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Autonomizing Legacy Systems

Autonomizing Legacy Systems. Gail Kaiser Programming Systems Lab Columbia University. The Legacy We Want. The Legacy We Get. Not Just Systems. But System of Systems. Systems of Legacy Systems.

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Autonomizing Legacy Systems

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  1. Autonomizing Legacy Systems Gail Kaiser Programming Systems Lab Columbia University

  2. The Legacy We Want The Legacy We Get Gail Kaiser/Columbia University

  3. Not Just Systems But System of Systems

  4. Systems of Legacy Systems • Methodologies and technologies are being devised for building new autonomic systems, but these often do not apply so nicely to legacy components • Autonomic capabilities are needed in “systems of systems” assembled from existing components • We propose one approach to autonomizing legacy systems and assembling autonomic systems-of-systems Gail Kaiser/Columbia University

  5. Who is “We”? • Bob Balzer and Dave Wile, Teknowledge • Nathan Combs, BBN • David Garlan and Bradley Schmerl, CMU • George Heineman, WPI • Gail Kaiser, Columbia University • David Wells and Paul Pazandak, OBJS • And colleagues and students Others welcome to Join! Gail Kaiser/Columbia University

  6. Outline • Retrofitting Autonomicity • Common Infrastructure • Probes • Gauges • Coordinated Effectors • Architectural Model-based Analysis & Decision • Progress & Contributions • Other PSL projects towards Autonomic Computing Gail Kaiser/Columbia University

  7. Retrofitting Autonomicity • Inserting adaptation (self-xyz) mechanisms into existing application code is difficult, error-prone and costly, and hard to reuse or reason about • We seek to enable autonomic properties through a solution orthogonal to the legacy systems’ main business logic and communication framework • Introduce common external infrastructure • This infrastructure becomes an integral part of the system-of-systems’ “self”, co-existing and cooperating with the systems’ functional mechanisms Gail Kaiser/Columbia University

  8. Decision Controllers Gauge Bus Interpretation Gauges Architectural Models Collection Probe Bus Probes Running System Configuration Effectors Gail Kaiser/Columbia University

  9. Four-tiered Infrastructure • Data is collected from a running system instrumented with non-invasive probes • Gauges map the probe data into the system architectural model as well as into a metric model • The “decision & control” layer analyzes implications of the interpreted data on overall system functionality and performance, and determines whether/when/how to • Deploy new (or disable existing) gauges and probes, and/or • Deploy software effectors to reconfigure or adapt individual components and/or major substructures of the system • Effectors make the actual changes Gail Kaiser/Columbia University

  10. Key Point • Augment system-of-systems with a decentralized decision & control mechanism that can specify and manage both local optimizations and full-system reconfigurations • Decentralized process/workflow definition and enactment Gail Kaiser/Columbia University

  11. Kinesthetics eXtreme (KX) • Columbia PSL’s implementation of Common Infrastructure • Currently missing architectural modeling, so usable but limited • Most of KX can be downloaded from http://www.psl.cs.columbia.edu/software.html • KX is being applied for load balancing, etc. in Telecom Italia Lab’s heterogeneous instant messaging and ISI’s open information geographical analysis system (GeoWorlds) Gail Kaiser/Columbia University

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  14. Probes & Gauges • Active Interfaces Development Environment (AIDE, WPI) inserts callback probes into Java source code • Probelets are Worklets mobile agents, so only WVM needs to be pre-installed • Smart Events proposed standard XML Schema (with OBJS) for infrastructure communication • XUES (XML-based Universal Event Service) • Event Packager transforms legacy probe output, logs & replays event streams • Event Distiller recognizes complex temporal event patterns, interfaces to decision and to gauge visualizers Gail Kaiser/Columbia University

  15. Gail Kaiser/Columbia University

  16. Coordinated Effectors • Workflakes structures decentralized workflow through plugins on top of Cougaar blackboard (with BBN) • Selects, customizes, instantiates, dispatches and coordinates Worklets mobile agents, to make local adaptations and more global reconfigurations • Currently missing workflow notation, now just Java Gail Kaiser/Columbia University

  17. Gail Kaiser/Columbia University

  18. Adding Architectural Model-based Analysis & Decision Capabilities • Acme is a proposed standard architecture description language (CMU, Teknowledge, et al) • Extensible with various annotation schemes, e.g., for architectural styles and constraints • Dynamic Acme will explicitly model architectural changes and monitor style/constraint compliance • Infer architectural descriptions from observed application behavior • Map architectural model repairs into implementation level modifications • Construct architecture-based repair strategies and translate into repair tactics managed by workflow engine and accomplished by effectors Gail Kaiser/Columbia University

  19. Progress • Multiple probe technologies interchangeable with common API • ProbeMeister (OBJS) instruments Java byte code • Instrumented Connectors (Teknowledge) instruments DLLs • Currently integrating multiple gauge technologies with common API Gail Kaiser/Columbia University

  20. Next Steps • Define strawman decision & control API, disseminate, iterate • Architectural modeling throughout • Architecture-based probe placement • Generate Gauge templates from Acme • Augment Acme with “architecturally significant implementation-level events” (ASILEs) • Choose or develop workflow interchange language between Tailor (CMU) and Workflakes Gail Kaiser/Columbia University

  21. Expected Contributions • Approach to autonomizing legacy systems and systems of systems • Common autonomic infrastructure that can easily be internalized • Standard schemas and APIs for probe, gauge and decision layers • Reference implementation Gail Kaiser/Columbia University

  22. Conclusions • Peek at what the software engineering research community is doing towards Autonomic Computing • Focus on legacy systems and systems of systems as well as methodologies and technologies for building new systems from scratch Gail Kaiser/Columbia University

  23. Common Themes Across PSL’s Other Related Research • Decentralized Processes (Workflows) • “Contextualization” • Alliances (Treaties and Summits) • Survivability and Multi-Level Security Gail Kaiser/Columbia University

  24. Air-Force Army Navy Sharedservices DISCUS Higher Security Summit Imposing Treaties Transportation Lower Security Summit to alert News-Media

  25. Decentralized Information Spaces for Composition and Unification of Services (DISCUS) • Successor to OzWeb • Builds on WebServices • (Relatively) Static Service Spaces • Dynamic Summits • Gatekeepers impose Treaties as to whether/how Summits can use Service Space services • Workflow coordinates services

  26. Survivor 007 Gail Kaiser/Columbia University

  27. Survivor 007 • Successor to Survivor NG (Next Generation) built by modifying the Naval Research Lab’s MLS Workflow System • Uses Worklets mobile agents to locate and recruit task processors dynamically and deliver tasks (including pumping, synchronization, compensating tasks) • Multi-mirrored workflow state and data • Considers possibilities of network partitioning and disabling of rogue task processors Gail Kaiser/Columbia University

  28. Gail Kaiser/Columbia University

  29. Memento Real World

  30. Memento Virtual World

  31. Memento • Successor to CHIME (Columbia Hypermedia IMmersion Environment) and AI2TV (Adaptive Interactive Internet Team Video) • Project physical meeting spaces into 3D Collaborative Virtual Environment • Project CVE information spaces into Large Displays & other devices in physical spaces • Continually augmented and reorganized contextbase using NLP and video summarization and clustering technologies Gail Kaiser/Columbia University

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