Ce diaporama a bien été signalé.
Nous utilisons votre profil LinkedIn et vos données d’activité pour vous proposer des publicités personnalisées et pertinentes. Vous pouvez changer vos préférences de publicités à tout moment.

Stéphanie Challita's PhD Defense Presentation

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.

  • Identifiez-vous pour voir les commentaires

Stéphanie Challita's PhD Defense Presentation

  1. 1. Inferring Models from Cloud APIs and Reasoning over Them: a Tooled and Formal Approach Presented by: Stéphanie CHALLITA PhD committee: Philippe MERLE Inria Lille – Nord Europe Supervisor Benoit COMBEMALE University of Toulouse & Inria Rennes Reviewer Christian PEREZ Inria Lyon Reviewer Hélène COULLON IMT Atlantique Examiner Laetitia JOURDAN University of Lille Examiner Faiez ZALILA Inria Lille – Nord Europe Invited PhD Defense December 21, 2018
  2. 2. Stéphanie CHALLITA – PhD Defense 2/109 Cloud computing Created by Sam Johnston, downloaded from https://en.wikipedia.org/wiki/Cloud_computing
  3. 3. Stéphanie CHALLITA – PhD Defense 3/109 Multi-Cloud computing
  4. 4. 4/109Stéphanie CHALLITA – PhD Defense Problem statement Deployment Model Management Interface Service Model Public SOAP & REST IaaS, PaaS, SaaS Public REST IaaS, PaaS, SaaS Public REST IaaS Private REST IaaS
  5. 5. 5/109Stéphanie CHALLITA – PhD Defense Problem statement Developer Natural language is ambiguous!
  6. 6. Stéphanie CHALLITA – PhD Defense 6/109 Problem statement
  7. 7. 7/109 To semantically reason over extracted models of cloud APIs Stéphanie CHALLITA – PhD Defense Thesis objective
  8. 8. 1. State of the Art 2. Foundation  OCCIware 3. Contributions  Inferring models from cloud APIs: GCP model  Reasoning on cloud APIs: fclouds framework 4. Conclusion  Summary  Perspectives Stéphanie CHALLITA – PhD Defense 8/109 Outline
  9. 9. Stéphanie CHALLITA – PhD Defense 9/109 Approaches for multi-clouds - Actors State of the Art Foundation Contributions Conclusion Cloud provider Cloud developer Cloud architect Use Use Offer
  10. 10. 10/109Stéphanie CHALLITA – PhD Defense Programming Space Provider Space Cloud provider Cloud developer Cloud architect Modeling Space Semantic Space State of the Art Foundation Contributions Conclusion Approaches for multi-clouds
  11. 11. Stéphanie CHALLITA – PhD Defense 11/109 Approaches for multi-clouds State of the Art Foundation Contributions Conclusion AWS API OCCI API DigitalOcean API Provider Space Private Cloud provider Public GCP API … OCCI CIMI … Public Public
  12. 12. Stéphanie CHALLITA – PhD Defense 12/109 Approaches for multi-clouds State of the Art Foundation Contributions Conclusion AWS API OCCI API DigitalOcean API Provider Space Public Private Cloud provider Public GCP API … Public Cloud Brokers OCCI CIMI …
  13. 13. Stéphanie CHALLITA – PhD Defense 13/109 Approaches for multi-clouds State of the Art Foundation Contributions Conclusion AWS API OCCI API DigitalOcean API DigitalOcean SDK Programming Space GCP SDK AWS SDK Provider Space Public Private Cloud provider Public GCP API Cloud developerOCCI SDK … … Public Cloud Brokers OCCI CIMI …
  14. 14. Stéphanie CHALLITA – PhD Defense 14/109 Approaches for multi-clouds State of the Art Foundation Contributions Conclusion AWS API OCCI API DigitalOcean API DigitalOcean SDK Multi-cloud Libraries Programming Space GCP SDK AWS SDK Provider Space Public Private Cloud provider Public GCP API Cloud developerOCCI SDK … … Public Cloud Brokers OCCI CIMI …
  15. 15. Approaches for multi-clouds State of the Art Foundation Contributions Conclusion Cloud Metamodel Cloud Model conforms to represented by defines Cloud Meta-metamodel conforms to M0 M1 M2 M3 15/109 Cloud architect Model Code generation Static analysis Documentation Transformation Stéphanie CHALLITA – PhD Defense
  16. 16. Stéphanie CHALLITA – PhD Defense 16/109 Approaches for multi-clouds State of the Art Foundation Contributions Conclusion Modeling Space Cloud architect CloudML AWS API OCCI API DigitalOcean API DigitalOcean SDK Multi-cloud Libraries Programming Space GCP SDK AWS SDK Provider Space Public Private Cloud provider Public GCP API Cloud developerOCCI SDK … … Public Cloud Brokers CAMEL TOSCAOpenTOSCASALOON StratusML OCCI CIMI …
  17. 17. Stéphanie CHALLITA – PhD Defense 17/109 Approaches for multi-clouds State of the Art Foundation Contributions Conclusion Issue 1: Fixed metamodels, not extensible to support additional concepts
  18. 18. RQ#1: Is it possible to have a solution that allows to represent all kinds of cloud resources despite their heterogeneity, and a complete framework for managing them? - How to design the cloud developer needs at a high-level of abstraction? - How to verify the cloud structural and behavioral properties before any concrete deployments? - How to deploy and manage cloud configurations? Stéphanie CHALLITA – PhD Defense 18/109 Research questions State of the Art Foundation Contributions Conclusion OCCIware Research topics: Model-Driven Engineering (MDE), Models@run.time
  19. 19. Stéphanie CHALLITA – PhD Defense 19/109 Approaches for multi-clouds State of the Art Foundation Contributions Conclusion Issue 2: Fuzziness of the concepts of the cloud modeling languages
  20. 20. Stéphanie CHALLITA – PhD Defense 20/109 RQ#2: Is it possible to automatically extract precise models from cloud APIs and to synchronize them with the cloud evolution? - How to provide an accurate description for a cloud API? - How to correct the existing drawbacks in a cloud API documentation? - How to analyze a cloud API documentation? State of the Art Foundation Contributions Conclusion GCP model Research topics: API mining, reverse-engineering, NLP Research questions
  21. 21. Stéphanie CHALLITA – PhD Defense 21/109 Approaches for multi-clouds State of the Art Foundation Contributions Conclusion Issue 3: Little attention paid to the semantics
  22. 22. Stéphanie CHALLITA – PhD Defense 22/109 RQ#3: Is it possible to reason on cloud APIs and identify their similarities and differences? - How to better understand cloud solutions? - How to make sure that a cloud solution reflects the desired behaviour? - How to ensure an accurate migration from a cloud solution to another? State of the Art Foundation Contributions Conclusion fclouds Research topics: model verification & validation, semantic alignment Research questions
  23. 23. Stéphanie CHALLITA – PhD Defense 23/109 Thesis vision Model-Driven Approach for the Cloud Formal Approach for the Cloud OCCIware fclouds Infer Reason OCCIGCPAWS State of the Art Foundation Contributions Conclusion
  24. 24. Stéphanie CHALLITA – PhD Defense 24/109 Thesis vision Model-Driven Approach for the Cloud OCCIware State of the Art Foundation Contributions Conclusion
  25. 25.  Community-based effort hosted by  Resource-oriented model and RESTful API  Everything as a Service, i.e., XaaS OCCI Stéphanie CHALLITA – PhD Defense 25/109 State of the Art Foundation Contributions Conclusion
  26. 26. OCCI core model Stéphanie CHALLITA – PhD Defense 26/109 State of the Art Foundation Contributions Conclusion Category scheme: URI term: String title: String [0..1] Kind Mixin Action Entity id: URI Resource Link Attribute name: String type: String [0..1] mutable: Boolean [0..1] required: Boolean [0..1] default: String [0..1] description: String [0..1] 0..1 * actions 1* actions * mixins * entities 1 kind * entities 1 target 1 source * links 0..1 parent * * depends * 1 * attributes * applies Source: R.Nyrén, A.Edmonds, A.Papaspyrou, T.Metsch and B.Parák, “Open Cloud Computing Interface-Core,” Open Grid Forum, In Specification Document GFD.221, Feb. 2016.
  27. 27. Stéphanie CHALLITA – PhD Defense 27/109 OCCIware metamodel State of the Art Foundation Contributions Conclusion
  28. 28. Stéphanie CHALLITA – PhD Defense 28/109 OCCIware tool chain State of the Art Foundation Contributions Conclusion
  29. 29. Stéphanie CHALLITA – PhD Defense 29/109 OCCIware tool chain State of the Art Foundation Contributions Conclusion
  30. 30.  OCCIware is a factory to build cloud domain-specific modeling frameworks Stéphanie CHALLITA – PhD Defense 30/109 State of the Art Foundation Contributions Conclusion OCCIware use cases
  31. 31.  OCCIware is a factory to build cloud domain-specific modeling frameworks Stéphanie CHALLITA – PhD Defense 31/109 State of the Art Foundation Contributions Conclusion OCCIware use cases
  32. 32.  OCCIware is a factory to build cloud domain-specific modeling frameworks Stéphanie CHALLITA – PhD Defense 32/109 State of the Art Foundation Contributions Conclusion OCCIware use cases
  33. 33.  OCCIware is a factory to build cloud domain-specific modeling frameworks Stéphanie CHALLITA – PhD Defense 33/109 State of the Art Foundation Contributions Conclusion OCCIware use cases
  34. 34.  OCCIware is a factory to build cloud domain-specific modeling frameworks Stéphanie CHALLITA – PhD Defense 34/109 State of the Art Foundation Contributions Conclusion OCCIware use cases
  35. 35. Stéphanie CHALLITA – PhD Defense 35/109 Thesis vision Model-Driven Approach for the Cloud OCCIware State of the Art Foundation Contributions Conclusion
  36. 36. Stéphanie CHALLITA – PhD Defense 36/109 Thesis vision State of the Art Foundation Contributions Conclusion Model-Driven Approach for the Cloud OCCIware Infer OCCIGCPAWS
  37. 37. conformsto Cloud documentation 37/109Stéphanie CHALLITA – PhD Defense Cloud API documentation Cloud developer/architect Cloud provider An agreement with the developer on exactly how the system will operate Cloud documentations are written in natural language  human errors and/or semantic confusions State of the Art Foundation Contributions Conclusion
  38. 38. Stéphanie CHALLITA – PhD Defense 38/109 Global Vision State of the Art Foundation Contributions Conclusion  Inferring models from cloud APIs  Work of API mining, reverse-engineering HTML Model  Model refinement (NLP techniques, graphical output…)
  39. 39. 39/109Stéphanie CHALLITA – PhD Defense Google Cloud Platform (GCP) use case State of the Art Foundation Contributions Conclusion Is partner withIs adopted by
  40. 40.  Informal heterogeneous documentation  Imprecise types  Implicit attribute metadata  Hidden links  Redundancy  Lack of visual support Stéphanie CHALLITA – PhD Defense 40/109 List of GCP documentation drawbacks State of the Art Foundation Contributions Conclusion
  41. 41. Stéphanie CHALLITA – PhD Defense 41/109 Informal heterogeneous documentation State of the Art Foundation Contributions Conclusion Available at https://cloud.google.com/compute/docs/reference/latest/networks Available at https://cloud.google.com/dataproc/docs/reference/rest/v1/projects.regions.clusters
  42. 42. 42/109Stéphanie CHALLITA – PhD Defense Imprecise types State of the Art Foundation Contributions Conclusion
  43. 43. Stéphanie CHALLITA – PhD Defense 43/109 GCP snapshot  GCP engineers could update/correct GCP documentation  Continuously following up with GCP documentation is costly  Snapshot of GCP API State of the Art Foundation Contributions Conclusion A Snapshot GCP HTML pages GCP documentation
  44. 44. 44/109Stéphanie CHALLITA – PhD Defense GCP crawler & GCP model State of the Art Foundation Contributions Conclusion GCP Crawler A B Snapshot GCP HTML pages GCP documentation  GCP Crawler to extract all GCP resources, their attributes and actions  GCP Model for a better description of the GCP resources GCP Model C
  45. 45. 45/109Stéphanie CHALLITA – PhD Defense State of the Art Foundation Contributions Conclusion GCP crawler & GCP model
  46. 46. 46/109Stéphanie CHALLITA – PhD Defense State of the Art Foundation Contributions Conclusion GCP crawler & GCP model
  47. 47. 47/109Stéphanie CHALLITA – PhD Defense State of the Art Foundation Contributions Conclusion GCP crawler & GCP model
  48. 48. 48/109Stéphanie CHALLITA – PhD Defense State of the Art Foundation Contributions Conclusion GCP crawler & GCP model
  49. 49. 49/109Stéphanie CHALLITA – PhD Defense State of the Art Foundation Contributions Conclusion GCP crawler & GCP model
  50. 50. 50/109Stéphanie CHALLITA – PhD Defense State of the Art Foundation Contributions Conclusion GCP crawler & GCP model
  51. 51. 51/109Stéphanie CHALLITA – PhD Defense State of the Art Foundation Contributions Conclusion GCP crawler & GCP model
  52. 52. 52/109Stéphanie CHALLITA – PhD Defense State of the Art Foundation Contributions Conclusion GCP crawler & GCP model
  53. 53. 53/109Stéphanie CHALLITA – PhD Defense State of the Art Foundation Contributions Conclusion GCP crawler & GCP model
  54. 54. 54/109Stéphanie CHALLITA – PhD Defense State of the Art Foundation Contributions Conclusion GCP crawler & GCP model
  55. 55. 55/109Stéphanie CHALLITA – PhD Defense State of the Art Foundation Contributions Conclusion GCP crawler & GCP model
  56. 56. 56/109Stéphanie CHALLITA – PhD Defense State of the Art Foundation Contributions Conclusion GCP Crawler GCP Model A B Snapshot GCP HTML pages GCP documentation C  GCP Crawler to extract all GCP resources, their attributes and actions  GCP Model for a better description of the GCP resources and for reasoning over them No more Informal Heterogeneous Documentation GCP crawler & GCP model OCCIware Metamodel GCP configuration conforms to represented by Ecore Metamodel conforms to M0 M1 M2 M3 GCP model GCP doc conforms to
  57. 57. 57/109Stéphanie CHALLITA – PhD Defense Model transformations State of the Art Foundation Contributions Conclusion Implicit Attribute Metadata Detection Link Identification Redundancy Removal Model Transformations Type Refinement Model Visualization GCP Crawler A B Snapshot GCP HTML pages GCP documentation GCP Model C
  58. 58. 58/109  By adopting the data type system proposed by OCCIware metamodel  defining regular expressions  using the EMF validator to check the type constraints that are attached to the attributes Stéphanie CHALLITA – PhD Defense Type refinement State of the Art Foundation Contributions Conclusion
  59. 59. 59/109  By adopting the data type system proposed by OCCIware metamodel  defining regular expressions  using the EMF validator to check the type constraints that are attached to the attributes Stéphanie CHALLITA – PhD Defense Type refinement State of the Art Foundation Contributions Conclusion  If the type of an attribute in the documentation is string and the description explains that this is an email address, we apply the email validation constraint:  STRINGTYPE + this regular expression: ^[A-Z0-9._%+-]+@[A-Z0-9.-]+.[A-Z]{2,6}$ No more Imprecise Types
  60. 60. 60/109  To explicitly store information into additional attributes defined in the ATTRIBUTE concept of our GCP MODEL  We use Natural Language Processing (NLP) techniques Word Tagging/Part-of- Speech (PoS)  We declare pre-defined tags for some GCP specific attribute properties:  mu tab le = tru e if [In p u t -O n ly ]  mu tab le = false if [O u tp u t - on ly ]/ read on ly  required = true if [Required]  req u ired = false if [O ption al ]  d efau lt = X if Th e d efau lt valu e is X Stéphanie CHALLITA – PhD Defense Implicit attribute metadata detection State of the Art Foundation Contributions Conclusion
  61. 61. 61/109Stéphanie CHALLITA – PhD Defense Implicit attribute metadata detection State of the Art Foundation Contributions Conclusion
  62. 62. 62/109Stéphanie CHALLITA – PhD Defense Implicit attribute metadata detection State of the Art Foundation Contributions Conclusion
  63. 63. 63/109Stéphanie CHALLITA – PhD Defense Implicit attribute metadata detection State of the Art Foundation Contributions Conclusion
  64. 64. 64/109Stéphanie CHALLITA – PhD Defense Implicit attribute metadata detection State of the Art Foundation Contributions Conclusion
  65. 65. 65/109Stéphanie CHALLITA – PhD Defense Model visualization State of the Art Foundation Contributions Conclusion No more Lack of Visual Support Built via OCCIware designer
  66. 66. 66/109  Documentation spread over many pages – Recursive parsing one must deeply explore the documentation to completely define all the required concepts Stéphanie CHALLITA – PhD Defense Challenges State of the Art Foundation Contributions Conclusion
  67. 67. 67/109  Documentation spread over many pages – Recursive parsing one must deeply explore the documentation to completely define all the required concepts  Huge and tough documentation analysis – Finely observe the structure of the page to correctly design the crawler – Read the descriptions to carefully design the rules Stéphanie CHALLITA – PhD Defense Challenges State of the Art Foundation Contributions Conclusion
  68. 68. 68/109  Documentation spread over many pages – Recursive parsing one must deeply explore the documentation to completely define all the required concepts  Huge and tough documentation analysis – Finely observe the structure of the page to correctly design the crawler – Read the descriptions to carefully design the rules  Huge and tough model analysis – Verify the automatically set values to incrementally refine the knowledge extraction Stéphanie CHALLITA – PhD Defense Challenges State of the Art Foundation Contributions Conclusion
  69. 69. 69/109Stéphanie CHALLITA – PhD Defense State of the Art Foundation Contributions Conclusion Analysis  How many resources are provided by GCP documentation?  Why GCP documentation is heterogeneous?  To what extend GCP resources have redundant characteristics?  How GCP model allows us to factorize them?
  70. 70. 70/109Stéphanie CHALLITA – PhD Defense State of the Art Foundation Contributions Conclusion How many resources are provided by GCP documentation?
  71. 71. Why GCP documentation is heterogeneous? Runtime Config 71/109Stéphanie CHALLITA – PhD Defense Cloud User Account & GCP Model State of the Art Foundation Contributions Conclusion
  72. 72. 72/109Stéphanie CHALLITA – PhD Defense State of the Art Foundation Contributions Conclusion To what extend GCP resources have redundant characteristics?
  73. 73. 73/109Stéphanie CHALLITA – PhD Defense State of the Art Foundation Contributions Conclusion To what extend GCP resources have redundant characteristics?
  74. 74. 74/109Stéphanie CHALLITA – PhD Defense How GCP model allows us to factorize them? State of the Art Foundation Contributions Conclusion
  75. 75. Stéphanie CHALLITA – PhD Defense 75/109 Thesis vision State of the Art Foundation Contributions Conclusion Model-Driven Approach for the Cloud OCCIware Infer OCCIGCPAWS
  76. 76. Stéphanie CHALLITA – PhD Defense 76/109 Thesis vision State of the Art Foundation Contributions Conclusion Model-Driven Approach for the Cloud Formal Approach for the Cloud OCCIware fclouds Infer Reason OCCIGCPAWS
  77. 77. Need to reason on the common principles that cloud solutions must adhere to Stéphanie CHALLITA – PhD Defense 77/109 Exploring the semantic space State of the Art Foundation Contributions Conclusion Multi-cloud Libraries Modeling Space Programming Space Provider Space Cloud Brokers Cloud provider Cloud developer Cloud architect Model-Driven Approaches for the Cloud (MDAC) Semantic Space Formal Approaches for the Cloud
  78. 78. Need to reason on the common principles that cloud solutions must adhere to Stéphanie CHALLITA – PhD Defense 78/109 Exploring the semantic space State of the Art Foundation Contributions Conclusion [1] K. Yongsiriwit, M. Sellami, and W. Gaaloul, “A Semantic Framework Supporting Cloud Resource Descriptions Interoperability,” in 2016 IEEE 9th International Conference on Cloud Computing (CLOUD). IEEE, 2016, pp. 585–592. [2] N. Loutas, E. Kamateri, and K. Tarabanis, “A Semantic Interoperability Framework for Cloud Platform as a Service,” in 2011 IEEE Third International Conference on Cloud Computing Technology and Science (CloudCom). IEEE, 2011, pp. 280–287. Multi-cloud Libraries Modeling Space Programming Space Provider Space Cloud Brokers Cloud provider Cloud developer Cloud architect Model-Driven Approaches for the Cloud (MDAC) Semantic Space fcloudsPSIF [2][1]
  79. 79. 79/109Stéphanie CHALLITA – PhD Defense Need for the semantic space Mathematical specification Formal Validation Reasoning One interpretation Accuracy Earlier error detection, cheaper correction State of the Art Foundation Contributions Conclusion
  80. 80. 80/109Stéphanie CHALLITA – PhD Defense fclouds framework State of the Art Foundation Contributions Conclusion fclouds is a framework for providing formal specifications of cloud APIs & reasoning over them  Catalog of cloud formal models  Based on a formal language
  81. 81. Formalization of OCCI core concepts & CRUD operations in Alloy 81/109Stéphanie CHALLITA – PhD Defense fclouds formal language State of the Art Foundation Contributions Conclusion
  82. 82. 82/109  Formalization of OCCI core concepts in Alloy Stéphanie CHALLITA – PhD Defense fclouds static semantics Extension Configuration Resource Link KindAction DataType Attribute Time source target links use kinds types resources action State of the Art Foundation Contributions Conclusion
  83. 83. 83/109  Formalization of OCCI core concepts in Alloy  Concepts are modeled as signatures Stéphanie CHALLITA – PhD Defense Extension Configuration Resource Link KindAction DataType Attribute Time source target links use kinds types resources action State of the Art Foundation Contributions Conclusion fclouds static semantics sig Configuration { use : set Extension , resources : set Resource -> Time }
  84. 84. 84/109  Formalization of OCCI core concepts in Alloy  Concepts are modeled as signatures  Time concept is added to distinguish between mutable and immutable fields Stéphanie CHALLITA – PhD Defense Extension Configuration Resource Link KindAction DataType Attribute Time source target links use kinds types resources action sig Configuration { use : set Extension , resources : set Resource -> Time } State of the Art Foundation Contributions Conclusion fclouds static semantics
  85. 85.  Formalization of OCCI behavioral specification1 in Alloy  Operations are modeled as predicates 85/109Stéphanie CHALLITA – PhD Defense State of the Art Foundation Contributions Conclusion OCCIAPI Create Retrieve Update Delete 1. R. Nyrén, A. Edmonds, T. Metsch and B. Parák, “Open Cloud Computing Interface - HTTP Protocol,” Open Grid Forum, In Specification Document GFD.223, Feb. 2016. fclouds operational semantics
  86. 86.  Formalization of OCCI behavioral specification1 in Alloy  Operations are modeled as predicates  Time concept is added to distinguish between pre- states and post-states 86/109Stéphanie CHALLITA – PhD Defense State of the Art Foundation Contributions Conclusion OCCIAPI Create Retrieve Update Delete 1. R. Nyrén, A. Edmonds, T. Metsch and B. Paràk, “Open Cloud Computing Interface - HTTP Protocol,” Open Grid Forum, In Specification Document GFD.223, Feb. 2016. fclouds operational semantics
  87. 87. 87/109Stéphanie CHALLITA – PhD Defense pred CreateResource [ config : Configuration, resourceId : String, kind : Kind , t, t ’ : Time ] { / / preconditions at instant t no resource : config.resources.t | resource.id = resourceId kind in config.use.kinds } State of the Art Foundation Contributions Conclusion fclouds operational semantics
  88. 88. 88/109Stéphanie CHALLITA – PhD Defense pred CreateResource [ config : Configuration, resourceId : String, kind : Kind , t, t ’ : Time ] { / / preconditions at instant t no resource : config.resources.t | resource.id = resourceId kind in config.use.kinds / / postconditions at instant t ’ one resource : Resource { resource.id = resourceId resource . kind = kind config.resources. t ’ = config.resources.t + resource } } State of the Art Foundation Contributions Conclusion fclouds operational semantics
  89. 89. 89/109Stéphanie CHALLITA – PhD Defense fclouds properties  Consistency  Sequentiality  Reversibility  Conformance to HTTP 2 protocol  Idempotence  Safety State of the Art Foundation Contributions Conclusion
  90. 90. 90/109Stéphanie CHALLITA – PhD Defense fclouds properties - sequentiality Definition: “Two cloud API operations are sequential when one cannot happen if the other one did not happen at the time before ” State of the Art Foundation Contributions Conclusion
  91. 91. 91/109Stéphanie CHALLITA – PhD Defense fclouds properties - sequentiality Definition: “Two cloud API operations are sequential when one cannot happen if the other one did not happen at the time before ” No counterexample t1 t2 Update-VM t0 Cores: 2 Memory: 2 GB Disk: 256 GB Cores: 4 Memory: 2 GB Disk: 256 GB assert RetrieveResourceThenUpdateResource { ... } State of the Art Foundation Contributions Conclusion
  92. 92. 92/109Stéphanie CHALLITA – PhD Defense fclouds properties - sequentiality Pairs of sequential OCCI operations State of the Art Foundation Contributions Conclusion Create Retrieve Update Delete Create Retrieve Update Delete
  93. 93. 93/109Stéphanie CHALLITA – PhD Defense Catalog of cloud formal specifications State of the Art Foundation Contributions Conclusion IaaS PaaS IoT Transverse cloud concerns MoDMaCAO [CLOSER 2018] OMCRI [IoT 2018] CoT [CoopIS 2017] Platform [OGF 2016] Infrastructure [OGF 2016] CRTP [OGF 2016] SLA [OGF 2016] Monitoring [OGF 2016] Cloud Simulation [EDGE 2017] Cloud Elasticity [CLOUD 2017] [OCCIware Deliverable 2.4.1] [IC2E 2018] [CLOUD 2016]
  94. 94.  Template-based approach  Alloy concept = templates for each OCCI concept conforms to 94/109Stéphanie CHALLITA – PhD Defense Alloy generator State of the Art Foundation Contributions Conclusion OCCI extension Alloy specification fclouds specification
  95. 95. 95/109Stéphanie CHALLITA – PhD Defense Alloy generator State of the Art Foundation Contributions Conclusion
  96. 96. 96/109Stéphanie CHALLITA – PhD Defense Alloy generator State of the Art Foundation Contributions Conclusion
  97. 97. 97/109  Verification of fclouds properties via Alloy Analyzer Stéphanie CHALLITA – PhD Defense Verification of properties State of the Art Foundation Contributions Conclusion
  98. 98. 98/109  Verification of fclouds properties via Alloy Analyzer  Definition & validation of domain-specific properties Example  In OCCI Infrastructure: Stéphanie CHALLITA – PhD Defense Compute NetworkNetworkInterface assert NetworkInterfaceBetweenComputeAndNetwork { ... } State of the Art Foundation Contributions Conclusion Verification of properties
  99. 99. pred ComputeMapInstance [ c : one Compute, i : one Instance ] { i.name = c.occicomputehostname i.machinetype.isSharedCpu = c.occicomputeshare i.machinetype.memoryMb = mul [ 1024, c.occicomputememory ] … } 99/109  Ensure semantic alignment Example  An instance at GCP is a compute at OCCI Stéphanie CHALLITA – PhD Defense Formal transformation rules Compute GCP configuration OCCI configuration resources resources String Integer Boolean Instance State of the Art Foundation Contributions Conclusion
  100. 100. Stéphanie CHALLITA – PhD Defense 100/109 Thesis vision Model-Driven Approach for the Cloud Formal Approach for the Cloud OCCIware fclouds Infer Reason OCCIGCPAWS State of the Art Foundation Contributions Conclusion
  101. 101. Stéphanie CHALLITA – PhD Defense 101/109 Global scenario State of the Art Foundation Contributions Conclusion GCP Model GCP.als Infer Reason transformation Model-driven engineering Integrated formal methods
  102. 102.  RQ#1: Is it possible to have a solution that allows to represent all kinds of cloud resources despite their heterogeneity, and a complete framework for managing them? A model-driven framework to deal with all kinds of cloud resources with OCCIware  RQ#2: Is it possible to automatically extract precise models from cloud APIs and to synchronize them with the cloud evolution? Inferring precise cloud models from online documentations and the GCP use case  RQ#3: Is it possible to reason on cloud APIs and identify their similarities and differences? Reasoning on cloud models with fclouds Stéphanie CHALLITA – PhD Defense 102/109 Summary State of the Art Foundation Contributions Conclusion
  103. 103.  Following the evolution of GCP API Stéphanie CHALLITA – PhD Defense 103/109 Perspectives State of the Art Foundation Contributions Conclusion
  104. 104.  Following the evolution of GCP API  Developing more semantic alignment between cloud APIs Stéphanie CHALLITA – PhD Defense 104/109 Perspectives State of the Art Foundation Contributions Conclusion
  105. 105.  Following the evolution of GCP API  Developing more semantic alignment between cloud APIs  Improving the management of cloud applications with OCCI and TOSCA Stéphanie CHALLITA – PhD Defense 105/109 Perspectives State of the Art Foundation Contributions Conclusion
  106. 106.  Extending the properties of formal cloud APIs Stéphanie CHALLITA – PhD Defense 106/109 Perspectives State of the Art Foundation Contributions Conclusion
  107. 107.  Extending the properties of formal cloud APIs  Exploring the use of formal models to address the challenges of other distributed systems (IoT, edge computing, etc.) Stéphanie CHALLITA – PhD Defense 107/109 Perspectives State of the Art Foundation Contributions Conclusion
  108. 108. Stéphanie CHALLITA stephanie.challita@inria.fr researchers.lille.inria.fr/schallit Stéphanie CHALLITA – PhD Defense 108/109 Thank you! Questions?
  109. 109. Stéphanie CHALLITA – PhD Defense 109/109 International journal Faiez Zalila, Stéphanie Challita, Philippe Merle. “Model-Driven Cloud Resource Management with OCCIware.” Future Generation Computer Systems (FGCS), 2018. (under review) International conferences Stéphanie Challita, Faiez Zalila, Philippe Merle. “Specifying Semantic Interoperability between Heterogeneous Cloud Resources with the fclouds Formal Language”. IEEE International Conference on Cloud Computing (CLOUD). 2018. Stéphanie Challita, Faiez Zalila, Christophe Gourdin, Philippe Merle. “A Precise Model for Google Cloud Platform" . IEEE International Conference on Cloud Engineering (IC2E). 2018. Fabian Korte, Stéphanie Challita, Faiez Zalila, Philippe Merle, Jens Grabowski. “Model-Driven Configuration Management of Cloud Applications with OCCI”. International Conference on Cloud Computing and Services Science (CLOSER). 2018. Faiez Zalila, Stéphanie Challita, Philippe Merle. “A Model-Driven Tool Chain for OCCI”. International Conference on Cooperative Information Systems (CoopIS). 2017. Stéphanie Challita, Fawaz Paraiso, Philippe Merle. “Towards Formal-based Semantic Interoperability in Multi-Clouds: The fclouds Framework’’. IEEE International Conference on Cloud Computing (CLOUD). 2017. Stéphanie Challita, Fawaz Paraiso, Philippe Merle. “A Study of Virtual Machine Placement Optimization in Data Centers”. International Conference on Cloud Computing and Services Science (CLOSER). 2017. Fawaz Paraiso, Stéphanie Challita, Yahya Al-dhuraibi, Philippe Merle. “Model-Driven Management of Docker Containers”. IEEE International Conference on Cloud Computing (CLOUD). 2016. Award Awardee of L’Oréal – UNESCO For Women In Science program

×