A Truthful Incentive Mechanism for Emergency Demand Response in Colocation Data Centers. Linquan Zhang, Shaolei Ren Chuan Wu and Zongpeng Li


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1 A Truthful Incentive Mechanism for Emergency Demand Response in Colocation Data Centers Linquan Zhang, Shaolei Ren Chuan Wu and Zongpeng Li 1
2 Demand vs Supply in Power Industry? Ideal: Supply = Demand Fact: Supply Demand 2
3 Demand Response Marketbased program to extract flexibility on the demand side, to reduce peak energy usage and cost, and to increase adoption of renewables, etc. 3
4 Emergency Demand Response Reduce energy consumption to a certain level during emergencies; The last line of defence for power grids before cascading blackouts take place; 4
5 Emergency Demand Response 2500 Demand Response in PJM: January 7, 2014 Emergency DR 2000 Economic DR MW Reduction Hour Demand Response in PJM: January 7, 2014 Notes: Emergency DR Amounts are Emergency Energy Settlement Reductions. 5
6 Data Centers are Power Hungry In 2013, U.S. data centers power consumption 91 billion kwh of electricity; 34 large (500MW) power plants; roughly 140 billion kwh annually by 2020, 50 large power plants, $13 billion annually in electricity bills 100 million metric tons of carbon pollution per year. 6
7 Data Centers in EDR Data centers are promising participants in emergency demand response; For example, On July 22, 2011, hundreds of data centers participated in emergency demand response and contributed by cutting their electricity usage before a nationwide blackout occurred in the U.S. and Canada. 7
8 Colocation Data Centers Most large data centers are colocations; (1,200 colocations in the U.S.) Many colocations are in metropolitan areas, where demand response is most wanted; Highly Uncoordinated. Unlike owneroperated data centers, colocations have no control of the servers which are managed by the tenants; A photo of a colocation data center No incentive to save energy. Typical pricing approach is based on the tenants subscribed power at fixed rates, regardless of their power usage. 8
9 Estimated % of Electricity Usage by U.S. Data Center Segment in % 2% 37% 53% Traditional Enterprise Colocation DC Hyperscale Cloud Highperformance Computing The now U.S.$25 billion global colocation market is expected to grow to U.S.$43 billion by 2018 with a projected annual compound growth rate of 11%. Current Trend: many enterprise inhouse data centers are moving to colocations! 9
10 How do colocations help in EDR?
11 Goals of Auction Approach Provide incentive to tenants in colocations; Eliminate falsified bids from strategic tenants; Try to minimize the colocationwide cost; 11
12 an energy reduction capacity for EDR three years ahead [3]. We formulate below the social cost minimization problem, referred to as MinCost, which provides the optimal winner determination decisions and usage of BES (i.e., EDRstrategies) ColocationWide Cost Minimization for the colocation operator, to achieve the energy reduction target δ, assumingtruthfulbidsareknown. subject to: MinCost: minimize x,y energy reduction by winning tenants y + γ i N cost of backup energy storage e i x i δ, αy + i N b i x i (1) (1a) x i {0, 1}, i N, (1b) y 0. (1c) where γ i N e ix i is the colocationlevel energy reduction resulting from tenants server energy reduction i N e ix i. 12 EDR target energy reduction cost w a ap la co sa L γ ti ac ti en su
13 Can VCG Auction Help? The underlying problem is NPcomplete; Optimally solving the cost minimization problem is computationally infeasible; NO! VCG auction cannot help in an efficient way! 13
14 TruthDR We propose a reverse auction named TruthDR, Step3: Submit bids... Tenant 1 Tenant 2 Tenant 3 Tenant N Colocation Operator (Auctioneer) Step1: EDR signal Step2: Solicit bids from tenants Step4: Notify tenants winning bids & payments 14
15 TruthDR Properties: truthful in expectation computationally efficient; individually rational 2approximation in colocationwide social cost, compared with the optimal solution. 15
16 Details about TruthDR Design a 2 approximation algorithm a randomized auction framework 16
17 2Approximation Algorithm MiniCost Enhanced LPR 17
18 2Approximation Algorithm MiniCost lower bound of OPT Enhanced LPR LPRDual 18
19 Randomized Auction Framework Algorithm 2 TruthDR: Truthful Randomized Auction 1: Optimal Fractional Solution Solve LPR (2), obtaining optimal BES usage y and optimal fractional winner decisions x. 2: Decomposition into Mixed Integer Solutions Decompose the fractional decisions (min{βx, 1}, βy ) to aconvexcombinationoffeasiblemixedintegersolutions (x l,y l ),l I, of (1) using a convex decomposition technique, using Alg. 1 as the separation oracle in the ellipsoid method to solve the primal/dual decomposition LPs. 3: Winner Determination and Payment Select a mixed integer solution (x l,y l ) from set I randomly, using weights of the solutions in the decomposition as probabilities Calculate the payment of tenant i as f i = { 0 if xi =0 b i + αγei b i min{2x i (b,b i),1}db min{2x19 i (b i,b i ),1} otherwise (4a) is to ens the truthfulne sition of βy and hence (4b We can sol the possible m solve LP (4) there are expo and hence an therefore reso and φ are ass (4c), respectiv min y l
20 Randomized Auction Framework Algorithm 2 TruthDR: Truthful Randomized Auction 1: Optimal Fractional Solution Solve LPR (2), obtaining optimal BES usage y and optimal fractional winner decisions x. 2: Decomposition into Mixed Integer Solutions Decompose the fractional decisions (min{βx, 1}, βy ) to aconvexcombinationoffeasiblemixedintegersolutions (x l,y l ),l I, of (1) using a convex decomposition technique, using Alg. 1 as the separation oracle in the ellipsoid method to solve the primal/dual decomposition LPs. 3: Winner Determination and Payment Select a mixed integer solution (x l,y l ) from set I randomly, using weights of the solutions in the decomposition as probabilities Calculate the payment of tenant i as f i = { 0 if xi =0 b i + αγei b i min{2x i (b,b i),1}db min{2x20 i (b i,b i ),1} otherwise (4a) is to ens the truthfulne sition of βy and hence (4b We can sol the possible m solve LP (4) there are expo and hence an therefore reso and φ are ass (4c), respectiv min y l
21 Randomized Auction Framework Algorithm 2 TruthDR: Truthful Randomized Auction 1: Optimal Fractional Solution Solve LPR (2), obtaining optimal BES usage y and optimal fractional winner decisions x. 2: Decomposition into Mixed Integer Solutions Decompose the fractional decisions (min{βx, 1}, βy ) to aconvexcombinationoffeasiblemixedintegersolutions (x l,y l ),l I, of (1) using a convex decomposition technique, using Alg. 1 as the separation oracle in the ellipsoid method to solve the primal/dual decomposition LPs. 3: Winner Determination and Payment Select a mixed integer solution (x l,y l ) from set I randomly, using weights of the solutions in the decomposition as probabilities Calculate the payment of tenant i as f i = { 0 if xi =0 b i + αγei b i min{2x i (b,b i),1}db min{2x21 i (b i,b i ),1} otherwise (4a) is to ens the truthfulne sition of βy and hence (4b We can sol the possible m solve LP (4) there are expo and hence an therefore reso and φ are ass (4c), respectiv min y l
22 Conclusion This work studied how to enable colocation EDR at the minimum colocationwide cost. To address the challenges of uncoordinated power management and tenants lack of incentives for EDR, we proposed a firstofitskind auction based incentive mechanism, called TruthDR, which is computationally efficient, truthful in expectation and guarantees a 2approximation in colocationwide social cost
23 Thanks! Questions? 23
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