Inside the DBMS

1. Inside the DBMS Energy Awareness and Energy Management

2. Group Participants • Michael Bender, Stony Brook University • Goetz Graefe, HP Labs • Le Gruenwald, National Science Foundation • Volker Hoefner, University of Kaiserslautern • SamirKhuller, University of Maryland • Bradley Kuszmaul, MIT • Alexandros Labrinidis, University of Pittsburgh • Mohamed Mokbel, University of Minnesota • MeikelPoess, Oracle Corporation • YichengTu, University of South Florida • Bo Zeng, University of South Florida

3. Repeated question • How is energy efficiency different than optimizing for • space (i.e., storage) and • time (i.e., performance)?

4. Indexing / Storage • How to build/maintain an index in an energy-efficient way? • E.g., deferred maintenance to handle spikes • Traditional trade-offs different now: • Load balancing VS switching off • How to consider different technologies at the same time? • Should we expose APIs for cross-layer optimization?

5. Concurrency Control & Recovery • Recovery/resiliency/fail-over are prime candidates for revisiting for energy efficiency • E.g., use (redo-only) logs vs copies • Power-aware concurrency control is possible • E.g., use latches more often / optimistic CC • Consider different storage layers/hardware alternatives

6. Query Execution (1) • What makes an algorithm energy-efficient? • Can new join/group-by algorithms be energy-efficient? • Is fast automatically energy-efficient? • No; E.g., Differential Voltage Scaling • Would data compression help? • More data fit in memory • Computation directly on compressed data?

7. Query Execution (2) • How can scheduling help? • E.g., load shaping by shifting load for later to avoid spikes (i.e., over-provisioning)

8. Query Optimization (1) • Cost models for energy consumption (need instrumentation) • Compile-time decisions should be shifted to run-time (to handle load/energy cost) • Binary decisions VS gradual transitions • Take into account different hardware options

9. Query Optimization (2) • Performance improvements: • percentage [not interesting] • factors [starts to get interesting] • orders of magnitude [really interesting] • ARE THERE ORDER OF MAGNITUDE OPPORTUNITIES? • Easy: utilizing new hardware • Difficult?

10. Query Optimization (3) • How to consider energy, as part of self-managing data management? • Auto-admin-style optimizers for storage/performance • Can there be query optimizers for energy consumption? • Can there be a “here’s 2 KWh, do the best you can” optimizations?

11. Benchmarks • Infrastructure needed to reduce barrier of entry to research in the area • E.g., a resource sharing repository as a start • Can we include energy in a way similar to $ for TPC benchmarks? • Idea for SIGMOD programming content topic to be energy-efficient algorithms

12. Involving the user (1) • Link query execution to energy spent • Use real dollar cost of energy instead of just amount of energy spent • Distinguish processing during peak energy demand hours VS low demand • Can differentiate for sustainability (i.e., charge for energy from renewable sources is cheaper) • Consider as part of SLAs (with big grain of salt)

13. Involving the user (2) • Vendors providing differentiated service, that includes energy costs • how can users optimize over different vendors?