Budapesti Műszaki és Gazdaságtudományi Egyetem Méréstechnika és Információs Rendszerek Tanszék Infrastructure for Model-based Control of Distributed IT systems Gergely János Paljak Advisors: András Pataricza, Tamás Kovácsházy

Federated, virtualized data center Virtualization Intelligent system management Application A Application B Server 1 Server 2 Server 3 Central resource pools Individual servers, static allocation sensors for capacity/utilization monitoring Individual servers, static allocation sensors for capacity/utilization monitoring Virtual servers: Easier reconfiguration in runtime Virtual servers: Easier reconfiguration in runtime Federated DC: centralized management, intelligent decision making for provisioning

Manual configuration of system supervision Software Component Monitoring Server Presentation Server Historical Data Deployment of supervisory infrastructure: -Moderate effort -Low probability of faults Deployment of supervisory infrastructure: -Moderate effort -Low probability of faults S S S S S S Deployment of sensors (local agents/agentless): -Moderate effort -Some configuration faults can be introduced Deployment of sensors (local agents/agentless): -Moderate effort -Some configuration faults can be introduced Configuring alerting & diagnostic logic: -Effort larger by orders of magnitude -Faulty configurations due to ad-hoc design Configuring alerting & diagnostic logic: -Effort larger by orders of magnitude -Faulty configurations due to ad-hoc design

System Management as a Control Problem Software Component Service deployed on provides Decision Making Control theory Monitoring Provisioning Controlled Plant Sensors Controller Actuator Collect and store data about the state of the infrastucture Based on human expertise or automation Effectuate changes in the infrastructure applied to IT Infrastructures Control Objective (e.g. SLA) Control Policy Supervised NodeMonitoring / Control Node

Architecture Realistic infrastructure: Multi-tier Widely-used components Realistic infrastructure: Multi-tier Widely-used components Realistic workload Reconfigurable in runtime Integrated system monitoring, wide range of measured metrics Integrated intelligent data processing (in MatLab)

Measured attributes  We chose to measure all possibly relevant performance attributes  Leave the selection of truly relevant attributes to data processing PlatformPlatform AgentAgent Processes AgentAgentAgentAgent AgentAgentAgentAgent Middle- ware Clients Ex. CPU idle (%), free memory (kb), network bytes sent Ex. MySQL threads, Tomcat processing time, Apache open connections Ex. MySQL threads, Tomcat processing time, Apache open connections

Variable selection - the dilemma LinearEntropy based Objectivemin E(distance error 2 )max (shape similarity) Feature preservationSimple projectionMore context InvarianceLinear transformationAny bijective function Main characteristics retainedAvarege distanceShape Plane mirror Less details Less distortion Plane mirror Less details Less distortion Spheric mirror More details Huge distortion Spheric mirror More details Huge distortion Paljak, Kocsis, Égel, Tóth, Pataricza: „Sensor Selection for IT infrastructure Monitoring”, AUTONOMICS ‘09

Future work metric Software Component Service deployed on provides metric Prediction Control Actuator (reconfiguration) Actuator (reconfiguration) Dimension reduction Prediction based on the smaller state space Model-predictive control for proactivity Adaptive reconfiguration

Summary  An infrastructure for evaluating control methods in system management was built  The pilot infrastructure is a three-tier server system with integrated system monitoring and data processing  We are going to use this infrastructure for o Modeling multi-tier systems (identification) o Create algorithms for control o Evaluate the developped algorithms