The Experts below are selected from a list of 114 Experts worldwide ranked by ideXlab platform
Hesham Hassan - One of the best experts on this subject based on the ideXlab platform.
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Implementing generic PaaS deployment API: repackaging and deploying applications on heterogeneous PaaS platforms
International Journal of Big Data Intelligence, 2016Co-Authors: Eman Hossny, Sherif Khattab, Fatma Omara, Hesham HassanAbstract:The cloud platform-as-a-service (PaaS) model provides developers with the ability to deploy and manage their applications remotely in the cloud and pay only for actual usage hours. Currently, there is no standard API for PaaS deployment and management; each PaaS provider [e.g., Google Appengine (GAE), OpenShift (OS), Cloud Foundry (CF), and Windows Azure] has its own proprietary APIs. This lack of standardisation adds a layer of complexity to application deployment and migration between heterogeneous PaaS platforms because of API incompatibility. A standard (generic) PaaS deployment API overcomes the previously mentioned PaaS API heterogeneity. A generic open-source API, namely the COAPS API, has been proposed to support deployment and management of applications on CF and OS PaaS platforms. This work implements COAPS deployment API to include the GAE PaaS. Whereas both CF and OS PaaS platforms use the same application packaging, deploying the same application on GAE requires application repackaging. We evaluated our work using a case study in which the same application is automatically deployed on CF and GAE.
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A case study for deploying applications on heterogeneous paas platforms
Proceedings - 2013 International Conference on Cloud Computing and Big Data CLOUDCOM-ASIA 2013, 2013Co-Authors: Eman Hossny, Sherif Khattab, Fatma Omara, Hesham HassanAbstract:Cloud Platform-as-a-Service (PaaS) model provides developers with the ability to deploy and manage their applications remotely through the cloud and pay only for actual usage hours. Currently, there is no standard API for PaaS management and deployment, each PaaS provider has its own specific APIs (e.g., Google Appengine (GAE), OpenShift (OS), Cloud Foundry (CF), and Windows Azure). Therefore, deploying applications on heterogeneous PaaS platforms is considered one of the challenges that make some developers worry about using PaaS services. Such challenge can be solved by providing a standard or a generic API that overcomes PaaS API heterogeneity. The aim of this paper is to report on our effort to use and extend a generic API, namely the COAPS API, which supports deployment and management on Cloud Foundry and OpenShift. According to the work in this paper, an extension of the COAPS API is provided to include the deployment on Google Appengine as a case study to demonstrate COAPS API generality.
Shiqiang Yang - One of the best experts on this subject based on the ideXlab platform.
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CoNEXT - Cloud-based social application deployment using local processing and global distribution
Proceedings of the 8th international conference on Emerging networking experiments and technologies - CoNEXT '12, 2012Co-Authors: Zhi Wang, Baochun Li, Shiqiang YangAbstract:Social applications represent a paradigm shift on how the Internet is to be used, and have already changed the way we work, live, and play. When it comes to deploying social applications, cloud computing platforms are used to meet the Internet-scale, self-propagating, and fast-growing demands from these applications. Yet, to deploy social media applications in the most effective and economic fashion, we need to strategically design and follow a set of theoretical and practical principles. In this paper, we seek to design a set of new principles to guide social application deployment. Learning from large-scale measurement-based observations using a real-world social application, the gist of our principles is to detach the typically integrated "collection → processing → distribution" work ows in social applications into separate local processing and global distribution procedures, which can be effectively deployed using different cloud services. Moreover, based on a predictive model of regional propagation, we formulate the resource allocation problems in the processes of collecting/processing and distributing content as two optimization problems, which can be solved by efficient algorithms. Finally, based on our theoretical design, we have implemented an example social application on Amazon EC2 and Google Appengine, where IaaS-based computation instances perform content collection and processing, and the PaaS-based platform is employed to distribute the contents that are widely propagating. Our PlanetLab-based trace-driven experiments have further confirmed the superiority of our design.
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Cloud-based social application deployment using local processing and global distribution
CoNEXT, 2012Co-Authors: Zhi Wang, Lifeng Sun, Baochun Li, Shiqiang YangAbstract:Social applications represent a paradigm shift on how the Internet is to be used, and have already changed the way we work, live, and play. When it comes to deploying social ap- plications, cloud computing platforms are used to meet the Internet-scale, self-propagating, and fast-growing demands from these applications. Yet, to deploy social media applica- tions in the most effective and economic fashion, we need to strategically design and follow a set of theoretical and prac- tical principles. In this paper, we seek to design a set of new principles to guide social application deployment. Learn- ing from large-scale measurement-based observations using a real-world social application, the gist of our principles is to detach the typically integrated “collection → processing → distribution” workflows in social applications into separate local processing and global distribution procedures, which can be effectively deployed using different cloud services. More- over, based on a predictive model of regional propagation, we formulate the resource allocation problems in the pro- cesses of collecting/processing and distributing content as two optimization problems, which can be solved by efficient algorithms. Finally, based on our theoretical design, we have implemented an example social application on Amazon EC2 and Google Appengine, where IaaS-based computation in- stances perform content collection and processing, and the PaaS-based platform is employed to distribute the contents that are widely propagating. Our PlanetLab-based trace- driven experiments have further confirmed the superiority of our design.
He Zhang - One of the best experts on this subject based on the ideXlab platform.
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A Practical Methodology for Cloud Services Evaluation
2015Co-Authors: He ZhangAbstract:Abstract—Given an increasing number of Cloud services available in the market, evaluating candidate Cloud services is crucial and beneficial for both service customers (e.g. cost-benefit analysis) and providers (e.g. direction of improvement). When it comes to performing any evaluation, a suitable methodology is inevitably required to direct experimental implementations. Nevertheless, there is still a lack of a sound methodology to guide the evaluation of Cloud services. By borrowing the lessons from evaluation of traditional computing systems, referring to the guidelines for Design of Experiments (DOE), and summarizing the existing experiences of real experimental studies, we proposed a generic methodology for Cloud services evaluation. Furthermore, we have established a pre-experimental knowledge base and specified corresponding suggestions to make this methodology more practical in the Cloud Computing domain. Through evaluating the Google Appengine Python runtime as a preliminary validation, we show that Cloud evaluators may achieve more rational and convincing experimental results and conclusions following such an evaluation methodology
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2013 IEEE Ninth World Congress on Services CEEM: A Practical Methodology for Cloud Services Evaluation
2014Co-Authors: He ZhangAbstract:Abstract—Given an increasing number of Cloud services available in the market, evaluating candidate Cloud services is crucial and beneficial for both service customers (e.g. costbenefit analysis) and providers (e.g. direction of improvement). When it comes to performing any evaluation, a suitable methodology is inevitably required to direct experimental implementations. Nevertheless, there is still a lack of a sound methodology to guide the evaluation of Cloud services. By borrowing the lessons from evaluation of traditional computing systems, referring to the guidelines for Design of Experiments (DOE), and summarizing the existing experiences of real experimental studies, we proposed a generic Cloud Evaluation Experiment Methodology (CEEM) for Cloud services evaluation. Furthermore, we have established a pre-experimental knowledge base and specified corresponding suggestions to make this methodology more practical in the Cloud Computing domain. Through evaluating the Google Appengine Python runtime as a preliminary validation, we show that Cloud evaluators may achieve more rational and convincing experimental results and conclusions following such an evaluation methodology
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SERVICES - CEEM: A Practical Methodology for Cloud Services Evaluation
2013 IEEE Ninth World Congress on Services, 2013Co-Authors: Liam O'brien, He ZhangAbstract:Given an increasing number of Cloud services available in the market, evaluating candidate Cloud services is crucial and beneficial for both service customers (e.g. cost benefit analysis) and providers (e.g. direction of improvement). When it comes to performing any evaluation, a suitable methodology is inevitably required to direct experimental implementations. Nevertheless, there is still a lack of a sound methodology to guide the evaluation of Cloud services. By borrowing the lessons from evaluation of traditional computing systems, referring to the guidelines for Design of Experiments (DOE), and summarizing the existing experiences of real experimental studies, we proposed a generic Cloud Evaluation Experiment Methodology (CEEM) for Cloud services evaluation. Furthermore, we have established a pre-experimental knowledge base and specified corresponding suggestions to make this methodology more practical in the Cloud Computing domain. Through evaluating the Google Appengine Python runtime as a preliminary validation, we show that Cloud evaluators may achieve more rational and convincing experimental results and conclusions following such an evaluation methodology.
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CEEM: A Practical Methodology for Cloud Services Evaluation
'Institute of Electrical and Electronics Engineers (IEEE)', 2013Co-Authors: He Zhang, Li Zheng, O'brien LiamAbstract:Abstract—Given an increasing number of Cloud services available in the market, evaluating candidate Cloud services is crucial and beneficial for both service customers (e.g. costbenefit analysis) and providers (e.g. direction of improvement). When it comes to performing any evaluation, a suitable methodology is inevitably required to direct experimental implementations. Nevertheless, there is still a lack of a sound methodology to guide the evaluation of Cloud services. By borrowing the lessons from evaluation of traditional computing systems, referring to the guidelines for Design of Experiments (DOE), and summarizing the existing experiences of real experimental studies, we proposed a generic Cloud Evaluation Experiment Methodology (CEEM) for Cloud services evaluation. Furthermore, we have established a pre-experimental knowledge base and specified corresponding suggestions to make this methodology more practical in the Cloud Computing domain. Through evaluating the Google Appengine Python runtime as a preliminary validation, we show that Cloud evaluators may achieve more rational and convincing experimental results and conclusions following such an evaluation methodology
Klemke Roland - One of the best experts on this subject based on the ideXlab platform.
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ARLearn and StreetLearn software for virtual reality and augmented reality multi user learning games
2012Co-Authors: Ternier Stefaan, Klemke RolandAbstract:Ternier, S., & Klemke, R. (2011). ARLearn and StreetLearn software for virtual reality and augmented reality multi user learning games (Version 1.0) [Computer software]. Heerlen, The Netherlands: Open Universiteit in the Netherlands.The software package is deployable on Google Appengine. It can be used to create and run augmented reality games with mobile devices and virtual reality games with a Google streetview based front-end. The version uploaded here represents a snapshot. The latest version is accessible here: http://code.Google.com/p/arlearn/Surfnet/Kennisnet Innovation Programme 201
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ARLearn and StreetLearn software for virtual reality and augmented reality multi user learning games
2012Co-Authors: Ternier Stefaan, Klemke RolandAbstract:Ternier, S., & Klemke, R. (2011). ARLearn and StreetLearn software for virtual reality and augmented reality multi user learning games (Version 1.0) [Software Documentation]. Heerlen, The Netherlands: Open Universiteit in the Netherlands.Documentation for the Streetlearn/ARlearn software package. The software package is deployable on Google Appengine. It can be used to create and run augmented reality games with mobile devices and virtual reality games with a Google streetview based front-end. The version uploaded here represents a snapshot. The latest version is accessible here: http://code.Google.com/p/arlearn/Surfnet/Kennisnet Innovation Programme 201
Eman Hossny - One of the best experts on this subject based on the ideXlab platform.
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Implementing generic PaaS deployment API: repackaging and deploying applications on heterogeneous PaaS platforms
International Journal of Big Data Intelligence, 2016Co-Authors: Eman Hossny, Sherif Khattab, Fatma Omara, Hesham HassanAbstract:The cloud platform-as-a-service (PaaS) model provides developers with the ability to deploy and manage their applications remotely in the cloud and pay only for actual usage hours. Currently, there is no standard API for PaaS deployment and management; each PaaS provider [e.g., Google Appengine (GAE), OpenShift (OS), Cloud Foundry (CF), and Windows Azure] has its own proprietary APIs. This lack of standardisation adds a layer of complexity to application deployment and migration between heterogeneous PaaS platforms because of API incompatibility. A standard (generic) PaaS deployment API overcomes the previously mentioned PaaS API heterogeneity. A generic open-source API, namely the COAPS API, has been proposed to support deployment and management of applications on CF and OS PaaS platforms. This work implements COAPS deployment API to include the GAE PaaS. Whereas both CF and OS PaaS platforms use the same application packaging, deploying the same application on GAE requires application repackaging. We evaluated our work using a case study in which the same application is automatically deployed on CF and GAE.
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A case study for deploying applications on heterogeneous paas platforms
Proceedings - 2013 International Conference on Cloud Computing and Big Data CLOUDCOM-ASIA 2013, 2013Co-Authors: Eman Hossny, Sherif Khattab, Fatma Omara, Hesham HassanAbstract:Cloud Platform-as-a-Service (PaaS) model provides developers with the ability to deploy and manage their applications remotely through the cloud and pay only for actual usage hours. Currently, there is no standard API for PaaS management and deployment, each PaaS provider has its own specific APIs (e.g., Google Appengine (GAE), OpenShift (OS), Cloud Foundry (CF), and Windows Azure). Therefore, deploying applications on heterogeneous PaaS platforms is considered one of the challenges that make some developers worry about using PaaS services. Such challenge can be solved by providing a standard or a generic API that overcomes PaaS API heterogeneity. The aim of this paper is to report on our effort to use and extend a generic API, namely the COAPS API, which supports deployment and management on Cloud Foundry and OpenShift. According to the work in this paper, an extension of the COAPS API is provided to include the deployment on Google Appengine as a case study to demonstrate COAPS API generality.
