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IBM Student Workshop for Frontiers of Cloud Computing 2011

IBM Student Workshop for Frontiers of Cloud Computing 2011.

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IBM Student Workshop for Frontiers of Cloud Computing 2011

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  1. IBM Student Workshop for Frontiers of Cloud Computing 2011 Cloud Transformation AdvisorA Pattern-based Approach to Cloud TransformationYi-Min Chee, Nianjun Zhou, Fan Jing Meng, SaeedBagheri, PeideZhong, Jian Wang, Chang Hua Sun, Dong XuDuan, SugandhMehta, Tao Liu, Shao Liang JiaDecember 2, 2011

  2. Motivation As more and more applications are moved to the Cloud, there will be an increased desire to also make transformations (i.e. application changes) • Why Transform? • Enable new business models • Functionality-as-a-service (SaaS, BPaaS), outcome-based • Address concerns highlighted by the cloud • Security & Privacy • Take advantage of specific capabilities • Multi-tenancy, Metering & Billing, Self-service Provisioning, … Can we use pattern-based analysis to help determine the best course of action to transform an application for the Cloud environment? • Why Patterns? • Well-known in the literature (Gang of Four, …) • Widely applied in software architecture, design & engineering • Can capture best practices at many different levels • From High-level / Platform Independent to Very Specific

  3. Outline • Transformation & Patterns • Definition of a Transformation Problem • Mathematical Formulation • Cloud Transformation Advisor • Related & Future Work

  4. Cloud Transformation – What needs to be considered? We define the union-intersection of Application Profile, Enablement Pattern, and Cloud Platform Information as a set of common features and quality attributes (General Transformation Template) • Application Profile Information • Features about the application to be transformed and its context • Architectural Details • Implementation Details • Business / Project Context • Requirements to be addressed by Transformation • Functional • Non-Functional (Quality Attributes) • Enablement Pattern Information • Problem that is solved • Requirement addressed by the pattern • Activities to apply the pattern • Roles, skills, effort, tools, automation, … • Features required by a pattern • Architectural, platform, language, technology, etc… • Quality Attributes • Cloud Platform Capability Information • Features supported by a particular cloud platform • Infrastructure, Platform layers • Supported Middleware • Cloud Services

  5. Enablement Patterns – Example Each pattern is represented in the knowledge base in terms of the set of activities, roles, and skills required to apply the pattern, as well as the set of dependencies, which are prerequisites to the usage of the pattern. • Pattern e11 uses the mediation capabilities of an Enterprise Service Bus to route a request (in this case a vetting request) to a service provider based on the ID of the tenant (participant) • Pattern e12 uses the dynamic routing & assembly functionality of the IBM WebSphere Business Services Fabric product to accomplish the same objective *source: IBM Software-as-a-Service Blueprints

  6. A Transformation Problem Instance • Given: • Application to be transformed and a set of requirements • Knowledge Base of enablement patterns which each address one requirement (multiple patterns per requirement) • Determine: • The “best” solution, where a solution consists of a set of patterns which collectively address the application requirements • The total number of possible solutions (cardinality) N(S): (where lis the total number of requirements; niis the number of the candidate patterns for the ith requirement, and n is the total number of patterns) Example: Application with 3 requirements. The knowledge base contains 3 patterns which address requirement r1, 2 patterns which address requirement r2, and a single pattern which addresses requirement r3. The possible solutions are (p1,1, p2,1, p3,1), (p1,1, p2,2, p3,1), (p1,2, p2,1, p3,1), (p1,2, p2,2, p3,1), (p1,3, p2,1, p3,1) and(p1,3, p2,2, p3,1)

  7. Transformation Problem - Mathematical Formulation • Requirements for a cloud application: • The set of all enablement patterns: • A feasible solution is represented by an array where: • The set of all features: • Enablement Pattern-to-Features mapping (from pattern harvesting): • Defines the set of features required by each enablement pattern • Application Profile-to-Feature mapping (from user): • Defines the set of features contained by the application Example: Application & Enablement Pattern mapping to Features The Application includes features f1, f3, f7, and f8 Pattern p1,1 requires feature f1, Pattern p1,2 requires features f2 & f3, …

  8. Transformation Problem - Mathematical Formulation, cont’d • Define “best” solution as the one which minimizes the number of conflicts between the features required by its set of enablement patterns and the features utilized by the application to be transformed: • How to calculate for a given solution? • Let represent whether a given feature is required by a pattern in the solution but not included in the application, i.e.: • Let H be a (u x n) dimensional matrix which defines enablement pattern – application feature relationships, such that: • Then but not included in the application Column j: contains a 1 for each feature required by the jth pattern Multiply each row iby ai a1 . . . au x x u x n Knowledge Base Application Profile

  9. Transformation Problem - Mathematical Formulation, final • So given the above, we obtain the optimal solution (from a transformation fitness perspective) by solving for: • This is an integer programming problem • For large problem size, use branch and bound or heuristic algorithm • Given the optimal solution (set of enablement patterns), we can then use a similar formulation to solve for the cloud platform which best supports the chosen set of enablement patterns • Coverage for a given platform of the features required by the selected patterns (See paper for formulation which takes into account pattern cost / effort) Ensures that only one pattern is selected for each requirement

  10. Transformation in Practice • Technical Feasibility (e.g. defined as above) is only one aspect of pattern selection • In Practice, the choice of patterns for transformation involves analysis of trade-offs • Technical feasibility • Non-functional characteristics (e.g. quality attributes) • Generic: efficiency, reliability, scalability • Domain-specific: level of isolation, encryption strength • How can we leverage the mathematical formulation for feasibility to assist in the selection of patterns? Candidate Applications for Transformation Enablement Pattern Knowledge Base Application 1 Application 2 Application 3 Cloud Transformation Advisor Transformation Option 1 Activities, tools. skills needed Transformation Option 2 Activities, tools. skills needed Transformation Option 3 Activities, tools. skills needed ✓

  11. Cloud Transformation Advisor • Realizes a Phased Approach to assist the Architect / Consultant in determining the best solution for Cloud Transformation: • Identify Required Capabilities • Generate & Assess Transformation Alternatives • Evaluate and Select Solution Cloud Transformation Advisor Steps Understand “why” Evaluate “how well” & make selection Define “what” & “how” Check feasibility Evaluate transformation alternatives Select transformation solution Identify biz scenarios* & desired cloud capabilities Compose transformation alternatives Collect Data *optional Collect Data Phase III : Evaluate Phase II : Compose feasible technical solutions Phase I : Collect data

  12. Step 1: Data Collection Application Information is entered into the tool A set of required capabilities is selected

  13. Step 2: Alternative Generation & Assessment The advisor generates transformation alternatives For each alternative, a feasibility check is performed (with additional application information input as needed)… …and transformation activities are determined

  14. Step 3: Evaluation & Selection Alternatives are compared using a set of criteria (effort & quality attributes) A report can be generated for the selected solution

  15. Knowledge Base Portal for Pattern Harvesting

  16. Related Work • Zdun and Avgeriou describe a systematic approach for the modeling of architectural design patterns through the use of architectural primitives • They advocate a more structured representation for describing their architectural patterns in order to address issues of expressiveness and variability • Kamal and Avgeriou extend this with a focus on enriching the capturing semantics around the behavior of a pattern • Zdun, et.al. describe an architecting process that is based on pattern selection, which is also supported by a set of tools • Focus is more on documenting the architectural decisions that are made rather on providing assistance to guide the selection of patterns • Petter, et.al. propose a framework for pattern evaluation based on design science, which includes a set of criteria to be used • Focused on the pattern lifecycle management as opposed to pattern selection for usage

  17. Future Directions • Mathematical Model • Extensions to the mathematical model and its application in the Advisor • Incorporating additional decision factors (quality, cost-benefit, risk, …) into an overall mathematical framework for analysis • Engineering Perspective • Improved support for pattern harvesting and knowledge base management by domain experts • Automated data collection for Advisor input • Integration with discovery & code scanning tools • Automation of Transformation activities • Additional user assistance enabled by tracking usage of the tool • collaborative filtering techniques to augment the advisor’s recommendations • determination of content quality • identification of areas of need in terms of harvesting additional content

  18. For More Information, contact: Yi-Min Chee ymchee@us.ibm.com Thank You

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