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 Samir Khuller, University of Maryland Bradley Kuszmaul, MIT Alexandros Labrinidis, University of Pittsburgh Mohamed Mokbel, University of Minnesota Meikel Poess, Oracle Corporation Yicheng Tu, 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?