Congress on Services","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129249944","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 : 2013-06-28DOI: 10.1109/SERVICES.2013.42
S. Shetty
Cloud computing allows users to remotely store their data into the cloud and provides on-demand applications and services from a shared pool of configurable computing resources. The security of the outsourced data in the cloud is dependent on the security of the cloud computing system and network. Though, there have been numerous efforts on securing data on the cloud computing system, evaluation of data security on the network between cloud provider and its users is still a very challenging task. The audit of the cloud computing system and network will provide insights on the security and performance of VMs and the operating system on multiple data centers and the intra-cloud network managed by cloud providers and the wide-area network between the cloud user and cloud provider. Thus, network traffic analysis for cloud auditing is of critical importance so that users can resort to an external audit party to verify the data security on the network between cloud provider and its users. This paper presents the following key technologies required to analyze network traffic in the cloud computing environment: IP geolocation of network devices between cloud provider and its users, monitoring the data security of the cloud network path, and online mining of massive cloud auditing logs generated by cloud network traffic.
{"title":"Auditing and Analysis of Network Traffic in Cloud Environment","authors":"S. Shetty","doi":"10.1109/SERVICES.2013.42","DOIUrl":"https://doi.org/10.1109/SERVICES.2013.42","url":null,"abstract":"Cloud computing allows users to remotely store their data into the cloud and provides on-demand applications and services from a shared pool of configurable computing resources. The security of the outsourced data in the cloud is dependent on the security of the cloud computing system and network. Though, there have been numerous efforts on securing data on the cloud computing system, evaluation of data security on the network between cloud provider and its users is still a very challenging task. The audit of the cloud computing system and network will provide insights on the security and performance of VMs and the operating system on multiple data centers and the intra-cloud network managed by cloud providers and the wide-area network between the cloud user and cloud provider. Thus, network traffic analysis for cloud auditing is of critical importance so that users can resort to an external audit party to verify the data security on the network between cloud provider and its users. This paper presents the following key technologies required to analyze network traffic in the cloud computing environment: IP geolocation of network devices between cloud provider and its users, monitoring the data security of the cloud network path, and online mining of massive cloud auditing logs generated by cloud network traffic.","PeriodicalId":169370,"journal":{"name":"2013 IEEE Ninth World Congress on Services","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129161902","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 : 2013-06-28DOI: 10.1109/SERVICES.2013.77
Fangfang Yuan, Lidong Zhai, Yanan Cao, Li Guo
In this paper, we proposed an intrusion detection system for detecting anomaly on Android smartphones. The intrusion detection system continuously monitors and collects the information of smartphone under normal conditions and attack state. It extracts various features obtained from the Android system, such as the network traffic of smartphones, battery consumption, CPU usage, the amount of running processes and so on. Then, it applies Bayes Classifying Algorithm to determine whether there is an invasion. In order to further analyze the Android system abnormalities and locate malicious software, along with system state monitoring the intrusion detection system monitors the process and network flow of the smartphone. Finally, experiments on the system which was designed in this paper have been carried out. Empirical results suggest that the proposed intrusion detection system is effective in detecting anomaly on Android smartphones.
{"title":"Research of Intrusion Detection System on Android","authors":"Fangfang Yuan, Lidong Zhai, Yanan Cao, Li Guo","doi":"10.1109/SERVICES.2013.77","DOIUrl":"https://doi.org/10.1109/SERVICES.2013.77","url":null,"abstract":"In this paper, we proposed an intrusion detection system for detecting anomaly on Android smartphones. The intrusion detection system continuously monitors and collects the information of smartphone under normal conditions and attack state. It extracts various features obtained from the Android system, such as the network traffic of smartphones, battery consumption, CPU usage, the amount of running processes and so on. Then, it applies Bayes Classifying Algorithm to determine whether there is an invasion. In order to further analyze the Android system abnormalities and locate malicious software, along with system state monitoring the intrusion detection system monitors the process and network flow of the smartphone. Finally, experiments on the system which was designed in this paper have been carried out. Empirical results suggest that the proposed intrusion detection system is effective in detecting anomaly on Android smartphones.","PeriodicalId":169370,"journal":{"name":"2013 IEEE Ninth World Congress on Services","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129408317","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 : 2013-06-28DOI: 10.1109/SERVICES.2013.69
S. Sakr, Anna Liu
Elasticity has been recognized as one of the most appealing features for users of cloud services. It represents the ability to dynamically and rapidly scale up or down the allocated computing resources on demand. In practice, it is difficult to understand the elasticity requirements of a given application and workload, and to assess if the elasticity provided by a cloud service will meet these requirements. In this experience paper, we take the position that a deep understanding of the capabilities of cloud-hosted database services is a crucial requirement for cloud users in order to bring forward the vision of deploying data-intensive applications on cloud platforms. We argue that it is important that cloud users become able to paint a comprehensive picture of the relationship between the capabilities of the different type of cloud database services, the application characteristics and workloads, and the geographical distribution of the application clients and the underlying database replicas. We discuss the current elasticity capabilities of the different categories of cloud database services and identify some of the main challenges for deploying a truly elastic database tier on cloud environments. Finally, we propose a benchmarking mechanism that can evaluate the elasticity capabilities of cloud database services in different application scenarios and workloads.
{"title":"Is Your Cloud-Hosted Database Truly Elastic?","authors":"S. Sakr, Anna Liu","doi":"10.1109/SERVICES.2013.69","DOIUrl":"https://doi.org/10.1109/SERVICES.2013.69","url":null,"abstract":"Elasticity has been recognized as one of the most appealing features for users of cloud services. It represents the ability to dynamically and rapidly scale up or down the allocated computing resources on demand. In practice, it is difficult to understand the elasticity requirements of a given application and workload, and to assess if the elasticity provided by a cloud service will meet these requirements. In this experience paper, we take the position that a deep understanding of the capabilities of cloud-hosted database services is a crucial requirement for cloud users in order to bring forward the vision of deploying data-intensive applications on cloud platforms. We argue that it is important that cloud users become able to paint a comprehensive picture of the relationship between the capabilities of the different type of cloud database services, the application characteristics and workloads, and the geographical distribution of the application clients and the underlying database replicas. We discuss the current elasticity capabilities of the different categories of cloud database services and identify some of the main challenges for deploying a truly elastic database tier on cloud environments. Finally, we propose a benchmarking mechanism that can evaluate the elasticity capabilities of cloud database services in different application scenarios and workloads.","PeriodicalId":169370,"journal":{"name":"2013 IEEE Ninth World Congress on Services","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127443413","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 : 2013-06-28DOI: 10.1109/SERVICES.2013.40
Puya Ghazizadeh, R. Mukkamala, S. Olariu
Data integrity is a major concern in outsourced IT services like cloud computing. Cloud computing has become popular because of cost reductions, time saving and mobility in service. However data integrity is still an unresolved issue in cloud services. We present an efficient mechanism for evaluating data integrity in cloud database-as-a-service. Our approach is based on inserting fake tuples into the database. In our model the owner of the data is the only trusted party and the server as a service provider or any other users are not trusted. We refer to distrusted party as a potentially malicious attacker. In our approach we define generating functions to create fake tuples with uniform distribution. Malicious attackers are not able to distinguish between fake tuples and real tuples. Our approach does not use encryption which makes it more efficient. We explore the strengths and limitations of these generating functions by describing our approach.
{"title":"Data Integrity Evaluation in Cloud Database-as-a-Service","authors":"Puya Ghazizadeh, R. Mukkamala, S. Olariu","doi":"10.1109/SERVICES.2013.40","DOIUrl":"https://doi.org/10.1109/SERVICES.2013.40","url":null,"abstract":"Data integrity is a major concern in outsourced IT services like cloud computing. Cloud computing has become popular because of cost reductions, time saving and mobility in service. However data integrity is still an unresolved issue in cloud services. We present an efficient mechanism for evaluating data integrity in cloud database-as-a-service. Our approach is based on inserting fake tuples into the database. In our model the owner of the data is the only trusted party and the server as a service provider or any other users are not trusted. We refer to distrusted party as a potentially malicious attacker. In our approach we define generating functions to create fake tuples with uniform distribution. Malicious attackers are not able to distinguish between fake tuples and real tuples. Our approach does not use encryption which makes it more efficient. We explore the strengths and limitations of these generating functions by describing our approach.","PeriodicalId":169370,"journal":{"name":"2013 IEEE Ninth World Congress on Services","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123246028","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：