1. “Capping the Brown Energy Consumption of Internet Services at Low Cost” by K. Le, R. Bianchini, T. D. Nguyen, O. Bilgir, and M. Martonosi – An Algorithmic Perspective David Shmoys Cornell

2. Goals of the Paper Provide optimization-based model to balance the usage of “brown” energy vs. “green” energy in assigning customers to data centers Key issues - cap-and-trade framework above annual baseline brown energy cap - amortized requirement on response time - user requests are of multiple types - capacity of centers - minimize total energy cost

3. One “Epoch” t=1,…,T Centers iRequest Types j n(j,t) U(i)

4. One “Epoch” t=1,…,T Centers iRequest Types j n(j,t) Brown cost U(i)

5. One “Epoch” t=1,…,T Centers iRequest Types j n(j,t) Green cost U(i)

6. One “Epoch” t=1,…,T Centers iRequest Types j n(j,t) U(i) There is also a baseline energy cost for each center depending on - Brown vs. Green - which center i’s load Load(i) And also an offset cost C(i,Load(i))

7. Decisions, Decisions, Decisions Compute mix of green vs brown power available at each center (fixed for “entire” planning period 1,…,T)

8. One “Epoch” t=1,…,T Centers iRequest Types j n(j,t) U(i)

9. Decisions, Decisions, Decisions Compute mix of green vs brown power available at each center (fixed for “entire” planning period 1,…,T) Compute assignment of requests to centers (assuming proportionate energy type transmission cost?) Service level requirement (at least p% requests handled with delay at most D)? Total brown energy cap over all centers

10. Obvious Algorithmic Playground Online algorithms – Do requests come one at a time? Or rather epoch by epoch? Apply viewpoint of competitive analysis? Two-stage stochastic model – use distributional model on arrivals to determine brown/green mixture rates and then minimize expected cost over planning horizon (say willing to adjust mixture each epoch) Multi-stage stochastic models?