AXP: Adaptive eXecution Platform for Services Grids Prof. Dr. Andreas Polze Peter Tröger Operating Systems and Middleware Group

Who We Are Hasso Plattner UP ■ Privately funded institute ■ Bachelor and Master in IT Systems Engineering ■ ~50 people for ~400 students Operating Systems and Middleware group ■ Prof. Dr. Andreas Polze + 6 Ph.D.’s + 2 Post-Docs ■ “Extending the reach of middleware” ■ System predictability in wide-area distributed computing ■ Dependable control systems with small devices

BB-Grid Workshop Teaching Architecture of COTS operating systems ■ Windows 2000, Mac OS X, BSD Unix, Solaris Architecture of component-based, distributed systems ■ Development of distributed applications with middleware platforms ■ CORBA, J2EE, COM+,.NET, Web-Services Operating systems for embedded and mobile devices ■ Windows CE,.NET Compact Framework ■ Real-time operating systems (LynxOS, VxWorks, QNX) Paradigms, design patterns and implementation strategies ■ Predictable behaviour for component-based, distributed and embedded systems ■ Performance, fault tolerance and timeliness

BB-Grid Workshop Research Extension of middleware for predictable systems ■ Paradigms, design patterns, implementation strategies ■ Timeliness, security, fault tolerance Distributed systems ■ Predictable behaviour in heterogeneous distributed systems ■ Legacy integration and vertical monitoring in SOA frameworks ■ Standardization and service orientation in grid environments Embedded systems ■ Analytical redundancy and online replacement ■ Dynamic (re-)configuration of component systems ■ Aspect-oriented programming in C# and.NET

BB-Grid Workshop Aspect Weaving Adaptive Reconfiguration Extending The Reach Of Middleware Coordination Languages Dynamic Placement Grid Service Provisioning Adaptive eXecution Platform Microsoft WRK Predictable Resources Distributed Control LabAdaptive Services Grid Dynamic Updates Embedded Middleware Standards Resource Partitioning Embedded Devices Distributed Systems Programming Models

BB-Grid Workshop Registry S1 S3 Service Composition S3 S1 Services Grid Infrastructure Reply App Adaptive Process Management Register Service Provider Request App Service Requester S1 S2 S3 Integration of Internal Services External Legacy Systems External Web / Grid Services S2 Instance Monitoring & SLA Negotiation Scalability, SLA fulfillment S2 * S1*

BB-Grid Workshop ASG Architecture

BB-Grid Workshop Stateful Service Concept

BB-Grid Workshop AXP Architecture

BB-Grid Workshop AXP for DCL

BB-Grid Workshop AXP Service Lifecycle Service Deployment Service Instantiation Service Placement Service Update Service Destruction Service Usage Service Monitoring Service Undeployment Client Admin

BB-Grid Workshop Dynamic Placement Coordination layer decides about placement of services on execution resources ■ AXP stack ensures data consistency for concurrent activities from clients ■ Demands central state data handling ■ One client-visible logical instance per instantiation ■ Multiple physical instances on differing execution hosts, dynamic routing Apply dynamic resource allocation strategies ■ Theoretical foundations from Capacity Planning research (Q-RAM, LogP) Service Container A Service Container B Service Container C svc1.1 svc2.1 svc1.2 Service Request Router svc1.3 svc3.1

BB-Grid Workshop Unified Monitoring Data Model Request package enters platform (source: WSQM) Service reachable, but broken (source: Laprie) Time for EJB / Handler processing (source: JSR-77) Finished requests / uptime (source: WSQM) Service not reachable (source: WSLA) Service Resource

BB-Grid Workshop AXP & DaimlerChrysler Telematic ASG C5Mobile Service Provider Renesas SH7780 QNX Neutrino MOST BUS GSM/ GPRS Module CAN BUS JSME Commu- nication Module External Service Proxy Service Atomic Service OSGi C-5 Coordination Layer SOAP Protocol Stack

BB-Grid Workshop Grid Aspects of AXP Dynamic Placement ■ On-demand allocation of grid resources as execution host ■ Submit stand-alone service executable (servlets) or container Application of standards ■ Re-use of WSRF work in service environment ■ DRMAA API specification work ■ J2EE-compliant, portable implementation Scheduling ■ Resource allocation strategies ■ Theoretical foundations (capacity planning, Q-RAM, LogP) ■ Resource partitioning on grid nodes ■ Re-use of grid prediction mechanisms (NWS, meta-schedulers)

BB-Grid Workshop ASG Testbed in BB-Grid BB-Grid Head Node Dual-Xeon; 400GB RAID5; daily backup Dual-Xeon, 2GB RAM, 250GB HDD, Debian Linux Java, Condor 4-Way UltraSparc2,16GB RAM, 140GB HDD Solaris 10, Java, Condor Dual-Itanium, 1GB RAM, 12GB HDD Debian Linux, Java, Condor

BB-Grid Workshop Cooperation EU projects ■ Adaptive Services Grid ■ Leonardo Da Vinci Deutsche Post IT-Solutions ■ AOP ■ Embedded systems Microsoft / Microsoft Research ■ Micro.NET ■ Windows Research Kernel ■ Curriculum Research Kit Bachelor projects ■ DaimlerChrysler Research ■ Siemens AG ■ Software AG

BB-Grid Workshop Backup

BB-Grid Workshop The Distributed Control Lab Visual Studio Integration

BB-Grid Workshop Aspect Weaving Adaptive Reconfiguration The Big Picture Coordination Languages Dynamic Placement Grid Service Provisioning Adaptive eXecution Platform Microsoft WRK Realtime.Net Predictable Resources Distributed Control LabAdaptive Services Grid Dynamic Updates Micro.Net Lego.Net Migration Monitoring Data Model SOC Resource Partitioning Service Composition Semantic Web SLA Management