Peter X. Gao, Andrew R. Curtis, Bernard Wong, S. Keshav. Cheriton School of Computer Science University of Waterloo

Transcription

1 Peter X. Gao, Andrew R. Curtis, Bernard Wong, S. Keshav Cheriton School of Computer Science University of Waterloo August 15,

2 = ~1M servers CO 2 of 280,000 cars 2

3 Datacenters and Request Routing DC 2 Dynamic DNS DC 1 3

4 Where to route? Datacenter Latency Electricity Price Carbon footprint DC1 (Texas) Low High High DC2 (Washington) High Low Low Texas Washington Other 8% Nuclear 10% Gas 10% Coal 8% Other 6% Coal 37% Gas 45% Nuclear 9% Hydro 67% 4

5 Carbon Footprint (g/kwh) Where to route? A.M. P.M. Datacenter Latency Electricity Price Carbon footprint DC1 (Texas) Low High High DC2 (Washington) High Low Low Datacenter Latency Electricity Price Carbon footprint DC1 (Texas) Low High Low DC2 (Washington) High Low High

6 How to split? DC 1 DC 2 6

7 FORTE and its Contributions FORTE: Flow Optimization based framework for Requestrouting and Traffic Engineering Contributions: Principled framework for managing the three-way trade-off between access latency, electricity cost, and carbon footprint Green datacenter upgrade plans Impact of carbon taxes on datacenter carbon footprint reduction 7

8 Surprising Results FORTE can reduce datacenter carbon footprint by 10% with no increase in electricity cost and access latency Carbon Tax is not effective because taxes are only about 5% of electricity price Electricity Cost "Carbon Cost" 8

9 Outline Model P1: Assigning users to datacenters P2: Assigning data objects to datacenters P3: Datacenter upgrade Evaluation 9

10 Model User Groups: u i NY Datacenters: n j Carbon emission: c(n j ) Electricity price: e(n j ) Capacity: cap(n j ) Data Objects: dk LA DC Requests r(u i, d k ) 10

11 Model User Groups: u i NY Datacenters: n j Carbon emission: c(n j ) Electricity price: e(n j ) Capacity: cap(n j ) Data Objects: dk LA P1 P3 P2 DC Requests r(u i, d k ) 11

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13 Outline Model P1: Assigning users to datacenters P2: Assigning data objects to datacenters P3: Datacenter upgrade Evaluation 13

14 Assigning Users to Datacenters Datacenters: n j User Groups: u i Data Objects: d k P1 14

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17 Datacenter Capacity Constraints Datacenters: n j User Groups: u i Data Objects: dk u2 u3 n4 Capacity: cap(n j ) 17

18 Scale of Linear Program Evaluation problem size: Over 1 million variables FORTE can solve it in approximately 2 min Actual problem: Can be over 1 billion variables 18

19 Fast-FORTE Greedy Heuristic Running time O(N logn) vs Simplex O(~N 6 ) Reduces running time from 2 minutes to 6 seconds Ratio between approximated and optimal objective value is

20 Outline Model P1: Assigning users to datacenters P2: Assigning data objects to datacenters P3: Datacenter upgrade Evaluation 20

21 Assigning Data Objects to Datacenters Datacenters: n j User Groups: u i Data Objects: d k P2 21

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26 Outline Model P1: Assigning users to datacenters P2: Assigning data objects to datacenters P3: Datacenter upgrade Evaluation 26

27 Using FORTE for upgrading datacenters Datacenters: n j User Groups: u i Data Objects: d k P3 27

28 Using FORTE for upgrading datacenters Datacenter operators need to decide: Which datacenters should be upgraded? How many servers in that datacenter should be upgraded? The upgrade decisions are based on: Estimation of future traffic demands Annual budget on upgrading Trade-off between cost and benefit 28

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34 Outline Model P1: Assigning users to datacenters P2: Assigning data objects to datacenters P3: Datacenter upgrade Evaluation 34

35 Akamai traffic data Datasets Akamai delivers about 15% - 20% Internet traffic 3 weeks coarse-grained data in U.S. Aggregated every 5 minutes U.S. Energy Information Administration Carbon footprint Electricity cost Data Objects: Synthetic with long-tail popularity, 10% latency tolerant 35

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38 Three-way Tradeoff Tradeoff between carbon emissions, average distance, and electricity costs. 38

39 Carbon Emission ( ton/hour) Two-way Tradeoff between Carbon Emission and Electricity Cost 7 6,8 6,6 6,4 6,2 (987, 6.5) 6 5,8 5,6 (987, 5.83) (1010, 5.73) 5,4 5, Electricity Cost ($/hour) 39

40 Will Carbon Taxes or Credits Work? Akamai uses ~2 * 10 8 kwh per year Electricity cost of 2 * 10 8 kwh: 2 * 10 8 kwh * 11.2c/kWh = $22.4 M Carbon cost of 2 * 10 8 kwh : 2 * 10 8 kwh * 500g/kWh = 10 5 t 10 5 t * $10/t = $1 M Electricity Cost "Carbon Cost" 40

41 Green Upgrades Low Electricity Price CA1 WA1 Use Green Energy Use Green Energy Reduce Access Latency Reduce Access Latency NY1 NJ2 NJ1 CA2 TX1 Year1 Year 2 Year 3 Reduces carbon emission by 25% compare to carbon oblivious plan 41

42 Conclusions FORTE is a request routing framework that can reduce carbon emissions by 10% without affecting latency and electricity cost Surprisingly, carbon taxes do not provide sufficient incentives to reduce carbon emissions A green upgrade plan can further reduce carbon emissions by 25% over 3 years 42

43 Acknowledgement We thank Prof. Bruce Maggs for providing us access to Akamai traces We thank our shepherd Prof. Fabian Bustamante and the reviewers for their insightful comments 43

44 25% carbon reduction FORTE performs upgrades every June, hence the drop in carbon emissions every 12 months. 44

45 Impact on Application Service Providers There are three types of service providers: Integrated Service Provider. e.g.: Google, Facebook Infrastructure Service Provider. e.g.: Amazon Cloudfront, Microsoft Azure Content Distribution Network. e.g.:akamai, Limelight Service providers that own their infrastructure can use FORTE to reduce their carbon footprint. 45

46 Is electricity price and carbon footprint correlated? 46

47 PUE Our experiment: PUE = 1.2 Google 2012: Average PUE = 1.13, Best PUE = 1.08 Industrial Average 2011: PUE =

48 Energy and Emergy (Embedded Energy) Embedded energy accounts for about 20% of the total energy over a datacenter s lifetime Chang et. at., Green Server Design: Beyond Operational Energy to Sustainability, HotPower

49 Datacenter electricity consumption and carbon footprint Datacenters consume 1.3% of worldwide electricity and it is expected to grow to 8% in [Analytics Press] Datacenters carbon emission is about 0.6% of world total and it is expected to grow to 2.6% in 2020, exceeding Germany. [McKinsey Quarterly, Nov 2008] 49

50 Three-way tradeoff 50

51 Average Carbon Footprint in U.S. 51

52 Population Density Map 52

53 Residential Electricity Price Map 53

54 Data Source Details Data User Groups Datacenters Location Datacenters Capacity User Requests Datacenters Carbon footprint Datacenters Electricity Price Data Objects Access Latency Source Akamai Akamai Akamai Akamai U.S. Energy Information Administration U.S. Energy Information Administration Simulated Approximated by geographical distance 54