Towards “Good-enough” Autonomic Computing Architectures: A Goal-Oriented Approach

1. Towards “Good-enough” Autonomic Computing Architectures:A Goal-Oriented Approach Lawrence Chung The University of Texas at Dallas

2. Autonomic Computing: A Requirements View “A smart machine should • understand the tasks to be performed, • perform the required tasks, and • manage itself with the goal of staying available and performing efficiently. “ [Hong et. al] • Functional requirements – what the system should do - task-oriented • Non-Functional Requirements – properties the system should have in doing what the system should do - availability, efficiency, stability, sustainability, security, usability, reconfigurability, interactivity, survivability, evolvability, adaptability, …

3. Autonomic Computing: An Architectural View • Architectual design space potentially huge Mapping requirements into architectural constituents: • Components– Process, Data, Control, Resource, etc. – what, how many • Connections – explicit, implicit, #param, …, RPC, Messages, MOMs, etc. – what, how many • Constraints– dependencies among components, (de-)activation conditions, etc., • Patterns– structural, behavioral, etc. • Styles– OO, Imp. Invocation, Pipe&Filter, … • Rationale – why the particular architectual solution • Infrastructure – J2EE/.NET/CORBA/… Is a decision-making process. Autonomic computing architecture ~ only as good, or bad, as the development decisions!

4. Autonomic Computing:A Goal-Oriented Approach “… manage itself with the goalof staying available and performing efficiently…” [Hong et. Al] • Requirements as Goals • Functional Requirements as Hardgoals • Non-Functional Requirements as Softgoals • Refinements of Goals • Analysis of Goal Conflicts and Synergies • Exploration of Satificing Design Solutions [Simon] • Analysis of Tradeoffs through Correlations • Evaluation of Goal Satificing • Traceability • Use of Repository of reusable knowledge

5. Adaptability: A Key Success Factor “Autonomic computing systems have the ability to manage themselves. And to dynamically adapt to changes in business policies and objectives…” http://www.ibm.com/autonomic/pdfs/WAS_AC_WhitePaper.pdf Adaptability is the ability of software systems to accommodate changes in their environment. Software System Environment Environment: OS, hardware platform, heat, noise, speed, number of interrupts, vibration, power, attenuation, new commands, etc.

6. Why Adaptability? • Easier to develop systems satisfying new requirements • Facilitates addition of new software features • Saves maintenance costs • Staying useful • Survival of the fittest!

7. Examples of Adaptable Systems • Dual-mode cell phone • Dynamic uploading of firmware • Command-processing system accepting commands of different versions • A software system operating on different OS • Performing system maintenance functions when the system is least busy • A dynamically changeable format - from 2 digit year to 4 digit year; change units of measurement • Mars Pathfinder project • eLiza project at IBM

8. Representation and Reasoning • Softgoal Interdependency Graph (SIG) Example SIG Legend

9. Autonomic Computing:Towards A Society of Collaborating Agents • Shift in paradigm • From OO to GO (& AO) modeling • From Distributed to Collaborative computing • Collaborative Infrastructure – J2EE/.Net/CORBA/… not enough • From non-intentional (OO) to intentional (GO) middleware • From non-social to social (AO) middleware