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胡秩瑋

胡秩瑋. Minimizing Electricity Cost: Optimization of Distributed Internet Data Centers in a Multi-Electricity-Market Environment. Overview. INTRODUCTION RELATED WORK FORMULATION AND MODELING SOLUTION METHOD DESIGN

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胡秩瑋

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  1. 胡秩瑋 Minimizing Electricity Cost: Optimization of Distributed Internet Data Centers in a Multi-Electricity-Market Environment

  2. Overview • INTRODUCTION • RELATED WORK • FORMULATION AND MODELING • SOLUTION METHOD DESIGN • ELECTRICITY PRICE AT CERTAIN LOCATIONS FOR GOOGLE INTERNET DATA CENTERS • PERFORMANCE EVALUATION • CONCLUSION AND FUTURE WORK

  3. INTRODUCTION • As the developing of cloud computing, there are more and more Internet Data Centers(IDCs). • IDC demands a lot of power, it means that it costs much money. • The goal of this paper is to minimize the cost of IDCs while guaranteeing quality of service.

  4. INTRODUCTION • Authors achieve the goal with linear programming and polynomial-time method. • The evaluations are based on real-life electricity price data.

  5. FORMULATION AND MODELINGTotal Electricity Cost Modeling for IDC • Two assumption: 1. The power consumption profile is constant for every server. 2. Each server at the same location receives the same traffic rate in steady-state.

  6. FORMULATION AND MODELINGTotal Electricity Cost Modeling for IDC • Thus, the total electricity cost for N data center location is: i:IDC location :the number of turned on servers at i :Spot price of electricity at i:power consumption for one server at i

  7. FORMULATION AND MODELINGWorkload Constraint Modeling • We model the workload constraints in order to capture the requests allocations among all the data center locations for each front-end Web portal. • We have: :the requests demand at each front-end Web portal server j (j=1,…,C) C :total number of front-end Web portal servers :the request arrival rate from front-end Web portal server j to locationi

  8. FORMULATION AND MODELINGDelay Constraint Modeling • At location i with servers, when each server has the service rate and the total arrival rate is , the average delay is given as = • : service rate

  9. FORMULATION AND MODELINGFormulation of Total Electricity Cost Minimization Problem • The objective function(problem one):

  10. SOLUTION METHOD DESIGN • We are going to present a solution method for problem one in this section. • We approximate the problem by linear programming and solve it with polynomial-time algorithm.

  11. SOLUTION METHOD DESIGNMixed Integer Linear Programming Formulation • First of all, we rewrite the problem one as: subject to: • .

  12. SOLUTION METHOD DESIGNMixed Integer Linear Programming Formulation • As must be integer, we can transform Problem1 to Problem2: Subject to , = ,

  13. SOLUTION METHOD DESIGNPolynomial-Time Solution for Approximated Total Electricity Cost Minimization Problem • In this subsection, we show that Problem Two can be converted to a minimum cost flow problem. • we consider a simple case that N =3 and C = 5.

  14. SOLUTION METHOD DESIGNPolynomial-Time Solution for Approximated Total Electricity Cost Minimization Problem Subject to λ11 + λ12 + λ13 = L1, λ21 + λ22 + λ23 = L2, λ31 + λ32 + λ33 = L3, λ41 + λ42 + λ43 = L4, λ51 + λ52 + λ53 = L5, λ11 + λ21 + λ31 + λ41 + λ51 λ12 + λ22 + λ32 + λ42 + λ52 ≤ λ13 + λ23 + λ33 + λ43 + λ53 ≤ Total workload from 5 web servers Maximum workload that all locations can afford

  15. ELECTRICITY PRICE AT CERTAIN LOCATIONS FOR GOOGLE INTERNET DATA CENTERS California : hour-ahead market Texas : 15-min ahead market Atlanta : fixed electricity rate

  16. ELECTRICITY PRICE AT CERTAIN LOCATIONS FOR GOOGLE INTERNET DATA CENTERS

  17. PERFORMANCE EVALUATION

  18. PERFORMANCE EVALUATION

  19. PERFORMANCE EVALUATION

  20. PERFORMANCE EVALUATION Average electricity cost of request processing: : Average electricity cost of upper limitation number of servers: :

  21. CONCLUSION AND FUTURE WORK

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