�ݺ�ߣshows by User: StephanieCHALLITA

�ݺ�ߣshows by User: StephanieCHALLITA / http://www.slideshare.net/images/logo.gif �ݺ�ߣshows by User: StephanieCHALLITA / Sun, 11 Oct 2020 17:17:01 GMT �ݺ�ߣShare feed for �ݺ�ߣshows by User: StephanieCHALLITA Automated Reverse-Engineering of a Cloud API /slideshow/automated-reverseengineering-of-a-cloud-api/238831831 ens-seminar-201011171701
Google Cloud Platform (GCP) is one of the leaders among cloud APIs. It has gained notable expansion due to its suite of public cloud services that it based on a huge, solid infrastructure. GCP allows developers to use these services by accessing GCP RESTful API that is described through HTML pages on its website. However, the documentation of GCP API is written in natural language (English prose) and therefore shows several drawbacks, such as Informal Heterogeneous Documentation, Imprecise Types, Implicit Attribute Metadata, Hidden Links, Redundancy and Lack of Visual Support. To avoid confusion and misunderstandings, the cloud developers obviously need a precise specification of the knowledge and activities in GCP. Therefore, this paper introduces GCP MODEL, an inferred formal model-driven specification of GCP which describes without ambiguity the resources offered by GCP.]]>
Google Cloud Platform (GCP) is one of the leaders among cloud APIs. It has gained notable expansion due to its suite of public cloud services that it based on a huge, solid infrastructure. GCP allows developers to use these services by accessing GCP RESTful API that is described through HTML pages on its website. However, the documentation of GCP API is written in natural language (English prose) and therefore shows several drawbacks, such as Informal Heterogeneous Documentation, Imprecise Types, Implicit Attribute Metadata, Hidden Links, Redundancy and Lack of Visual Support. To avoid confusion and misunderstandings, the cloud developers obviously need a precise specification of the knowledge and activities in GCP. Therefore, this paper introduces GCP MODEL, an inferred formal model-driven specification of GCP which describes without ambiguity the resources offered by GCP.]]> Sun, 11 Oct 2020 17:17:01 GMT /slideshow/automated-reverseengineering-of-a-cloud-api/238831831 StephanieCHALLITA@slideshare.net(StephanieCHALLITA) Automated Reverse-Engineering of a Cloud API StephanieCHALLITA Google Cloud Platform (GCP) is one of the leaders among cloud APIs. It has gained notable expansion due to its suite of public cloud services that it based on a huge, solid infrastructure. GCP allows developers to use these services by accessing GCP RESTful API that is described through HTML pages on its website. However, the documentation of GCP API is written in natural language (English prose) and therefore shows several drawbacks, such as Informal Heterogeneous Documentation, Imprecise Types, Implicit Attribute Metadata, Hidden Links, Redundancy and Lack of Visual Support. To avoid confusion and misunderstandings, the cloud developers obviously need a precise specification of the knowledge and activities in GCP. Therefore, this paper introduces GCP MODEL, an inferred formal model-driven specification of GCP which describes without ambiguity the resources offered by GCP. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/ens-seminar-201011171701-thumbnail.jpg?width=120&height=120&fit=bounds" /> Google Cloud Platform (GCP) is one of the leaders among cloud APIs. It has gained notable expansion due to its suite of public cloud services that it based on a huge, solid infrastructure. GCP allows developers to use these services by accessing GCP RESTful API that is described through HTML pages on its website. However, the documentation of GCP API is written in natural language (English prose) and therefore shows several drawbacks, such as Informal Heterogeneous Documentation, Imprecise Types, Implicit Attribute Metadata, Hidden Links, Redundancy and Lack of Visual Support. To avoid confusion and misunderstandings, the cloud developers obviously need a precise specification of the knowledge and activities in GCP. Therefore, this paper introduces GCP MODEL, an inferred formal model-driven specification of GCP which describes without ambiguity the resources offered by GCP.

Automated Reverse-Engineering of a Cloud API from St辿phanie Challita

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0 Stéphanie Challita's PhD Defense Presentation /slideshow/stphanie-challitas-phd-defense-presentation-127248302/127248302 challitaphddefense-190103232011
With the advent of cloud computing, different cloud providers with heterogeneous cloud services and Application Programming Interfaces (APIs) have emerged. This heterogeneity complicates the implementation of an interoperable multi-cloud system. Among the multi-cloud interoperability solutions, Model-Driven Engineering (MDE) has proven to be quite advantageous and is the mostly adopted methodology to rise in abstraction and mask the heterogeneity of the cloud. However, most of the existing MDE solutions for the cloud are not representative of the cloud APIs and lack of formalization. To address these shortcomings, I present in this thesis an approach based on Open Cloud Computing Interface (OCCI) standard, MDE, and formal methods. I provide two major contributions implemented in the context of the OCCIware project. First, I propose an approach based on reverse-engineering to extract knowledge from the ambiguous textual documentation of cloud APIs and to enhance its representation using MDE techniques. This approach is applied to Google Cloud Platform (GCP), where I provide GCP Model, a precise model-driven specification for GCP that is automatically inferred from GCP textual documentation. Second, I propose the fclouds framework to achieve semantic interoperability in multi-clouds, i.e., to identify the common concepts between cloud APIs and to reason over them. The fclouds language is a formalization of OCCI concepts and operational semantics in Alloy formal specification language. To demonstrate the effectiveness of the fclouds language, I formally specify thirteen case studies and verify their properties.]]>
With the advent of cloud computing, different cloud providers with heterogeneous cloud services and Application Programming Interfaces (APIs) have emerged. This heterogeneity complicates the implementation of an interoperable multi-cloud system. Among the multi-cloud interoperability solutions, Model-Driven Engineering (MDE) has proven to be quite advantageous and is the mostly adopted methodology to rise in abstraction and mask the heterogeneity of the cloud. However, most of the existing MDE solutions for the cloud are not representative of the cloud APIs and lack of formalization. To address these shortcomings, I present in this thesis an approach based on Open Cloud Computing Interface (OCCI) standard, MDE, and formal methods. I provide two major contributions implemented in the context of the OCCIware project. First, I propose an approach based on reverse-engineering to extract knowledge from the ambiguous textual documentation of cloud APIs and to enhance its representation using MDE techniques. This approach is applied to Google Cloud Platform (GCP), where I provide GCP Model, a precise model-driven specification for GCP that is automatically inferred from GCP textual documentation. Second, I propose the fclouds framework to achieve semantic interoperability in multi-clouds, i.e., to identify the common concepts between cloud APIs and to reason over them. The fclouds language is a formalization of OCCI concepts and operational semantics in Alloy formal specification language. To demonstrate the effectiveness of the fclouds language, I formally specify thirteen case studies and verify their properties.]]> Thu, 03 Jan 2019 23:20:11 GMT /slideshow/stphanie-challitas-phd-defense-presentation-127248302/127248302 StephanieCHALLITA@slideshare.net(StephanieCHALLITA) Stéphanie Challita's PhD Defense Presentation StephanieCHALLITA With the advent of cloud computing, different cloud providers with heterogeneous cloud services and Application Programming Interfaces (APIs) have emerged. This heterogeneity complicates the implementation of an interoperable multi-cloud system. Among the multi-cloud interoperability solutions, Model-Driven Engineering (MDE) has proven to be quite advantageous and is the mostly adopted methodology to rise in abstraction and mask the heterogeneity of the cloud. However, most of the existing MDE solutions for the cloud are not representative of the cloud APIs and lack of formalization. To address these shortcomings, I present in this thesis an approach based on Open Cloud Computing Interface (OCCI) standard, MDE, and formal methods. I provide two major contributions implemented in the context of the OCCIware project. First, I propose an approach based on reverse-engineering to extract knowledge from the ambiguous textual documentation of cloud APIs and to enhance its representation using MDE techniques. This approach is applied to Google Cloud Platform (GCP), where I provide GCP Model, a precise model-driven specification for GCP that is automatically inferred from GCP textual documentation. Second, I propose the fclouds framework to achieve semantic interoperability in multi-clouds, i.e., to identify the common concepts between cloud APIs and to reason over them. The fclouds language is a formalization of OCCI concepts and operational semantics in Alloy formal specification language. To demonstrate the effectiveness of the fclouds language, I formally specify thirteen case studies and verify their properties. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/challitaphddefense-190103232011-thumbnail.jpg?width=120&height=120&fit=bounds" /> With the advent of cloud computing, different cloud providers with heterogeneous cloud services and Application Programming Interfaces (APIs) have emerged. This heterogeneity complicates the implementation of an interoperable multi-cloud system. Among the multi-cloud interoperability solutions, Model-Driven Engineering (MDE) has proven to be quite advantageous and is the mostly adopted methodology to rise in abstraction and mask the heterogeneity of the cloud. However, most of the existing MDE solutions for the cloud are not representative of the cloud APIs and lack of formalization. To address these shortcomings, I present in this thesis an approach based on Open Cloud Computing Interface (OCCI) standard, MDE, and formal methods. I provide two major contributions implemented in the context of the OCCIware project. First, I propose an approach based on reverse-engineering to extract knowledge from the ambiguous textual documentation of cloud APIs and to enhance its representation using MDE techniques. This approach is applied to Google Cloud Platform (GCP), where I provide GCP Model, a precise model-driven specification for GCP that is automatically inferred from GCP textual documentation. Second, I propose the fclouds framework to achieve semantic interoperability in multi-clouds, i.e., to identify the common concepts between cloud APIs and to reason over them. The fclouds language is a formalization of OCCI concepts and operational semantics in Alloy formal specification language. To demonstrate the effectiveness of the fclouds language, I formally specify thirteen case studies and verify their properties.

St辿phanie Challita's PhD Defense Presentation from St辿phanie Challita

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0 MoDMaCAO: Model-Driven Configuration Management of Cloud Applications with OCCI (CLOSER'2018) /slideshow/modmacao-modeldriven-configuration-management-of-cloud-applications-with-occi-closer2018-104836193/104836193 closer2018modmacaopresentationfinal-180708152110
To tackle the cloud-provider lock-in, the Open Grid Forum (OGF) is developing the Open Cloud Computing Interface (OCCI), a standardized interface for managing any kind of cloud resources. Besides the OCCI Core model, which defines the basic modeling elements for cloud resources, the OGF also defines extensions that reflect the requirements of different cloud service levels, such as IaaS and PaaS. However, so far the OCCI PaaS extension is very coarse grained and lacks of supporting use cases and implementations. Especially, it does not define how the components of the application itself can be managed. In this paper, we present a model-driven framework that extends the OCCI PaaS extension and is able to use different configuration management tools to manage the whole lifecycle of cloud applications. We demonstrate the feasibility of the approach by presenting four different use cases and prototypical implementations for three different configuration management tools.]]>
To tackle the cloud-provider lock-in, the Open Grid Forum (OGF) is developing the Open Cloud Computing Interface (OCCI), a standardized interface for managing any kind of cloud resources. Besides the OCCI Core model, which defines the basic modeling elements for cloud resources, the OGF also defines extensions that reflect the requirements of different cloud service levels, such as IaaS and PaaS. However, so far the OCCI PaaS extension is very coarse grained and lacks of supporting use cases and implementations. Especially, it does not define how the components of the application itself can be managed. In this paper, we present a model-driven framework that extends the OCCI PaaS extension and is able to use different configuration management tools to manage the whole lifecycle of cloud applications. We demonstrate the feasibility of the approach by presenting four different use cases and prototypical implementations for three different configuration management tools.]]> Sun, 08 Jul 2018 15:21:10 GMT /slideshow/modmacao-modeldriven-configuration-management-of-cloud-applications-with-occi-closer2018-104836193/104836193 StephanieCHALLITA@slideshare.net(StephanieCHALLITA) MoDMaCAO: Model-Driven Configuration Management of Cloud Applications with OCCI (CLOSER'2018) StephanieCHALLITA To tackle the cloud-provider lock-in, the Open Grid Forum (OGF) is developing the Open Cloud Computing Interface (OCCI), a standardized interface for managing any kind of cloud resources. Besides the OCCI Core model, which defines the basic modeling elements for cloud resources, the OGF also defines extensions that reflect the requirements of different cloud service levels, such as IaaS and PaaS. However, so far the OCCI PaaS extension is very coarse grained and lacks of supporting use cases and implementations. Especially, it does not define how the components of the application itself can be managed. In this paper, we present a model-driven framework that extends the OCCI PaaS extension and is able to use different configuration management tools to manage the whole lifecycle of cloud applications. We demonstrate the feasibility of the approach by presenting four different use cases and prototypical implementations for three different configuration management tools. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/closer2018modmacaopresentationfinal-180708152110-thumbnail.jpg?width=120&height=120&fit=bounds" /> To tackle the cloud-provider lock-in, the Open Grid Forum (OGF) is developing the Open Cloud Computing Interface (OCCI), a standardized interface for managing any kind of cloud resources. Besides the OCCI Core model, which defines the basic modeling elements for cloud resources, the OGF also defines extensions that reflect the requirements of different cloud service levels, such as IaaS and PaaS. However, so far the OCCI PaaS extension is very coarse grained and lacks of supporting use cases and implementations. Especially, it does not define how the components of the application itself can be managed. In this paper, we present a model-driven framework that extends the OCCI PaaS extension and is able to use different configuration management tools to manage the whole lifecycle of cloud applications. We demonstrate the feasibility of the approach by presenting four different use cases and prototypical implementations for three different configuration management tools.

MoDMaCAO: Model-Driven Configuration Management of Cloud Applications with OCCI (CLOSER'2018) from St辿phanie Challita

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0 Specifying Semantic Interoperability between Heterogeneous Cloud Resources with the fclouds Formal Language (CLOUD'2018) /slideshow/specifying-semantic-interoperability-between-heterogeneous-cloud-resources-with-the-fclouds-formal-language-cloud2018/104607914 cloud2018-4-180706235951
With the advent of cloud computing, different cloud providers with heterogeneous services and Application Programming Interfaces (APIs) have emerged. Hence, building an interoperable multi-cloud system becomes a complex task. Our idea is to design fclouds framework to achieve semantic interoperability in multi-clouds, i.e., to identify the common concepts between cloud APIs and to reason over them. In this paper, we propose to take advantage of the Open Cloud Computing Interface (OCCI) standard and the Alloy formal specification language to define the fclouds language, which is a formal language for specifying heterogeneous cloud APIs. To do so, we formalize OCCI concepts and operational semantics, then we identify and validate five properties (consistency, sequentiality, reversibility, idempotence and safety) that denote their characteristics. To demonstrate the effectiveness of our cloud formal language, we present thirteen case studies where we formally specify infrastructure, platform, Internet of Things (IoT) and transverse cloud concerns. Thanks to the Alloy analyzer, we verify that these heterogeneous APIs uphold the properties of fclouds and also validate their own specific properties. Then, thanks to formal transformation rules and equivalence properties, we draw a precise alignment between our case studies, which promotes semantic interoperability in a multi-cloud system.]]>
With the advent of cloud computing, different cloud providers with heterogeneous services and Application Programming Interfaces (APIs) have emerged. Hence, building an interoperable multi-cloud system becomes a complex task. Our idea is to design fclouds framework to achieve semantic interoperability in multi-clouds, i.e., to identify the common concepts between cloud APIs and to reason over them. In this paper, we propose to take advantage of the Open Cloud Computing Interface (OCCI) standard and the Alloy formal specification language to define the fclouds language, which is a formal language for specifying heterogeneous cloud APIs. To do so, we formalize OCCI concepts and operational semantics, then we identify and validate five properties (consistency, sequentiality, reversibility, idempotence and safety) that denote their characteristics. To demonstrate the effectiveness of our cloud formal language, we present thirteen case studies where we formally specify infrastructure, platform, Internet of Things (IoT) and transverse cloud concerns. Thanks to the Alloy analyzer, we verify that these heterogeneous APIs uphold the properties of fclouds and also validate their own specific properties. Then, thanks to formal transformation rules and equivalence properties, we draw a precise alignment between our case studies, which promotes semantic interoperability in a multi-cloud system.]]> Fri, 06 Jul 2018 23:59:51 GMT /slideshow/specifying-semantic-interoperability-between-heterogeneous-cloud-resources-with-the-fclouds-formal-language-cloud2018/104607914 StephanieCHALLITA@slideshare.net(StephanieCHALLITA) Specifying Semantic Interoperability between Heterogeneous Cloud Resources with the fclouds Formal Language (CLOUD'2018) StephanieCHALLITA With the advent of cloud computing, different cloud providers with heterogeneous services and Application Programming Interfaces (APIs) have emerged. Hence, building an interoperable multi-cloud system becomes a complex task. Our idea is to design fclouds framework to achieve semantic interoperability in multi-clouds, i.e., to identify the common concepts between cloud APIs and to reason over them. In this paper, we propose to take advantage of the Open Cloud Computing Interface (OCCI) standard and the Alloy formal specification language to define the fclouds language, which is a formal language for specifying heterogeneous cloud APIs. To do so, we formalize OCCI concepts and operational semantics, then we identify and validate five properties (consistency, sequentiality, reversibility, idempotence and safety) that denote their characteristics. To demonstrate the effectiveness of our cloud formal language, we present thirteen case studies where we formally specify infrastructure, platform, Internet of Things (IoT) and transverse cloud concerns. Thanks to the Alloy analyzer, we verify that these heterogeneous APIs uphold the properties of fclouds and also validate their own specific properties. Then, thanks to formal transformation rules and equivalence properties, we draw a precise alignment between our case studies, which promotes semantic interoperability in a multi-cloud system. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/cloud2018-4-180706235951-thumbnail.jpg?width=120&height=120&fit=bounds" /> With the advent of cloud computing, different cloud providers with heterogeneous services and Application Programming Interfaces (APIs) have emerged. Hence, building an interoperable multi-cloud system becomes a complex task. Our idea is to design fclouds framework to achieve semantic interoperability in multi-clouds, i.e., to identify the common concepts between cloud APIs and to reason over them. In this paper, we propose to take advantage of the Open Cloud Computing Interface (OCCI) standard and the Alloy formal specification language to define the fclouds language, which is a formal language for specifying heterogeneous cloud APIs. To do so, we formalize OCCI concepts and operational semantics, then we identify and validate five properties (consistency, sequentiality, reversibility, idempotence and safety) that denote their characteristics. To demonstrate the effectiveness of our cloud formal language, we present thirteen case studies where we formally specify infrastructure, platform, Internet of Things (IoT) and transverse cloud concerns. Thanks to the Alloy analyzer, we verify that these heterogeneous APIs uphold the properties of fclouds and also validate their own specific properties. Then, thanks to formal transformation rules and equivalence properties, we draw a precise alignment between our case studies, which promotes semantic interoperability in a multi-cloud system.

Specifying Semantic Interoperability between Heterogeneous Cloud Resources with the fclouds Formal Language (CLOUD'2018) from St辿phanie Challita

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0 PechaKucha (FormaliSE'2018) /StephanieCHALLITA/pechakucha-formalise2018 pechakucha-formalise2018-180605113623
An overview of my research within a PechaKucha format. 20 slides x 20 seconds each. It’s very challenging but fun at the end! ]]>
An overview of my research within a PechaKucha format. 20 slides x 20 seconds each. It’s very challenging but fun at the end! ]]> Tue, 05 Jun 2018 11:36:23 GMT /StephanieCHALLITA/pechakucha-formalise2018 StephanieCHALLITA@slideshare.net(StephanieCHALLITA) PechaKucha (FormaliSE'2018) StephanieCHALLITA An overview of my research within a PechaKucha format. 20 slides x 20 seconds each. It’s very challenging but fun at the end! <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/pechakucha-formalise2018-180605113623-thumbnail.jpg?width=120&height=120&fit=bounds" /> An overview of my research within a PechaKucha format. 20 slides x 20 seconds each. It’s very challenging but fun at the end!

PechaKucha (FormaliSE'2018) from St辿phanie Challita

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0 A Precise Model for Google Cloud Platform (IC2E'2018) /slideshow/a-precise-model-for-google-cloud-platform-ic2e2018-94021245/94021245 challita-ic2e2018-180416213926
Today, Google Cloud Platform (GCP) is one of the leaders among cloud APIs. Although it was established only five years ago, GCP has gained notable expansion due to its suite of public cloud services that it based on a huge, solid infrastructure. GCP allows developers to use these services by accessing GCP RESTful API that is described through HTML pages on its website. However, the documentation of GCP API is written in natural language (English prose) and therefore shows several drawbacks, such as Informal Heterogeneous Documentation, Imprecise Types, Implicit Attribute Metadata, Hidden Links, Redundancy and Lack of Visual Support. To avoid confusion and misunderstandings, the cloud developers obviously need a precise specification of the knowledge and activities in GCP. Therefore, this paper introduces GCP MODEL, an inferred formal model-driven specification of GCP which describes without ambiguity the resources offered by GCP. GCP MODEL conforms to the Open Cloud Computing Interface (OCCI) metamodel and is implemented based on the open source model-driven Eclipse-based OCCIWARE tool chain. Thanks to our GCP MODEL, we offer corrections to the drawbacks we identified.]]>
Today, Google Cloud Platform (GCP) is one of the leaders among cloud APIs. Although it was established only five years ago, GCP has gained notable expansion due to its suite of public cloud services that it based on a huge, solid infrastructure. GCP allows developers to use these services by accessing GCP RESTful API that is described through HTML pages on its website. However, the documentation of GCP API is written in natural language (English prose) and therefore shows several drawbacks, such as Informal Heterogeneous Documentation, Imprecise Types, Implicit Attribute Metadata, Hidden Links, Redundancy and Lack of Visual Support. To avoid confusion and misunderstandings, the cloud developers obviously need a precise specification of the knowledge and activities in GCP. Therefore, this paper introduces GCP MODEL, an inferred formal model-driven specification of GCP which describes without ambiguity the resources offered by GCP. GCP MODEL conforms to the Open Cloud Computing Interface (OCCI) metamodel and is implemented based on the open source model-driven Eclipse-based OCCIWARE tool chain. Thanks to our GCP MODEL, we offer corrections to the drawbacks we identified.]]> Mon, 16 Apr 2018 21:39:26 GMT /slideshow/a-precise-model-for-google-cloud-platform-ic2e2018-94021245/94021245 StephanieCHALLITA@slideshare.net(StephanieCHALLITA) A Precise Model for Google Cloud Platform (IC2E'2018) StephanieCHALLITA Today, Google Cloud Platform (GCP) is one of the leaders among cloud APIs. Although it was established only five years ago, GCP has gained notable expansion due to its suite of public cloud services that it based on a huge, solid infrastructure. GCP allows developers to use these services by accessing GCP RESTful API that is described through HTML pages on its website. However, the documentation of GCP API is written in natural language (English prose) and therefore shows several drawbacks, such as Informal Heterogeneous Documentation, Imprecise Types, Implicit Attribute Metadata, Hidden Links, Redundancy and Lack of Visual Support. To avoid confusion and misunderstandings, the cloud developers obviously need a precise specification of the knowledge and activities in GCP. Therefore, this paper introduces GCP MODEL, an inferred formal model-driven specification of GCP which describes without ambiguity the resources offered by GCP. GCP MODEL conforms to the Open Cloud Computing Interface (OCCI) metamodel and is implemented based on the open source model-driven Eclipse-based OCCIWARE tool chain. Thanks to our GCP MODEL, we offer corrections to the drawbacks we identified. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/challita-ic2e2018-180416213926-thumbnail.jpg?width=120&height=120&fit=bounds" /> Today, Google Cloud Platform (GCP) is one of the leaders among cloud APIs. Although it was established only five years ago, GCP has gained notable expansion due to its suite of public cloud services that it based on a huge, solid infrastructure. GCP allows developers to use these services by accessing GCP RESTful API that is described through HTML pages on its website. However, the documentation of GCP API is written in natural language (English prose) and therefore shows several drawbacks, such as Informal Heterogeneous Documentation, Imprecise Types, Implicit Attribute Metadata, Hidden Links, Redundancy and Lack of Visual Support. To avoid confusion and misunderstandings, the cloud developers obviously need a precise specification of the knowledge and activities in GCP. Therefore, this paper introduces GCP MODEL, an inferred formal model-driven specification of GCP which describes without ambiguity the resources offered by GCP. GCP MODEL conforms to the Open Cloud Computing Interface (OCCI) metamodel and is implemented based on the open source model-driven Eclipse-based OCCIWARE tool chain. Thanks to our GCP MODEL, we offer corrections to the drawbacks we identified.

A Precise Model for Google Cloud Platform (IC2E'2018) from St辿phanie Challita

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0 Towards Formal-based Semantic Interoperability in Multi-Clouds (CLOUD'2017) /slideshow/towards-formalbased-semantic-interoperability-in-multiclouds/77106887 challitacloud2017-170620125714
Multi-cloud computing has been proposed as a way to reduce vendor lock-in, to improve resiliency during outages and geo-presence, to boost performance and to lower costs. However, semantic differences between cloud providers, as well as their heterogeneous management interfaces, make changing from one provider to another very complex and costly. This is quite challenging for the implementation of multi-cloud systems. In this paper, we aim to take advantage of formal methods to define a precise semantics for multi-clouds. We propose FCLOUDS, a formal-based framework for semantic interoperability in multi-clouds. This framework contains a catalogue of formal models that mathematically describe cloud APIs and reason over them. A precise alignment can be described between their concepts, which promotes semantic interoperability.]]>
Multi-cloud computing has been proposed as a way to reduce vendor lock-in, to improve resiliency during outages and geo-presence, to boost performance and to lower costs. However, semantic differences between cloud providers, as well as their heterogeneous management interfaces, make changing from one provider to another very complex and costly. This is quite challenging for the implementation of multi-cloud systems. In this paper, we aim to take advantage of formal methods to define a precise semantics for multi-clouds. We propose FCLOUDS, a formal-based framework for semantic interoperability in multi-clouds. This framework contains a catalogue of formal models that mathematically describe cloud APIs and reason over them. A precise alignment can be described between their concepts, which promotes semantic interoperability.]]> Tue, 20 Jun 2017 12:57:13 GMT /slideshow/towards-formalbased-semantic-interoperability-in-multiclouds/77106887 StephanieCHALLITA@slideshare.net(StephanieCHALLITA) Towards Formal-based Semantic Interoperability in Multi-Clouds (CLOUD'2017) StephanieCHALLITA Multi-cloud computing has been proposed as a way to reduce vendor lock-in, to improve resiliency during outages and geo-presence, to boost performance and to lower costs. However, semantic differences between cloud providers, as well as their heterogeneous management interfaces, make changing from one provider to another very complex and costly. This is quite challenging for the implementation of multi-cloud systems. In this paper, we aim to take advantage of formal methods to define a precise semantics for multi-clouds. We propose FCLOUDS, a formal-based framework for semantic interoperability in multi-clouds. This framework contains a catalogue of formal models that mathematically describe cloud APIs and reason over them. A precise alignment can be described between their concepts, which promotes semantic interoperability. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/challitacloud2017-170620125714-thumbnail.jpg?width=120&height=120&fit=bounds" /> Multi-cloud computing has been proposed as a way to reduce vendor lock-in, to improve resiliency during outages and geo-presence, to boost performance and to lower costs. However, semantic differences between cloud providers, as well as their heterogeneous management interfaces, make changing from one provider to another very complex and costly. This is quite challenging for the implementation of multi-cloud systems. In this paper, we aim to take advantage of formal methods to define a precise semantics for multi-clouds. We propose FCLOUDS, a formal-based framework for semantic interoperability in multi-clouds. This framework contains a catalogue of formal models that mathematically describe cloud APIs and reason over them. A precise alignment can be described between their concepts, which promotes semantic interoperability.

Towards Formal-based Semantic Interoperability in Multi-Clouds (CLOUD'2017) from St辿phanie Challita

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0 A Study of Virtual Machine Placement Optimization in Data Centers (CLOSER'2017) /slideshow/a-study-of-virtual-machine-placement-optimization-in-data-centers-closer2017/75352310 challitacloser2017-170424142744
In recent years, cloud computing has shown a valuable way for accommodating and providing services over the Internet such that data centers rely increasingly on this platform to host a large amount of applications (web hosting, e-commerce, social networking, etc.). Thus, the utilization of servers in most data centers can be improved by adding virtualization and selecting the most suitable host for each Virtual Machine (VM). The problem of VM placement is an optimization problem aiming for multiple goals. It can be covered through various approaches. Each approach aims to simultaneously reduce power consumption, maximize resource utilization and avoid traffic congestion. The main goal of this literature survey is to provide a better understanding of existing approaches and algorithms that ensure better VM placement in the context of cloud computing and to identify future directions.]]>
In recent years, cloud computing has shown a valuable way for accommodating and providing services over the Internet such that data centers rely increasingly on this platform to host a large amount of applications (web hosting, e-commerce, social networking, etc.). Thus, the utilization of servers in most data centers can be improved by adding virtualization and selecting the most suitable host for each Virtual Machine (VM). The problem of VM placement is an optimization problem aiming for multiple goals. It can be covered through various approaches. Each approach aims to simultaneously reduce power consumption, maximize resource utilization and avoid traffic congestion. The main goal of this literature survey is to provide a better understanding of existing approaches and algorithms that ensure better VM placement in the context of cloud computing and to identify future directions.]]> Mon, 24 Apr 2017 14:27:44 GMT /slideshow/a-study-of-virtual-machine-placement-optimization-in-data-centers-closer2017/75352310 StephanieCHALLITA@slideshare.net(StephanieCHALLITA) A Study of Virtual Machine Placement Optimization in Data Centers (CLOSER'2017) StephanieCHALLITA In recent years, cloud computing has shown a valuable way for accommodating and providing services over the Internet such that data centers rely increasingly on this platform to host a large amount of applications (web hosting, e-commerce, social networking, etc.). Thus, the utilization of servers in most data centers can be improved by adding virtualization and selecting the most suitable host for each Virtual Machine (VM). The problem of VM placement is an optimization problem aiming for multiple goals. It can be covered through various approaches. Each approach aims to simultaneously reduce power consumption, maximize resource utilization and avoid traffic congestion. The main goal of this literature survey is to provide a better understanding of existing approaches and algorithms that ensure better VM placement in the context of cloud computing and to identify future directions. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/challitacloser2017-170424142744-thumbnail.jpg?width=120&height=120&fit=bounds" /> In recent years, cloud computing has shown a valuable way for accommodating and providing services over the Internet such that data centers rely increasingly on this platform to host a large amount of applications (web hosting, e-commerce, social networking, etc.). Thus, the utilization of servers in most data centers can be improved by adding virtualization and selecting the most suitable host for each Virtual Machine (VM). The problem of VM placement is an optimization problem aiming for multiple goals. It can be covered through various approaches. Each approach aims to simultaneously reduce power consumption, maximize resource utilization and avoid traffic congestion. The main goal of this literature survey is to provide a better understanding of existing approaches and algorithms that ensure better VM placement in the context of cloud computing and to identify future directions.

A Study of Virtual Machine Placement Optimization in Data Centers (CLOSER'2017) from St辿phanie Challita

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