A Research Agenda for Accelerating Adoption of Emerging Technologies in Complex Edge-to-

Enterprise Systems

Jay RamanathanRajiv Ramnath

Co-Directors, CERCS@OSU23rd April 2009

Progression to Edge-to-enterprise

1980s• Individual productivity adata

management

1990s • Business process adata interoperability

2000s• Business effectiveness ashared IT

services, business monitoring, business intelligence

2010s

• Business value – limited by (a) complexity of legacy applications and (b) scale change in problem scope

Complexity Challenge - ExampleNationwide

• 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

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

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

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

Common Abstraction a Enterprise Ontology Concept a Shared Agent

Customer

• satisfaction

• value

• location

Business

• cost

• efficiency

Operational

• service

• location

Infrastructure use

• time used

• throughput

Infrastructure

• Creation• Longevity• Evolution• LegacyInteraction

Adds Value

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

Example – Traceable Visualization of 311 Data of Interactions between Requests and Agents

Potential Improvement in Response Policies

System Dynamics- City helpdesk triage

example of interaction

throughput rates between multiple

roles (dynamic assignment to agents) using

Vensim

City services impact analysis identifying points of innovation

0 1 2 30

500

1000

1500

2000

2500

3000

3500

4000

4500

5000MobilitySecurityCommunication/ reponsibility delineationOnline web based facilities (ex e-payments)Budgeting/funding processQuality controlProcurement managementGeomapping/GIS/GPSDocument managementInteroperability/system integrationExtend 311 capabilitiesAsset/ Inventory managementMonitoring systemsDocument ImagingEnterprise tools/ process trainingBusiness process mappingTools/softwareWork order system managementOutage downtimesDocumentation of work productsAutomationEncryption servicesAccess ManagementMarketing servicesInfrastructure installation

Complexity

FTEs

Summary

Autonomic(Agent)

Traceable(Interactions in a set

of shared agents)

Adaptive(Behavioral change in

context)

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