IBM Student Workshop for Frontiers of Cloud Computing

IBM Student Workshop for Frontiers of Cloud Computing 2011 Cloud Transformation Advisor A Pattern-based Approach to Cloud Transformation Yi-Min Chee, Nianjun Zhou, Fan Jing Meng, Saeed Bagheri, Peide Zhong, Jian Wang, Chang Hua Sun, Dong Xu Duan, Sugandh Mehta, Tao Liu, Shao Liang Jia December 2, 2011 © 2011 IBM Corporation

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 (Saa. S, BPaa. S), 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 © 2011 IBM Corporation

Outline § Transformation & Patterns § Definition of a Transformation Problem § Mathematical Formulation

Cloud Transformation – What needs to be considered? § 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) 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) § 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 © 2011 IBM Corporation

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 e 11 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 e 12 uses the dynamic routing & assembly functionality of the IBM Web. Sphere Business Services Fabric product to accomplish the same objective *source: IBM Software-as-a-Service Blueprints © 2011 IBM Corporation

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) Example: Application with 3 requirements. The knowledge base contains 3 patterns which address requirement r 1, 2 patterns which address requirement r 2, and a single pattern which addresses requirement r 3. The possible solutions are (p 1, 1, p 2, 1, p 3, 1), (p 1, 1, p 2, 2, p 3, 1), (p 1, 2, p 2, 1, p 3, 1), (p 1, 2, p 2, 2, p 3, 1), (p 1, 3, p 2, 1, p 3, 1) and (p 1, 3, p 2, 2, p 3, 1) § 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 l is the total number of requirements; ni is the number of the candidate patterns for the ith requirement, and n is the total number of patterns) © 2011 IBM Corporation

Transformation Problem - Mathematical Formulation § Requirements for a cloud application: Example: Application & Enablement Pattern mapping to Features The Application includes features f 1, f 3, f 7, and f 8 § The set of all enablement patterns: Pattern p 1, 1 requires feature f 1, Pattern p 1, 2 requires features f 2 & f 3, … § 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 © 2011 IBM Corporation

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. : but not included in the application § Let H be a (u x n) dimensional matrix which defines enablement pattern – application feature Column j: contains a 1 for each feature relationships, such that: th Multiply each row i by a required by the j pattern a 1 i x . . . au § Then uxn x Application Profile Knowledge Base © 2011 IBM Corporation

Transformation Problem - Mathematical Formulation, final § So given the above, we obtain the optimal solution (from a transformation fitness perspective) by solving for: Ensures that only one pattern is selected for each requirement § 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 formulation which takes into account pattern cost / effort) © 2011 IBM Corporation

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 Cloud Transformation Advisor Application 3 Transformation Option 1 Transformation Option 2 Transformation Option 3 Activities, tools. skills needed ✓ © 2011 IBM Corporation

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” Collect Data Evaluate “how well” & make selection Define “what” & “how” Identify biz scenarios* & desired cloud capabilities Compose transformation alternatives Check feasibility *optional Evaluate transformation alternatives Select transformation solution Collect Data Phase I : Collect data Phase II : Compose feasible technical solutions Phase III : Evaluate © 2011 IBM Corporation

Step 1: Data Collection Application Information is entered into the tool A set of

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 13 © 2011 IBM Corporation

Step 3: Evaluation & Selection Alternatives are compared using a set of criteria (effort

Knowledge Base Portal for Pattern Harvesting © 2011 IBM Corporation

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 © 2011 IBM Corporation

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 © 2011 IBM Corporation