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Self-Managing Systems for Automated SLA Management in Computing Utility

Explore controlling peak load challenges through dynamic server allocation and throttling to manage SLAs automatically. Learn about Oceano's mechanisms for monitoring, event correlation, and control actions like content-based throttling. Discover Autonomic Reservoir Optimization on Grids, featuring reservoir simulation, optimization services, economic modeling, and real-time data processing. Discuss the implementation of self-optimizing behaviors and interactions among various components within the system.

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Self-Managing Systems for Automated SLA Management in Computing Utility

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  1. CompSci 296.2 Self-Managing Systems Shivnath Babu

  2. Reminder • Slides and 2-page writeup due today • Thursday: Control-theory paper • Student presentations from next month • Feb 21 (next Tuesday): Progress talk, <= 5 minutes • Feb 23: Speaker from Cisco • Feb 28: Speaker from IBM Tivoli

  3. Oceano • Setting: Computing utility • Goal: Automatic SLA management • Challenges: Peak load >> average load, shared • Simple solution: overprovision • Solution: • Domain • Events: Monitoring, correlation • Control actions: Dynamic server allocation, throttling

  4. Oceano: Mechanisms • Figure 2 • Monitoring: detect events • Aggregation and correlation of events • Events  control actions

  5. Discussion • Strong points? • Weak points? • How does Oceano differ from related work? • How does Oceano deal with overload? • Content-based throttling • Allocating a Dolphin • Experiments

  6. Autonomic Reservoir Optimization on Grids • Prototype application: placement and operation of oil wells to maximize revenue • Proof of concept for: • New paradigm of application deployment • Peer-to-peer interactions among application modules, Grid services, resources, and data • Autonomic optimization • Self-optimizing behavior within and across components

  7. Components • Reservoir simulation (IPARS) • Optimization services (VFSA) • Economic modeling • Real-time data • Historical archives • Experts (collaborative portals)

  8. Interactions and Implementation • Figure 3 • Pawn: Figure 5 • Implementing reservoir optimization using Pawn

  9. Discussion • What does this work show? • Did they pick the right application? • How “autonomic” is this work?

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