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Online Assignment under SLAs and Brown Energy Caps

Online Assignment under SLAs and Brown Energy Caps. Inspired by the paper “Capping the brown energy consumption of Internet services at low cost” by K. Le, O. Bilgir, R. Bianchini, M. Martonosi, and T. Nguyen, in IGCC 2010. Spyridon Antonakopoulos

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Online Assignment under SLAs and Brown Energy Caps

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  1. Online Assignment underSLAs and Brown Energy Caps Inspired by the paper “Capping the brown energy consumption of Internet services at low cost” by K. Le, O. Bilgir, R. Bianchini, M. Martonosi, and T. Nguyen, in IGCC 2010 Spyridon Antonakopoulos 2nd NSF Workshop on the Science of Power Management, August 18th 2010

  2. Background and motivation • Overarching goal: promote usage of green (renewable) energy, as a substitute – and eventual replacement – of brown(carbon-intensive) energy. • Improving energy efficiency only has a modest, indirect contribution to the above goal. • A more direct approach: brown energy caps on industrial/business entities. • May be stipulated by government regulation, or self-imposed. • For example, the yearly brown energy usage of corporation A must not exceed an absolute threshold T. • Alternatively, the percentage of green energy used by A must be at least q%. • And what if A happens to require more brown energy than the cap allows? • Must suffer a penalty: government fine (cap-and-pay), purchase of carbon offsets (cap-and-trade), etc.

  3. Tentative problem formulation • Simplified view of an Internet service infrastructure: a front end (FE) and Jdata centers (J ≥ 2) in various geographical locations. • FE receives service requestsr1, r2, r3,… sequentially, and in turn forwards each one to some data center for processing. • Service Level Agreement (SLA) specifies that at least x% of requests must be serviced with at most L turnaround time – and no request is denied service. • For data center j: • c(j) = energy cost of serving a request; • en(j) = amount of energy consumed to serve a request; • br(j) = percentage of brown energy over total energy consumed; • L(i, j) = 1 if it can serve request ri within SLA-specified turnaround time, 0 otherwise. • This may vary between requests, e.g. due to server/network loads. • Assume that for every ri at least one data center can serve it within L. • If brown energy cap violated, any excess brown energy usage is penalized by p% additional cost.

  4. Research goal • Design an online algorithm that assigns requests to data centers (while respecting the SLA) and has low competitive ratio on the overall cost. • Comments • A cap in the absolute amount of brown energy – instead of a percentage cap – does not make much sense in the context of asymptotic competitive analysis. • Possible workaround: estimate the total number of service requests within a reasonably large time interval, then convert absolute-amount caps to percentage caps. • Given a percentage cap on brown energy usage, it might be possible to game the system by unnecessarily consuming green energy. • Fortunately, the current formulation does not permit this. • What variants/generalizations of the problem allow for a reasonable competitive ratio?

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