A widely used approach to achieving service level objectives for a software system (e.g., an email server) is to add a controller that manipulates the target system's tuning parameters. We describe a methodology for designing such controllers for software systems that builds on classical control theory. The classical approach proceeds in two steps: system identi®cation and controller design. In system identi®cation, we construct mathematical models of the target system. Traditionally, this has been based on a ®rst-principles approach, using detailed knowledge of the target system. Such models can be complex and dif®cult to build, validate, use, and maintain. In our methodology, a statistical (ARMA) model is ®t to historical measurements of the target being controlled. These models are easier to obtain and use and allow us to apply control-theoretic design techniques to a larger class of systems. When applied to a Lotus Notes groupware server, we obtain model-®ts with R 2 no lower than 75% and as high as 98%.In controller design, an analysis of the models leads to a controller that will achieve the service level objectives. We report on an analysis of a closed-loop system using an integral control law with Lotus Notes as the target. The objective is to maintain a reference queue length. Using root-locus analysis from control theory, we are able to predict the occurrence (or absence) of controller-induced oscillations in the system's response. Such oscillations are undesirable since they increase variability, thereby resulting in a failure to meet the service level objective. We implement this controller for a real Lotus Notes system, and observe a remarkable correspondence between the behavior of the real system and the predictions of the analysis. This indicates that the control theoretic analysis is suf®cient to select controller parameters that meet the desired goals, and the need for simulations is reduced.

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Managing the performance of e-commerce sites is challenging. Site content changes frequently, as do customer interests and business plans, contributing to dynamically varying workloads. To maintain good performance, system administrators must tune their information technology environment on an ongoing basis. Unfortunately, doing so requires considerable expertise and increases the total cost of system ownership. In this paper, we propose an agent-based solution that not only automates the ongoing system tuning but also automatically designs an appropriate tuning mechanism for the target system. We illustrate this in the context of managing a Web server. There we study the problem of controlling CPU and memory utilization of an Apache ® Web server using the application-level tuning parameters MaxClients and KeepAlive, which are exposed by the server. Using the AutoTune agent framework under the Agent Building and Learning Environment (ABLE), we construct agents to fully automate a control-theoretic methodology that involves model building, controller design, and run-time feedback control. Specifically, we design (1) a modeling agent that builds a dynamic system model from the controlled server run data, (2) a controller design agent that uses optimal control theory to derive a feedback control algorithm customized to that server, and (3) a run-time control agent that deploys the feedback control algorithm in an on-line realtime environment to automatically manage the Web server. The designed autonomic feedback control system is able to handle the dynamic and interrelated dependencies between the tuning parameters and the performance metrics with guaranteed stability from control theory. The effectiveness of the AutoTune agents is demonstrated through experiments involving variations in workload, server capacity, and business objectives. The results also serve as a validation of the ABLE toolkit and the AutoTune agent framework.The increasing complexity of computing systems and applications demands a correspondingly larger human effort for system configuration and performance management. This manual effort can be timeconsuming and error-prone, and requires highly skilled personnel, making it costly. Autonomic computing 1 uses the analogy of the human autonomic nervous system to suggest the use of a higher level of automation and self-management capability in computing systems.The complexity and importance of developing autonomic computing systems has attracted research

This paper describes the Agent Building and Learning Environment (ABLE) a Java-based framework for developing and deploying hybrid intelligent agents and agent applications. ABLE provides a set of reusable JavaBean components, called AbleBeans, along with several flexible interconnection methods for combining those components to create software agents. AbleBeans implement data access, filtering and transformation, learning, and reasoning capabilities. Function-specific AbleAgents are provided for classification, clustering, prediction, and genetic search.Application-specific agents can be constructed using one or more of these AbleBeans. AbleAgents are situated in their environment through the use of sensors and effectors, which provide a generic mechanism for linking them to Java applications.A GUI-based interactive development environment, the Able Agent Editor, is provided to assist in the construction of AbleAgents using AbleBean components. The Able agent platform is a FIPA-compliant distributed framework for creating multi-agent systems. The utility of the ABLE framework has been proven through its use in several IBM research projects.