Neural Adaptive Control in Application Service Management Environment

Abstract

We introduce a learning controller framework for adaptive control in application service management environments and explore its potential. Run-time metrics are collected by observing the enterprise system during its normal operation and load tests are persisted creating a knowledge base of real system states. Equipped with such knowledge the proposed framework associates system states and high/low service level agreement values with successful/unsuccessful control actions. These associations are used to induce decision rules, which help generating training sets for a neural networks-based control decision module that operates in the application run-time. Control actions are executed in the background of the current system state, which is then again monitored and stored extending the system state repository/knowledge base, and evaluating the correctness of the control actions frequently. This incremental learning leads to evolving controller behavior by taking into account consequences of earlier actions in a particular situation, or other similar situations. Our tests demonstrate that this controller is able to adapt to changing run-time conditions and workloads based on SLA definitions and is able to control the instrumented system under overloading effectively.