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Energy-Delay Tradeoffs of Base Stations in Cloud-Based...
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Energy-Delay Tradeoffs of Base Stations in Cloud-Based Cellular Networks Tao Zhao, Jian Wu, Sheng Zhou, and Zhisheng Niu Network Integration for Ubiquitous Linkage and Broadband(NiuLab) Tsinghua University, Beijing, China 1
Transcript of Energy-Delay Tradeoffs of Base Stations in Cloud-Based...
Energy-Delay Tradeoffs of Base Stationsin Cloud-Based Cellular Networks
Tao Zhao, Jian Wu, Sheng Zhou, and Zhisheng Niu
Network Integration for Ubiquitous Linkage and Broadband(NiuLab)Tsinghua University, Beijing, China
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Outline
1 Background
2 Energy-Delay TradeoffSystem ModelOptimization Problem and SolutionNumerical Results
3 Summary
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5G Challenges
Requirements & ChallengesMassive data: 1000× capacity → EfficiencyMassive connections: U-shape traffic → FlexibilityInnovative applications: IT → operators → Sustainability
Conventional cellular architecturesPeak load based provision → resource wasteDistributed BS decision → hard to coordinate or sleepTightly coupled software & hardware → hard to upgradeand deploy new services
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Cloud-Based Cellular Architectures [1]–[3]CONvergence of Cloud and cEllulaR sysTems
Centralized cloud computing platformSoftware defined servicesOpen IT technologies
RIE
Conductor
Regionalservers
Centralservers
Software-definedswitches
Control links
Local server
Data plane
Control
plane
VM
LTE vBSLTE vBSLTE vBS
VM
3G vBS3G vBS3G vBS
VM
3G vBS3G vBSManagement
VM
3G vBS3G vBSReal-timecloud app
[1] Y. Lin, L. Shao, Z. Zhu, et al., “Wireless network cloud: architecture and system requirements,” IBM Journalof Research and Development, vol. 54, no. 1, 4:1–4:12, 2010.[2] China Mobile Research Institute, “C-RAN: the road towards green RAN,” , White Paper, Dec. 2013, Version 3.0.[3] J. Liu, T. Zhao, S. Zhou, et al., “CONCERT: a cloud-based architecture for next-generation cellular systems,”IEEE Wireless Commun. Mag., 2014, To be published. eprint: arXiv:1410.0113.
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Virtual Base Stations
Virtual BS (VBS)BS function: software in virtual machineBBU (base band unit): commodity serversRRH (remote radio head): universal hardware
Focus: energy-delay tradeoffs of BSsTotal power consumption: BBU and RRHQueueing delayLoad aware resource adaptation
MotivationWhat does the energy-delay tradeoff relationship look likefor VBS?How is the energy performance compared with conventionalBS under delay constraints?
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Outline
1 Background
2 Energy-Delay TradeoffSystem ModelOptimization Problem and SolutionNumerical Results
3 Summary
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Power Consumption Model
Computational resource aware modelRRH:
PR =Poutη
+ PRF (1)
BBU: Nc CPU cores, CPU speed s, CPU load ρc
PB = Nc(PBm +∆PBρcsβ) (2)∆PB = (PBM − PBm)/sβ0 (3)
ρc =f(r)Ncs =
c0 + κrNcs (4)
BS:
P =
PB + PR, 0 < Pout ≤ Pmax
Psleep, Pout = 0(5)
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Queueing Model
BBU poolOne VBS on a server with Nc active CPU cores with speed s
Queueing model: M/G/1 Processor Sharing (PS)Flow arrival at BS: rate λ, average file size LData transmission rate r bps (r0 = 0; rn = r,n > 0)Traffic load: ρ = λL/rAverage queue length: En = ρ
1−ρ = λLr−λL
Average delay: ED = En/λBase station sleeping
Cycle: Tc = Ta + TsSwitching cost: EswAverage power consumption:
EP = pactive(PB + PR) + psleepPsleep +2EswETc
(6)
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Cell Coverage Model
3GPP propagation modelPath loss L(d)Noise factor FSystem bandwidth WDownlink SINR
SINR(d) = gPout =Pout
L(d)FN0W (7)
Data rateUser: assumed at the cell edgeSum data rate at BS
r = W log2(1 + gPout) (8)
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Outline
1 Background
2 Energy-Delay TradeoffSystem ModelOptimization Problem and SolutionNumerical Results
3 Summary
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Problem Formulation
Objective: system costsPower consumptionAverage queue lengthWeighting factor
Decision variables:Data transmission rateNumber of CPU cores
Optimization Problem
minr,Nc
z = EP+ αEn (9)
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Solving the Problem
Optimal rateOptimal rate satisfies:
Ω
(αgη
e
(r∗
r∗ − λL
)2
+gηPs − 1
e
)=
r∗ ln 2
W − 1 (10)
where Ω(·) is the principal branch of Lambert W function,
Ps = Po − Psleep − 2λEsw, (11)Po = NcPBm +∆PBc0sβ−1 + PRF, (12)
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Energy-Delay TradeoffProposition
There exists the unique energy optimal rate r∗e when:
λ <Po − Psleep
2Esw, (13)
L <W
λ ln 2
[Ω
(gηPs − 1
e
)+ 1
](14)
The corresponding energy optimal rate is:
r∗e =Wln 2
[Ω
(gηPs − 1
e
)+ 1
]. (15)
Otherwise, the average power consumption is monotonicallydecreasing with the average delay.In both cases, when the average delay goes to infinity, theaverage power consumption approachesPo + κ∆PBsβ−1λL + 2
λLW −1gη .
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Impact of Computational Resources
Impact of NcMax raterM(Nc) =
Ncs−c0
κMonotonicity:∂z∂Nc
= PBm > 0,∂r∗∂Nc
> 0,
Joint Optimization Algorithm1: Set NcM, Nc ← 1, S← Φ2: while Nc ≤ NcM do3: r(Nc)← argminr z(r,Nc)4: if r(Nc) ≤ rM(Nc) then5: S← S ∪ (r(Nc),Nc)6: Break out of the loop7: else8: S← S ∪ (rM(Nc),Nc)9: Nc ← Nc + 1
10: end if11: end while12: return
(r∗,N∗c) = argmin(r,Nc)∈S z(r,Nc)
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Outline
1 Background
2 Energy-Delay TradeoffSystem ModelOptimization Problem and SolutionNumerical Results
3 Summary
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Numerical Results
ParametersBBU: commodity serversRadio: LTE R11
ComparisonConventional BS (CBS):EARTH model
10−1
100
101
0
20
40
60
80
100
120
140
Average delay (s)
Avera
ge p
ow
er
(W)
λ=2 s−1
, VBS
λ=2 s−1
, CBS
λ=1 s−1
, VBS
λ=1 s−1
, CBS
(0.26,25.77)
64%
(0.26,72.09)
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Numerical Results
Energy-delay tradeoffs
10−1
100
101
20
25
30
35
40
45
50
55
60
65
Average delay (s)
Avera
ge p
ow
er
(W)
Nc=1
Nc=2
Nc=3
Nc=4
r=rM
(Nc)
10−1
100
101
0
10
20
30
40
50
60
70
80
Average delay (s)
Avera
ge p
ow
er
(W)
λ=2 s−1
, L=2 MB
λ=2 s−1
, L=1 MB
λ=1 s−1
, L=2 MB
λ=1 s−1
, L=1 MB
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Summary
Proposed a computational-resource-aware energyconsumption model for VBSs in cloud-based cellularnetworks.Derived the explicit form of the optimal data rate, andobserved the opportunity to achieve energy savings andreduce the average delay simultaneously.Proposed an efficient algorithm to jointly optimize thedata rate and the number of CPU cores.
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