A Study on Policy-Based Interaction Techniques with Autonomic Computing Peter Khooshabeh University of California, Santa Barbara 1 Department of Psychology, IGERT Interactive Multimedia Research Group IBM Almaden Research Center User Sciences and Experience Research (USER) Group Mentor: Eser Kandogan ( ) Managers: Daniel M. Russell, Barton Smith Team Members: Christopher Campbell ( Paul P. Maglio John Bailey We predict to see a statistical interaction between interface and experience with respect to negotiation General Method Autonomic computing systems manage themselves and dynamically adapt to change in accordance to business policies and objectives. Even with fully autonomic computing, a human will be in the loop at some level. Little is known about how to best support this mixed-initiative control in the context of system administration. What is Autonomic Computing? Computers continue to become cheaper, which leads to their wide proliferation. Organizations of all sizes have networks of computers managed by system administrators. As the number of computers increases, individual manual control is unwieldy. Autonomic computing services will provide policy-based solutions. This studies user experience with policy-based interfaces for IT management. We look at several factors in a controlled experiment in order to understand cognitive representations of automation Implications and Future Work If participants with low IT management experience perform best with the policy-based interface, then this finding supports the adoption of autonomic computing by business managers not directly involved with the technical infrastructure of organizations. Determine whether it is worthwhile to be able to step-into policies, investigate policy scope, broadness, and applicability and performance. Experimental Design (Figure 1) The Model (Figure 2) Results We have developed Simsys, a simulation model that is isomorphic to realistic IT system behavior and interaction. Research Questions What are the effects of policy representation and experience with amount of policy interaction? Do participants with less experience perform better with policy-based interface compared to manual interface? Do participants perform better when using low representational specificity policies? Autonomic Computing Structures Optimization Lower Bound Configuration Upper bound Lower Bound Upper bound Healing (Internally induced) Upper bound Lower Bound Upper bound Protection (Externally induced) Methods and Materials: Experimental Test Bed System Simsys is made up of processes. Examples of processes are collections of servers. Processes have operations that they can execute in steady state. Processes can also be connected. Shoppers send requests to a load balancer and it sends it to the HTTP Servers. The request then flows to a cluster of App Servers and possibly a federation of DB’s Amount of interaction High Low Experience LowHigh manual An alternative explanation is that policy-based interfaces will generally require more negotiation. In the experiment, IBM research staff are asked to act as chief information officers for a web store. The goal of the participant is to maximize the key performance indicator of profit margin. In doing so, participants have to consider the trade-offs of IT expenditures to improve performance and profit margin. Participants monitor: Length of time for a customer to be served (minimum, maximum, average latency) Total profit; operation costs (adding different servers, on-going maintenance, performing operations on servers) System learning (as indicated by revenue) is reliably better after using manual interface and worse after using policy interface. Total Sales are reliably higher using policy interface (50% higher) policy A X B X C Figure 1i Figure 1ii Figure 2 Previous Work (Figures 3,4,5) Figure 4 Learning