Analysing and modelling the characteristics of virtual machine (VM) usage gives cloud providers crucial information when dimensioning cloud infrastructure and designing appropriate allocation policies. In addition, administrators can use these models to build a normal behaviour profile of job requests, in order to differentiate malicious and normal activities. Finally, it allows researchers to design more accurate simulation environments. An open challenge is to empirically develop and verify an accurate model of VM usage for users in these applications. In this paper, we study the VM usage in the popular Amazon EC2 and Windows Azure cloud platforms, in terms of the VM request arrival and departure processes, and the number of live VMs in the system. We find that both the VM request arrival and departure processes exhibit self-similarity and follow the power law distribution. Our analysis also shows that the autoregressive integrated moving average (ARIMA) model can be used to fit and forecast the VM demands, which is an important requirement for managing the workload in cloud services.
{"title":"Analysing Virtual Machine Usage in Cloud Computing","authors":"Yi Han, Jeffrey Chan, C. Leckie","doi":"10.1109/SERVICES.2013.9","DOIUrl":"https://doi.org/10.1109/SERVICES.2013.9","url":null,"abstract":"Analysing and modelling the characteristics of virtual machine (VM) usage gives cloud providers crucial information when dimensioning cloud infrastructure and designing appropriate allocation policies. In addition, administrators can use these models to build a normal behaviour profile of job requests, in order to differentiate malicious and normal activities. Finally, it allows researchers to design more accurate simulation environments. An open challenge is to empirically develop and verify an accurate model of VM usage for users in these applications. In this paper, we study the VM usage in the popular Amazon EC2 and Windows Azure cloud platforms, in terms of the VM request arrival and departure processes, and the number of live VMs in the system. We find that both the VM request arrival and departure processes exhibit self-similarity and follow the power law distribution. Our analysis also shows that the autoregressive integrated moving average (ARIMA) model can be used to fit and forecast the VM demands, which is an important requirement for managing the workload in cloud services.","PeriodicalId":169370,"journal":{"name":"2013 IEEE Ninth World Congress on Services","volume":"43 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":"127349675","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.2013.27
M. Hale, R. Gamble
Mission critical information systems must be certified against a set of security controls to mitigate potential security incidents. Cloud service providers must in turn employ adequate security measures that conform to security controls expected by the organizational information systems they host. Since service implementation details are abstracted away by the cloud, organizations can only rely on service level agreements (SLAs) to assess the compliance of cloud security properties and processes. Various representation schema allow SLAs to embed service security terms, but are disconnected from documents regulating security controls. This paper demonstrates an extensible solution for building a compliance vocabulary that associates SLA terms with security controls. The terms allow services to express which security controls they comply with and enable at-a-glance comparison of security service offerings so organizations can distinguish among cloud service providers that best comply with security expectations. To exemplify the approach, we build a sample vocabulary of terms based on audit security controls from a standard set of governing documents and apply them to an SLA for an example cloud storage service. We assess the compatibility with existing SLAs and calculate the computational overhead associated with the use of our approach in service matchmaking.
{"title":"Building a Compliance Vocabulary to Embed Security Controls in Cloud SLAs","authors":"M. Hale, R. Gamble","doi":"10.1109/SERVICES.2013.27","DOIUrl":"https://doi.org/10.1109/SERVICES.2013.27","url":null,"abstract":"Mission critical information systems must be certified against a set of security controls to mitigate potential security incidents. Cloud service providers must in turn employ adequate security measures that conform to security controls expected by the organizational information systems they host. Since service implementation details are abstracted away by the cloud, organizations can only rely on service level agreements (SLAs) to assess the compliance of cloud security properties and processes. Various representation schema allow SLAs to embed service security terms, but are disconnected from documents regulating security controls. This paper demonstrates an extensible solution for building a compliance vocabulary that associates SLA terms with security controls. The terms allow services to express which security controls they comply with and enable at-a-glance comparison of security service offerings so organizations can distinguish among cloud service providers that best comply with security expectations. To exemplify the approach, we build a sample vocabulary of terms based on audit security controls from a standard set of governing documents and apply them to an SLA for an example cloud storage service. We assess the compatibility with existing SLAs and calculate the computational overhead associated with the use of our approach in service matchmaking.","PeriodicalId":169370,"journal":{"name":"2013 IEEE Ninth World Congress on Services","volume":"125 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":"115027142","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.2013.72
Xiang Li, Yushun Fan, Le Xin, Keman Huang, Yihang Luo, Yi Liu
Web services usually compose to workflows to satisfy complex demands. End-to-end execution time is widely seen as a key quality metric of web service workflows. That will be affected by many factors. This paper focuses on impacts of an important factor -- scheduling algorithm in services -- on collective end-to-end time characteristics of a set of web service workflows. We develop a novel simulator, in which workflows are simulated to execute. Impacts of different scheduling algorithms are evaluated through comparing simulation results. Simulation results indicate that maximal and average end-toend execution time of most workflows when using "earliest deadline first" (EDF) scheduling algorithm in services is significant shorter than that when using widely used "first in, first out" (FIFO) scheduling algorithm.
{"title":"Impacts of Scheduling Algorithms in Services on Collective End-to-End Execution Time Characteristics of Web Service Workflows","authors":"Xiang Li, Yushun Fan, Le Xin, Keman Huang, Yihang Luo, Yi Liu","doi":"10.1109/SERVICES.2013.72","DOIUrl":"https://doi.org/10.1109/SERVICES.2013.72","url":null,"abstract":"Web services usually compose to workflows to satisfy complex demands. End-to-end execution time is widely seen as a key quality metric of web service workflows. That will be affected by many factors. This paper focuses on impacts of an important factor -- scheduling algorithm in services -- on collective end-to-end time characteristics of a set of web service workflows. We develop a novel simulator, in which workflows are simulated to execute. Impacts of different scheduling algorithms are evaluated through comparing simulation results. Simulation results indicate that maximal and average end-toend execution time of most workflows when using \"earliest deadline first\" (EDF) scheduling algorithm in services is significant shorter than that when using widely used \"first in, first out\" (FIFO) scheduling algorithm.","PeriodicalId":169370,"journal":{"name":"2013 IEEE Ninth World Congress on Services","volume":"218 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":"115520376","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.2013.85
Wenyun Dai, Hao-peng Chen, Wenting Wang, X. Chen
Cloud computing has attracted increasing attention in recent years. With the growth in the number and frequency of applications being deployed into clouds, the burden of resource management of cloud providers is becoming heavier. The resource deployment must satisfy the need about the performance, availability and reliability of applications from the view of clients, but also ensure the high resource utilization of the cloud providers. In this paper, we design a multi-objective serial optimization with priorities approach, named RMORM, to find the resource deployment in clouds rapidly. This approach is of great practical significance and engineering value and scalable to add new constraints.
{"title":"RMORM: A framework of Multi-objective Optimization Resource Management in Clouds","authors":"Wenyun Dai, Hao-peng Chen, Wenting Wang, X. Chen","doi":"10.1109/SERVICES.2013.85","DOIUrl":"https://doi.org/10.1109/SERVICES.2013.85","url":null,"abstract":"Cloud computing has attracted increasing attention in recent years. With the growth in the number and frequency of applications being deployed into clouds, the burden of resource management of cloud providers is becoming heavier. The resource deployment must satisfy the need about the performance, availability and reliability of applications from the view of clients, but also ensure the high resource utilization of the cloud providers. In this paper, we design a multi-objective serial optimization with priorities approach, named RMORM, to find the resource deployment in clouds rapidly. This approach is of great practical significance and engineering value and scalable to add new constraints.","PeriodicalId":169370,"journal":{"name":"2013 IEEE Ninth World Congress on Services","volume":"35 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":"126568630","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.2013.68
Praveen Khethavath, Johnson P. Thomas, Eric Chan-Tin, Hong Liu
Cloud computing is an emerging field in computer science. Users are utilizing less of their own existing resources, while increasing usage of cloud resources. With the emergence of new technologies such as mobile devices, these devices are usually under-utilized, and can provide similar functionality to a cloud provided they are properly configured and managed. This paper proposes a Distributed Cloud Architecture to make use of independent resources provided by the devices/users. Resource discovery and allocation is critical in designing an efficient and practical distributed cloud. We propose using multi-valued distributed hash tables for efficient resource discovery. Leveraging the fact that there are many users providing resources such as CPU and memory, we define these resources under one key to easily locate devices with equivalent resources. We then propose a new auction mechanism, using a reserve bid formulated rationally by each user for the optimal allocation of discovered resources. Then we discuss how the Nash Equilibrium is achieved based on user requirements.
{"title":"Introducing a Distributed Cloud Architecture with Efficient Resource Discovery and Optimal Resource Allocation","authors":"Praveen Khethavath, Johnson P. Thomas, Eric Chan-Tin, Hong Liu","doi":"10.1109/SERVICES.2013.68","DOIUrl":"https://doi.org/10.1109/SERVICES.2013.68","url":null,"abstract":"Cloud computing is an emerging field in computer science. Users are utilizing less of their own existing resources, while increasing usage of cloud resources. With the emergence of new technologies such as mobile devices, these devices are usually under-utilized, and can provide similar functionality to a cloud provided they are properly configured and managed. This paper proposes a Distributed Cloud Architecture to make use of independent resources provided by the devices/users. Resource discovery and allocation is critical in designing an efficient and practical distributed cloud. We propose using multi-valued distributed hash tables for efficient resource discovery. Leveraging the fact that there are many users providing resources such as CPU and memory, we define these resources under one key to easily locate devices with equivalent resources. We then propose a new auction mechanism, using a reserve bid formulated rationally by each user for the optimal allocation of discovered resources. Then we discuss how the Nash Equilibrium is achieved based on user requirements.","PeriodicalId":169370,"journal":{"name":"2013 IEEE Ninth World Congress on Services","volume":"19 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":"122282318","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.2013.46
M. Hale, M. T. Gamble, R. Gamble
The service cloud model allows hosted services to be dynamically provisioned and composed as part of larger, more complex cloud applications. Compatibility of interaction and quality of service are important to provisioning similar services available in the cloud to a client request. Auditing individual services, the composition and its outcome, and the overall cloud resources, used for monetary assessment or to ensure critical operations, also provide properties for reasoning over service and composition capabilities. Security policies and potential violations pose a threat to the composition since sensitive data may be leaked if information flow control guarantees cannot be proven. Service engineering lacks design principles and an expression infrastructure for formal representation and reasoning within a service cloud model. Reasoning over service compositions requires a formal language that can express multiple service and cloud properties. In this paper, we use coordination language techniques to express services, their interaction capabilities and information sharing constraints, and the infrastructure of a service cloud model in which services can be accurately provisioned, composed and reasoned over to provide necessary guarantees. We discuss lessons learned from the process of formulating the service representation and cloud model infrastructure.
{"title":"A Design and Verification Framework for Service Composition in the Cloud","authors":"M. Hale, M. T. Gamble, R. Gamble","doi":"10.1109/SERVICES.2013.46","DOIUrl":"https://doi.org/10.1109/SERVICES.2013.46","url":null,"abstract":"The service cloud model allows hosted services to be dynamically provisioned and composed as part of larger, more complex cloud applications. Compatibility of interaction and quality of service are important to provisioning similar services available in the cloud to a client request. Auditing individual services, the composition and its outcome, and the overall cloud resources, used for monetary assessment or to ensure critical operations, also provide properties for reasoning over service and composition capabilities. Security policies and potential violations pose a threat to the composition since sensitive data may be leaked if information flow control guarantees cannot be proven. Service engineering lacks design principles and an expression infrastructure for formal representation and reasoning within a service cloud model. Reasoning over service compositions requires a formal language that can express multiple service and cloud properties. In this paper, we use coordination language techniques to express services, their interaction capabilities and information sharing constraints, and the infrastructure of a service cloud model in which services can be accurately provisioned, composed and reasoned over to provide necessary guarantees. We discuss lessons learned from the process of formulating the service representation and cloud model infrastructure.","PeriodicalId":169370,"journal":{"name":"2013 IEEE Ninth World Congress on Services","volume":"55 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":"131888928","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.2013.49
Jiuyun Xu, Ruru Zhang, Kunming Xing, S. Reiff-Marganiec
With the rapid development of web service standards and technology, the number of web services on Internet is increasing rapidly. Consequently, discovering the right service to meet a user's requirements quickly and accurately is crucial for the service community. Many web service discovery methods use web service models with semantic descriptions based on ontologies, allowing to apply logical reasoning to the discovery task. However, requiring logical reasoning can lead to sacrifices in efficiency of web services discovery. To address this problem, this paper proposes a combination of ontology encoding with the similarity of information content approach. We encode the concepts in the ontology in a binary encoding in order to improve the discovery efficiency and then we calculate the semantic similarity of information content between services. Validation of efficiency of the proposed approach is conducted through an experiment using the owls-tc2.0 as benchmark test set. The experimental results show that the proposed method not only can improve the efficiency of service discovery, but also can significantly improve the accuracy of service discovery compared with other discovery methods.
{"title":"Service Discovery Using Ontology Encoding Enhanced by Similarity of Information Content","authors":"Jiuyun Xu, Ruru Zhang, Kunming Xing, S. Reiff-Marganiec","doi":"10.1109/SERVICES.2013.49","DOIUrl":"https://doi.org/10.1109/SERVICES.2013.49","url":null,"abstract":"With the rapid development of web service standards and technology, the number of web services on Internet is increasing rapidly. Consequently, discovering the right service to meet a user's requirements quickly and accurately is crucial for the service community. Many web service discovery methods use web service models with semantic descriptions based on ontologies, allowing to apply logical reasoning to the discovery task. However, requiring logical reasoning can lead to sacrifices in efficiency of web services discovery. To address this problem, this paper proposes a combination of ontology encoding with the similarity of information content approach. We encode the concepts in the ontology in a binary encoding in order to improve the discovery efficiency and then we calculate the semantic similarity of information content between services. Validation of efficiency of the proposed approach is conducted through an experiment using the owls-tc2.0 as benchmark test set. The experimental results show that the proposed method not only can improve the efficiency of service discovery, but also can significantly improve the accuracy of service discovery compared with other discovery methods.","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":"114382304","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.2013.84
Melvin Greer
The objective of a Cloud Broker is to allow an enterprise to focus on providing new and innovative cloud-based solutions to their user community while managing some of the underlying risks, costs, and complexities.
{"title":"The New Cloud on the Horizon -- Cloud Brokers","authors":"Melvin Greer","doi":"10.1109/SERVICES.2013.84","DOIUrl":"https://doi.org/10.1109/SERVICES.2013.84","url":null,"abstract":"The objective of a Cloud Broker is to allow an enterprise to focus on providing new and innovative cloud-based solutions to their user community while managing some of the underlying risks, costs, and complexities.","PeriodicalId":169370,"journal":{"name":"2013 IEEE Ninth World Congress on Services","volume":"35 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":"114721969","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.2013.12
Khaleel W. Mershad, Abdulrahman Kaitoua, H. Artail, M. Saghir, Hazem M. Hajj
Cloud computing is increasingly becoming a desirable and foundational element in international enterprise computing. There are many companies which design, develop, and offer cloud technologies. However, cloud providers are still like lone islands. While current cloud computing models have provided significant benefits of maximizing the use of resources within a cloud, the current solutions still face many challenges including the lack of cross-leverage of available resources across clouds, the need to move data between clouds in some cases, and the lack of a global efficient cooperation between clouds. In [1], we addressed some of these challenges by providing an approach that enables various cloud providers to cooperate in order to execute, together, common requests. In this paper, we illustrate several enhancements to our work in [1] which focus on integrating hardware acceleration with the cloud services. We extend the Hadoop framework by adding provisions for hardware acceleration with Field Programmable Gate Arrays (FPGAs) within the cloud, for multi-cloud interaction, and for global cloud management. Hardware acceleration is used to offload computations when needed or as a service within the clouds. It can provide additional sources of revenues, reduced operating costs, and increased resource utilization. We derive a mathematical model for evaluating the performance of the most important entity in our system under various conditions.
{"title":"A Framework for Multi-cloud Cooperation with Hardware Reconfiguration Support","authors":"Khaleel W. Mershad, Abdulrahman Kaitoua, H. Artail, M. Saghir, Hazem M. Hajj","doi":"10.1109/SERVICES.2013.12","DOIUrl":"https://doi.org/10.1109/SERVICES.2013.12","url":null,"abstract":"Cloud computing is increasingly becoming a desirable and foundational element in international enterprise computing. There are many companies which design, develop, and offer cloud technologies. However, cloud providers are still like lone islands. While current cloud computing models have provided significant benefits of maximizing the use of resources within a cloud, the current solutions still face many challenges including the lack of cross-leverage of available resources across clouds, the need to move data between clouds in some cases, and the lack of a global efficient cooperation between clouds. In [1], we addressed some of these challenges by providing an approach that enables various cloud providers to cooperate in order to execute, together, common requests. In this paper, we illustrate several enhancements to our work in [1] which focus on integrating hardware acceleration with the cloud services. We extend the Hadoop framework by adding provisions for hardware acceleration with Field Programmable Gate Arrays (FPGAs) within the cloud, for multi-cloud interaction, and for global cloud management. Hardware acceleration is used to offload computations when needed or as a service within the clouds. It can provide additional sources of revenues, reduced operating costs, and increased resource utilization. We derive a mathematical model for evaluating the performance of the most important entity in our system under various conditions.","PeriodicalId":169370,"journal":{"name":"2013 IEEE Ninth World Congress on Services","volume":"47 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":"124683308","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.2013.53
Michael Smit, Mark Shtern, B. Simmons, Marin Litoiu
The sharing of large and interesting Big Data in cloud environments can be achieved using data-as-a-service, where a provider offers data to interested users. In enhanced data-as-a-service, the data provider also supplies compute infrastructure, allowing users to run analytics tasks local to the data and reducing the (expensive and slow) transmission of data over networks. This paper describes a services-based ecosystem that allows providers to precisely share portions of their data with users, using a model where users submit MapReduce jobs that run on the provider's Hadoop infrastructure. Providers are given mechanisms to filter, segment, and/or transform data before it reaches the user's task. The ecosystem also allows for intermediaries who offer value-added filtrations, segmentations, or transformations of the data (for example, pre-filtering a dataset to only include high-income users). We describe the RESTful services required to enable this ecosystem, introduce a prototype to demonstrate the concept, and present experiments using this ecosystem to both provide and analyze different segments of a single large data set.
{"title":"Enabling an Enhanced Data-as-a-Service Ecosystem","authors":"Michael Smit, Mark Shtern, B. Simmons, Marin Litoiu","doi":"10.1109/SERVICES.2013.53","DOIUrl":"https://doi.org/10.1109/SERVICES.2013.53","url":null,"abstract":"The sharing of large and interesting Big Data in cloud environments can be achieved using data-as-a-service, where a provider offers data to interested users. In enhanced data-as-a-service, the data provider also supplies compute infrastructure, allowing users to run analytics tasks local to the data and reducing the (expensive and slow) transmission of data over networks. This paper describes a services-based ecosystem that allows providers to precisely share portions of their data with users, using a model where users submit MapReduce jobs that run on the provider's Hadoop infrastructure. Providers are given mechanisms to filter, segment, and/or transform data before it reaches the user's task. The ecosystem also allows for intermediaries who offer value-added filtrations, segmentations, or transformations of the data (for example, pre-filtering a dataset to only include high-income users). We describe the RESTful services required to enable this ecosystem, introduce a prototype to demonstrate the concept, and present experiments using this ecosystem to both provide and analyze different segments of a single large data set.","PeriodicalId":169370,"journal":{"name":"2013 IEEE Ninth World Congress on Services","volume":"10 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":"129426769","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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