Agile Traffic Merging for Data Center Networks. Qing Yi and Suresh Singh Portland State University, Oregon June 10 th, 2014

Transcription

1 Agle Traffc Mergng for Data Center Networks Qng Y and Suresh Sngh Portland State Unversty, Oregon June 10 th, 2014

2 Agenda Background and motvaton Power optmzaton model Smulated greedy algorthm Traffc mergng approach Smulaton and results Related work Conclusons

3 Energy Proportonalty of DCN Fgure 1. E-commerce webste: 292 producton web servers over 5 days. Traffc vares by day/weekend; power doesn t. [1] Fgure 2. The goal to create an energy-proportonal data center network from non-proportonal. [6]

4 Fat Tree Core swtches Aggregaton swtches Edge swtches Servers Pod 1 Pod 2

5 Optmzng Energy Cost of DCN Mnmum-cost mult-commodty flow problem (MCMCF) Objectve functon: Mnmze Subject to: Capacty constrants Flow conservaton Demand satsfacton Powerlnk Powerport Power swtch

6 Analytcal Power Model For a network G=(V, E) å å å Power total = Power lnk + Power port + Power swtch = e 2 å uîv,wîv u f uw + å k u P port + uîv 2 np swtch and n uv 2 u V y 2, u k u w V u x uw where x uw and y u are bnary varables.

7 Optmzaton Formulaton Objectve functon: Mnmze Subject to: Capacty constrants Flow conservaton Demand satsfacton Lnk and flow correlaton Lnk and swtch correlaton Bdrectonal lnk power Non-splttng flows ( u, v), Power total ",u¹ s, d,, wv s f f uv c ( u, v), x uv x uv, ( u, v), s w f uv uv å f uw - å f wu = 0 wîv u wîv u wv d uv f t wd, ( u, v), f c u uw r t uv x u uv uv u V, wv, x y, x y x, y u wv uw u w u V u x wu wu y u

8 Greedy Flow Assgnment For each traffc flow, fnd a path wth suffcent capacty and lowest cost Cost functon: Cost of a path = cost of nodes + cost of lnks Cost of a node: cost(u) = 0/1 Cost of a lnk: cost(u, v) = 2 Left frst heurstc

9 Valdaton of Greedy Algorthm Optmzaton Implement power optmzaton model for a k=4 fat-tree DCN n CPLEX Apply synthetc traffc flows from 16 servers of the DCN Obtan the total number of actve swtches n and the number of actve ports k u for each swtch u Greedy smulaton Smulate a k=4 fat-tree DCN wth greedy flow assgnment algorthm Apply same synthetc traffc flows from 16 servers of the DCN Compute the number of actve swtches and actve ports for each swtch Results: The routes produced by the greedy flow assgnment algorthm are very close to those produced by the CPLEX optmzaton solver.

10 Valdaton of Greedy Algorthm Results: The routes produced by the greedy flow assgnment algorthm are very close to those produced by the CPLEX optmzaton solver.

11 Our Approach: Traffc Mergng Gven the sgnfcant underutlzaton of lnks and swtches, we propose consoldatng traffc from N lnks to k N lnks Usng an analog hardware devce, a merge network, to merge traffc Low power consumpton Sense packets on lnks Uplnk traffc s consoldated to the leftmost k nterfaces Downlnk routng s mplemented by software Fgure 3. Merge network

12 Applyng Traffc Mergng For a k-ary fat-tree wth all k- port swtches Apply two k/2 k/2 merge networks to each edge swtch and each aggregaton swtch Apply one k k merge network to each core swtch Power off k - m nterfaces of k k swtches Fgure 4. Merge network appled to an edge swtch.

13 Energy Savngs of Traffc Mergng The overall energy cost of a swtch can be parttoned nto: the statc costs of a swtch the cost of the chasss and nterconnecton fabrc the cost of the actve nterfaces Cost of swtch C mlog m m Wth merge networks, traffc load fracton λ s swtched to the leftmost nterfaces, thus Cost of a swtch wth traffc mergng C k log k k

14 Smulaton Smulate a 12-ary fat-tree DCN Support 432 servers. 180 swtches: 72 edge swtches, 72 aggregaton swtches and 36 core swtches. Each lnk has capacty of 1Gbps. Merge networks Each edge and aggregaton swtch has two merge networks connected, one on each sde. each core swtch connects wth one merge network.

15 Offered Traffc Load Synthetc traffc load Offered load fracton for each server vared from 0.1 to 0.7. Generate 8 traffc sutes wth dfferent source/snk patterns from the log-normal process. Random: source and snk node are randomly selected. Strde(): snk = (source + ) mod 432 Staggered(n): assgns probabltes p1,p2 for traffc gong to servers n the same subnet and to servers n the same pod but dfferent subnet, respectvely. Emprcal traffc load Use packet traces of 50 mnutes from a unversty campus data center wth 500 servers.

16 Results: Synthetc Traffc Sutes (I) Fgure 5. Fracton of actve swtches Fgure 6. Number of actve swtches s less than those n ElastcTree

17 Results: Synthetc Traffc Sutes (II) Fgure 7. Reducton n total DCN cost. Fgure 8. Comparson of fracton of cost from traffc mergng and that from ElastcTree.

18 Results: Traffc of Real Data Center Fgure 9. Traffc traces from a unversty data center Fgure 10. Energy savngs when usng traffc mergng vs. ElastcTree

19 Summary Strde(1) and Staggered(1) acheves most energy savngs (around 42% and 30%, respectvely). Traffc mergng can save more energy wth lghter traffc. Traffc mergng acheves better energy proportonalty than ElastcTree. For the traffc flows from real unversty data center, Power consumpton decreases from 30% to 17% when applyng merge networks compared wth ElastcTree.

20 Related Work (I) ElastcTree [1] Formal optmzaton model (does not consder power consumed by dle nterfaces of actve swtches) Greedy bn packng algorthm (usng left-frst heurstcs for the regular structure of herarchcal DCN topology) CARPO (Correlaton-Aware Optmzaton) [2] Power model consders power consumed by chasss, lnecards and ports, and ntegrates dynamc lnk rate Dynamcally consoldate negatve-correlated traffc flows (requre correlaton nformaton)

21 Related Work (II) Path Consoldaton [3 ] Leverages the multple equal-cost paths property of mult-rooted tree topology Consoldates redundant paths between each par of end nodes HERO (Herarchcal power model) [4] Two-level power model for the fat-tree topology. splts the optmzaton problem between network layers Heurstcs: power off swtches n the order of throughput, power consumpton, power effcency, total power consumpton of swtches

22 Contrbutons We revst the problem of energy consumpton n fattree data center networks and propose a greedy algorthm to fnd the mnmum subsets of network elements for gven traffc matrces. By mergng traffc carefully, a subset of unused nterfaces n actve swtches can be powered off and further energy savngs can be acheved n addton to the ElastcTree method.

23 Conclusons The proposed greedy algorthm can fnd near optmal flow assgnment for large-scale data center networks. Our approach of traffc mergng can further reduce energy consumpton of actve swtches. Wth very lght load, traffc mergng can save 20%-40% energy cost compared to ElastcTree. Traffc wth small number of nter-pod and nter-subnet flows can beneft more from traffc mergng.

24 References [1] Brandon Heller, Srn Seetharaman, Prya Mahadevan, Yanns Yakoums, Puneet Sharma, Sujata Banerjee and Nck Mckeown. ElastcTree: Savng Energy n Data Center Networks In Proceedngs of the 7th USENIX conference on Networked systems desgn and mplementaton (NSDI'10). Pages [2] XaodongWang, Yanjun Yan, XaoruWang, Kefa Lu, and Qng Cao CARPO: Correlaton-aware Power Optmzaton n Data Center Networks. In Proceedngs of the 31th Conference on Computer Communcatons (IEEE INFOCOM 2012) [3] Muhhamad Abdullah Adnan and Rajesh Gupta. Path Consoldaton for Dynamc Rght-szng of Data Center Networks. [4] Yan Zhang and Nrwan Ansar HERO: Herarchcal Energy Optmzaton for Data Center Networks. In Proceedngs of IEEE Internatonal Conference on Communcatons (ICC 2012) [5] US EPA Report to Congress on Server and Data Center Energy Ecency, Publc Law U.S.Envronment Protecton Agency, ENERGY STAY Program, by Lawrence Berkeley Natonal Laboratory, August2007. LBNL-363E. [6]

25 Improvng Data Center Energy Effcency

26 Data Center Energy Consumpton Fgure 1. Worldwde electrcty use for data centers n2000, 2005, and [5]