1. A Research Agenda for Accelerating Adoption of Emerging Technologies in Complex Edge-to-Enterprise Systems Jay Ramanathan Rajiv Ramnath Co-Directors, CERCS@OSU 23 rd April 2009

2. Progression to Edge-to-enterprise

3. Complexity Challenge - Example Nationwide Hurricane Katrina events caused high volumes and unexpected fluctuations in certain request types (claims) Customer service representatives needed to identify and triage the critical needs Request volumes caused change in application loads There was impact across servers, the hardware and the communication infrastructure There were unexpected changes in performance due to IT reallocations resulting in calls to the IT Help Desk IT help desk now needed to know how to deal with these new IT problems All of the above are the multi-dimensional aspects of a single complex system Technology by itself will not address all these aspects

4. Problem scope: Managing change from the edge through the enterprise Technology is not the biggest challenge with respect to adoption – Yesterday’s workshop showed ‘Cloud Computing’ is not just about IT and computing. – E.g. Developing SLA! To do it right requires investment in domain analysis, else the result is conflict, and usually an over-provisioned, expensive infrastructure. E.g. To leverage Clouds, you need: – Computing tools and technology, – Economic analysis capabilities – Organizational change management – Business process reengineering Need to think evolution, not transformation – the paradigm is one of continuous measurement, management and improvement - at all dimensions of the business – E.g. for data center management need to understand more than the mechanics of virtualization – Need to understand interactions between the facility, power and computing – Need a knowledge management process to support the evolution Complexity in breadth rather than in depth should also drive the research agenda, Requires development of a single, integrated methodological framework

5. Framework Objectives Co-engineering of customer goals, business goals, operational goals, and technology goals Integration of creational, operational and evolutionary views of underlying components. Why? – Separation of functional and non-functional (application vs. infrastructure, business transaction vs. IT transactions) aspects means (for example) we cannot easily correlate the network traffic to an application function and to business value (needed to argue that the network costs are appropriate!) – Separation of creational, operational, and evolutionary aspects means (for example) disconnects in defining the impact of a change to an existing architecture

6. Next idea? Traceability-enabled Adaptive Complex Enterprise First we need common abstractions and a shared theory, for example: An enterprise and its environment forms a complex system consisting of a set of shared Agents that interact and are ‘interested’ in the value provided by others An Agent is also a business value provider – is human or automated and autonomic (hides detail) An Agent is also a customer/stakeholder interested in certain outcomes - Business, IT, Operations, Strategy- of other agents All Agents can see the value of interaction with other agents, as authorized Physical Agents are made visible through sensors

7. Common Abstraction  Enterprise Ontology Concept  Shared Agent Creation Longevity Evolution Legacy Interaction Adds Value

8. Agent Interaction Modeling Value is created (or not) when Agents interact Provides the context for monitoring that provides the traceability. – Identifies the linkages to instrument to get traceability across layers – Helps develop policies and guidance for process, resource, data use, security and assurance – Enables line of sight visibility into agent value for decision making - e.g. what to charge for my service – Enables dynamic ‘collaboration’ between agents – they can ‘see and act’ accordingly

9. Example – Traceable Visualization of 311 Data of Interactions between Requests and Agents Potential Improvement in Response Policies

10. System Dynamics- City helpdesk triage example of interaction throughput rates between multiple roles (dynamic assignment to agents) using Vensim

11. City services impact analysis identifying points of innovation

12. Summary

13. Research Objectives A unified theory for Adaptive Complex Enterprise (ACE) systems – to replace silo-based experiential enterprise- related knowledge. – Enables tools for predictive management What happens when we move scope of services from individual to group to departments to enterprises to multi-enterprise? When, where and how are we really becoming more efficient? – Allows us to share principles and theories more effectively within the community. Approach: Develop and validate theory through field experiments on real industry problems. – Needs adoption of ACE (at least for now) and Deep Industry- University Collaboration