1. Improving Software Quality with Generic Autonomics Support Richard Anthony The University of Greenwich

2. Richard Anthony, The University of Greenwich, February 2005,2 The complexity problem for large and distributed applications: Complex internal behaviour Complex interactions with environment Complex interactions between components Complex run-time configuration and tuning Difficult to account for all possible behaviours at design time Autonomics provides a solution: Applications (and components thereof) continuously adapt to their environment and context Self- {Optimise, Protect, Heal, Configure, Manage} However, embedding autonomics represents significant risk: Non-deterministic behaviour (predicting the behaviours) Stability (limiting the envelope of behaviours) Testing complexity (capturing the behaviours) Testing costs (need to test component-wise AND holistically because of emergent qualities)

3. Richard Anthony, The University of Greenwich, February 2005,3 Our approach: Generic support for autonomics Better software: Development effort focussed on the business logic Much of the testing requirement (autonomic aspects) removed Risk (of instability!, of not working!) reduced Re-use of code and behaviour Through: API / code libraries that provide core autonomics capabilities Self-adaptive policy-based computing Non-deterministic aspects wrapped with deterministic shell; Can better meet the non-functional requirements such as: {Scalability, Robustness, Stability, Low-latency, Efficiency} with fewer conflicts / less compromise → Lower interaction complexity → Lower communication complexity Layered framework

4. Richard Anthony, The University of Greenwich, February 2005,4 Two-Dimensional Autonomics: Generic Autonomic Services in a layered framework Autonomic services such as: Service and feature discovery Dynamic cluster creation Self-managing deployment over cluster Each layer operates independently, exhibiting autonomic behaviours such as self-configuration A specific node remains autonomous and can have roles in each layer Application layer System layer Cluster layer

5. Richard Anthony, The University of Greenwich, February 2005,5 Autonomics Research at Greenwich General approaches: Nature-inspired emergence Generic autonomics Layered architecture for autonomics services Biologically-inspired concept of layered self-healing Specific services / applications: Emergent Election Algorithm Emergent Cluster Management Self-configuring Parallel Application Deployment Autonomic Application Management Connectivity services for Wireless Devices in Grid environments