1. Automating System &Device Management Ahmed Amer S-CITI Mini-Symposium 30 April 2004 University of Pittsburgh

2. Systems Research is … • Optimizing the usage of resources. • Abstracting systems details that are not of interest to the customer. • In short: “Automating optimization”

3. What can this “automation” do for you? • In days of olde … • Provided mechanisms for deciding what data to cache, which pages to swap, and items to use or avoid. • Nowadays … • Systems cost more in management and human support than the hardware costs. • Tune system parameters dynamically = prepared • Dynamic: Can it be better than any fixed setting? • Prepared 1-Can it react to changing workloads? • Prepared 2-Can it react to previsouly unknown workloads?

4. What is automation doing for S-CITI? • Universal Caching • Self-Optimizing • Fully Adaptive • Comprehensive and Simple (criteria-based) • Device Workload Management/Modification • Currently measuring the limits of predictive techniques for reducing disk power consumption.

5. Multiple Caching Policies • Example Policies • RANDOM • FIFO • SIZE • LRU • LFU • GDSF

6. Multiple Caching Policies • Example Policies • RANDOM • FIFO, LIFO • SIZE • LRU, MRU • LFU, MFU • GDSF

7. Multiple Caching Criteria • Example Policies • RANDOM • FIFO, LIFO • SIZE • LRU, MRU • LFU, MFU • GDSF • Associated Criteria • No Criteria • Arrival Order • Object Size • Recency • Frequency • Size, Recency, and Frequency

8. Ghost Caches & GD-GhOST

9. A Single “Good” Policy

10. A Change of Policy

11. Caching results so far … • Two new caching schemes: • GD-GhOST • Universal Caching • GD-GhOST • Based on a selection of GD-Size variants as component policies • Policy similarity allowed a scheme that produced a true hybrid, not policy switching • Exceeded the performance of the first master-algorithm caching scheme (ACME) • Used fewer component policies

12. Caching results so far … • Universal Caching • Uses base criteria - can be comprehensive without exhaustive listing of all policies • Appears to offer the best performance • Potentially lowest overheads and computational cost • Under active investigation to reduce it’s computational complexity and space requirements

13. Devices: Disk Power Consumption • Modifying disk behavior can potentially reduce the mechanical power consumption by over 40% in worst-case scenarios.

14. Concluding Remarks • This is ongoing work with the database and PARTS groups, and applicable within the middleware layers of the S-CITI project • You should not have to worry about low-level system tuning, we are working to ensure that… • …the algorithms will always react appropriately (as in the caching policies), or • …the algorithms will modify device behavior appropriately (as in the disk power example)