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Managing Complex Systems A Call to Arms! (and a Plea ...)

Managing Complex Systems A Call to Arms! (and a Plea ...). Jacques Sauvé - UFCG LANOMS 2007. The Problem (1). IT-based systems are becoming extremely complex Extreme distribution Hundreds of thousands of nodes Extreme heterogeneity Every system is “one-off”

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Managing Complex Systems A Call to Arms! (and a Plea ...)

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  1. Managing Complex SystemsA Call to Arms! (and a Plea ...) Jacques Sauvé - UFCG LANOMS 2007

  2. The Problem (1) • IT-based systems are becoming extremely complex • Extreme distribution • Hundreds of thousands of nodes • Extreme heterogeneity • Every system is “one-off” • Extreme rate of technology change • Your system isn’t client-server? (1980s-1990s) • Your system isn’t Web-based? (1990s-2000s) • Your system isn’t SOA-based? (2000s-?) • Your aren’t yet into Web 2.0? (2000s-?) • Your aren’t using mashups? (2000s-?)

  3. The Problem (2) • Managing these systems is becoming extremely complex • After decades, we still haven’t solved basic configuration management • We can’t even agree on basic metrics • Ex. How do you define/measure “availability”? • Management maturity evolves (ex. ITSM) but the business wants more (governance, lower risk, ...) • Wow! You’re not using ITIL? • Wow! You’re not using COBIT? • Wow! You’re not handling SOX compliance? • We don’t know how to measure risk, calculate Return On Investment (ROI), choose SLOs, ...

  4. The Problem (3) • We have a huge system (IT) supporting another huge system (a business) and someone comes along saying... • Align one with the other! • Couple them (oh no!) to give IT “visibility” into the business • Manage them autonomically ... • We don’t know how to do this ...

  5. Why? A Message ... • We can build complex systems but we don’t understand them! • How do usersbehave? • How do systems fail? • What is theemergentbehaviorofcomplex systems?

  6. About methodology (1) • We work too much like engineers (build) and not enough like scientists (observe) • Scientists observe nature; they let nature talk to them • We should observe our systems to learn • How do they run? • How do they break? • What emergent behavior do they have? • How does one (IT) affect the other (business)?

  7. About methodology (2) • Usingoursubjective, finger-in-the-air approach won’tyieldinsight • Insight comes aftersurprise • Surprise comes fromobservationnot design • Weshould build modelslater, afterwehaveobserved • Roentgen: “I didn’tthink, I experimented.” • Wedon’tevenagreeonmetricsyet • We can't even baseline well • Because IT services and infrastructure change too fast

  8. A Call to Arms! • Let us do more Experimental Computer Science using a scientific observation-based methodology to gain insight • Who better than us, the “monitoring people”? • This is somewhat of a break from tradition • Tradition can hinder discovery • Some can stick to tradition but some must seek new ways

  9. A Plea • Make Experimental Computer Science glamorous • Today, it is (wrongly) ignored • You have to be a builder to be glamorous • Add this to the conference Call For Papers • Let’s train our students better • Let’s share data! • Providers: make real data on huge systems available • Must we keep working on toy scenarios? • What insights will we get?

  10. What are the incentives? • Surely, with time, wewillgainnewinsightsandmakeclearercontributions • Surely, with time, wewill come to trustthe systems we build

  11. Thank you.

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