Shannon revisited: New separation principles for wireless multimedia

Transcription

1 Shannon revsed: New separaon prncples for wreless mulmeda Prof. Mhaela van der Schaar Mulmeda Communcaons and Sysems Lab Elecrcal Engneerng Deparmen, UCLA hp://medanelab.ee.ucla.edu/

2 UCLA Mulmeda communcaons Lab Delay-crcal sysems and neworks Mulmeda compresson & proc Rgorous cross-layer desgn Msson-crcal neworks & sysems Energy-effcen mulmeda sys Real-me sream mnng Desgns for neworked communes New classes of games & learnng Nework economcs Polcng n neworks and sysems Desgn and ncenves n Socal Nes

3 Delay-crcal neworkng and processng Mulmeda compresson, processng and neworkng MPEG, Phlps Delay-crcal Neworkng and Onlne Learnng Rgorous mehods for cross-layer desgn (dynamc envronmens) NSF, Inel, HP, Mcrosof Real-me Sream Mnng D D p, 1 p g p, p D F g NSF, ONR, Inel, Csco D F p, 1 p 1 p, p D F IBM, NSF g

4 Goal: Desgnng Large-Scale Mulmeda Mnng Applcaons n Dsrbued Processng Envronmens [NSF, IBM] D D p, 1 p g p, p D F g Challenges: Hgh Volume of daa: faser han a daabase can handle Complex Analycs: correlaon from mulple sources and/or sgnals; vdeo, audo or oher non-relaonal daa ypes Delay-crcal: responses requred n a specfed me Oher sysem requremens: Scalable o he number of flows Resource varably Falure Tolerance Daa canno be sored and reprocessed Requremens on graceful degradaon under falure Dsrbued compuaon by self-neresed agens D F p, 1 p 1 p, p D F g

5 Sream Compung: New Paradgm Tradonal Compung Sream Compung Hsorcal fac fndng wh daa-a-res Bach paradgm, pull model Query-drven: subms queres o sac daa Reles on Daabases, Daa Warehouses Real me analyss of daa-n-moon Sreamng daa Sream of srucured or unsrucured daa-n-moon Sream Compung Analyc operaons on sreamng daa n real-me Queres Daa Resuls Daa Queres Resuls

6 Sream mnng - Semanc concep deecon Smarer ces Taxonomy Urban Baggng Models Aeral Recon. Images Inpu Sream Parade Flag-burnng Gaherng Road Convoy Demonsraon Roadsde Bo mb Proes Unknown Resource-Adapve Analyc Pla cemen, Opmzaon Scenes and Acves Inellgence Analyss Sreams Mddleware Operang Sysem and Transpor Hardware Confguraon Ground Recon. Images Node Node Node Node Node Dsrbued, Real-me Sream Processng Road Convoy Proes

7 Sream mnng - Onlne Healhcare Monorng PROACTIVE OUTBREAK DETECTION REALTIME HEALTH CENSUS Census, CDC MONITOR INDIVIDUALS MONITORING SERVICES Bomerc Sensor Daa + Conexual Daa Sources MONITOR INDIVIDUALS TRENDING ANALYSIS CLINICAL DECISION Clncal, Insurance MONITOR INDIVIDUALS WELLNESS SERVICES THIRD PARTY CONSULTING SELF MANAGEMENT Wellness, Czen Dsrbued, Real-me Sream Processng

8 Sream mnng- Analyss for socal neworks Graph = nodes ( = people, e.g. bloggers) + lnks (= neracons) Each node ncludes a emporal sequence of documens (blog poss, wees, ) 2a. Idenfy key nfluencers Now: page rank, SNA measures, 2b. Characerze vral poenal Now: use of follower sascs INFLUENCE 1. Idenfy relevan conen Now: keyword search 3. Characerze objecve vs subjecve conen Now: lexcal and paern-based models RELEVANCE 4a. Topc evoluon & emergence Now: word co-occurrence, cluserng Dsrbued, Real-me Sream Processng Dsrbued, Real-me Sream Proc essng TOPIC IDENTIFICATION AND CLASSIFICATION SUBJECTIVITY 4b. Classfy new parally-observed documens Now: unsupervsed cluserng

9 Mul-dscplnary research effor Parallel and Grd Compung Hgh volume daa sream processng Conen-level roung, Topology formaon and Even messagng Sgnal Processng Real-me adapve analycs Sream daa aggregaon, flerng, compresson, processng Incremenal learnng Cross-layer desgn Sysem and Analycs Dsrbued sysem desgns for auonomous and selfneresed agens

10 Informaon processng and economcs New Classes of Engneerng Games Nework economcs NSF Polcng n neworks (Inervenon) Desgn and Incenves n Neworked Communes NSF, IBM

11 Shannon revsed: New separaon prncples for wreless mulmeda F. Fu and M. van der Schaar, "A Sysemac Framework for Dynamcally Opmzng Mul-User Vdeo Transmsson," IEEE J. Sel. Areas Commun., vol. 28, no. 3, pp , Apr F. Fu and M. van der Schaar, "Decomposon Prncples and Onlne Learnng n Cross-Layer Opmzaon for Delay-Sensve Applcaons", IEEE Trans. Sgnal Process., vol 58, no. 3, pp , Feb F. Fu and M. van der Schaar, "Srucure-Aware Sochasc Conrol for Transmsson Schedulng". F. Fu and M. van der Schaar, "Srucural Soluons o Dynamc Schedulng for Mulmeda Transmsson n Unknown Wreless Envronmens".

12 Our research focus To develop a rgorous mahemacal framework ha enables us o analyze and desgn mul-user envronmens and applcaons, where auonomous users nerfere wh each oher when sharng a common se of resources. Am: consruc a new heory for archecng nexgeneraon dsrbued neworks and sysems under nformaonal and/or delay-consrans. [NSF Career, 2004] 12

13 Neworked mulmeda apps are boomng! Conen Server PDA SDTV Brdge Inerne Inerne Access Access Pon RG DVD Recorder HDTV Wreless vdeo Vdeo conference In-home sreamng P2P neworks Exsng nework envronmens provde lmed suppor for delaysensve applcaons My research has been dedcaed n he pas 14 years o enablng effcen real-me mulmeda communcaon Wha s new? Developmen of a rgorous, unfed framework for he opmal desgn and deploymen of delay-sensve mulmeda communcaon 13

14 Challenges Orgnal VIDEO Source Vdeo Encodng Traffc Transmer Transmsson Sraegy Packe-based Nework Vdeo Decodng Recevng Sraegy Receved VIDEO Recever Challenge 1: Unknown, dynamc envronmens Dynamc source and channel condons Sascal knowledge of dynamcs - unknown 14

15 Challenges Orgnal VIDEO Source Vdeo Encodng Traffc Transmer Transmsson Sraegy Packe-based Nework Vdeo Decodng Recevng Sraegy Receved VIDEO Recever Challenge 2: Mulmeda raffc s hghly heerogeneous Dfferen delay deadlnes, mporance, and dependences 15

16 Challenges Orgnal VIDEO Source Vdeo Encodng Traffc Transmer Transmsson Sraegy Packe-based Nework Vdeo Decodng Recevng Sraegy Receved VIDEO Recever Challenge 3: Mul-user couplng/nerference (dynamc!) 16

17 Problem 1: Mnmze average delay for homogeneous raffc n dynamc neworks Transmer Recever Exsng soluons Nework conrol heory Sably-consraned opmzaon for sngle-user ransmsson [Tassulas 1992,2006, Neely 2006, Kumar 1995, Solyar 2003] Queue s sable, bu delay performance s subopmal (for low delay apps) Nework envronmen s consdered known no rue n pracce! Exsng soluons Sochasc conrol heory Markov decson process (MDP) formulaon [Berry 2002, Borkar 2007, Krshnamurhy 2006] Sasc knowledge of he underlyng dynamcs s requred Onlne learnng [Krshnamurhy 2007, Borkar 2008] Slow convergence and large memory requremen 17

18 Problem 2: Maxmze qualy of delay-sensve applcaons wh heerogeneous raffc Transmer Recever Exsng soluons - Mulmeda communcaon and neworkng Jon source and channel codng, schedulng, prorzaon ec. Cross-layer opmzaon [van der Schaar 2001, 2003, 2005, Kasaggelos 2002] Rae dsoron opmzaon (RaDO) [Chou, 2001, Frossard 2006, Grod 2006, Orega 2009] Lmaons Myopc opmzaon Only explores heerogeney of he meda daa, bu do no consder nework dynamcs and resource consrans Only known envronmens consdered Lnear ransmsson cos (e.g. no suable for energy-opmzaon) No sysemac soluons avalable - problem seemed oo hard? 18

19 Problem 3: Mul-user ransmsson sharng of nework resources Transmer Recever Transmer Recever Exsng soluons - Nework opmzaon heory Nework uly maxmzaon (NUM) [Chang 2007, Kasaggelos 2008] Uses sac uly funcon whou consderng he nework dynamcs No delay guaranees No learnng ably n unknown envronmens Exsng soluons Nework conrol heory Sably-consraned opmzaon for mul-user ransmsson [Tassulas 1992, 2006, Neely 2006, 2007, Kumar 1995, Solyar 2003] Queue s sable, bu delay performance s subopmal (for low-delay apps) Does no consder heerogeneous meda daa 19

20 Wha can we learn from Shannon abou how o address hese challenges? Shannon s separaon prncple: - Source code wh mnmal rae o sasfy desred source dsoron - Channel code wh mnmal rae o reduce channel errors Despe separae desgn of source and channel codecs, opmaly s acheved! 20

21 Separaon prncples Shannon s separaon prncple s no useful for delay-crcal communcaon desgns, because s vald only for: Saonary source and channel Unlmed delay (arbrary long block codes) Pon-o-pon communcaon sysems However, he dea of separaon prncples wll become useful f we can separae a he rgh places! Thus: new separaon prncples are needed! Ths work: - develops desgns ha separae no he rgh sub-problems - effcenly solves he separaed sub-problems 21

22 Foresghed opmzaon: a unfed framework Challenges Dynamc sysem Unknown dynamcs Learnng effcency Heerogeney Mul-user couplng Soluons Sochasc conrol Onlne learnng Separaon prncples 22

23 Key resuls Energy-effcen daa ransmsson* Prevous sae-of-ar mehods Sably consraned opmzaon [Neely 2006] Our mprovemens Reduce he delay by 70% (n low delay regon) Wreless vdeo ransmsson Mul-user vdeo ransmsson Rae-dsoron opmzaon [Chou 2001] Nework uly maxmzaon [Chang 2007] Improve up o 5dB n vdeo qualy Improve up o 3dB n vdeo qualy *mnmze he average delay 23

24 Roadmap Separaon prncple 1: separae he foresghed decson problem from he compuaon of unknown dynamcs Separaon prncple 2: separae he foresghed decson problems across daa packes Separaon prncple 3: separae he foresghed decson problems across users 24

25 Energy-effcen daa ransmsson a x y h Transmer Recever Pon-o-pon me-sloed communcaon sysem Sysem varables Backlog (queue lengh): h Channel sae: Fne sae Markov chan Daa arrval process: x a :..d. Decson a each me slo Amoun of daa o ransm (ransmsson rae): y ; 0 y x Energy consumpon: ½(h ; y ), convex n y, e.g. ½ (h ; y ) = ¾ 2 (2 y 1). h Wha Assumpon s he opmal 1: (queueng), delay vs. energy rade-off? u x y s bounded, supermodular and jonly concave n xy, ; u(x ; h ; y ) = (x y + ½(h ; y )). Assumpon 2: c½ h, y s ncreasng and convex n y for any gven h. 25

26 Foresghed opmzaon formulaon Why we need foresghed opmzaon? Markov Decson Processes (MDP) Onlne learnng Curren uly max y f u(s; y) + Sae-value funcon V (f(s; y; w)) E w } Queue lengh Channel condon Heerogeney Sae: s Acon: y Sae: s 0 = f(s; y; w) Curren me slo Nex me slo Dynamcs: w 26

27 Foresghed opmzaon formulaon Foresghed opmzaon (MDP) formulaon Sae: (x ; h ) Acon: y Polcy: ¼: (x ;h )! y Uly funcon: u(x ; h ; y ) = (x y + ½(h ; y )). Objecve (opmze delay and energy consumpon radeoff) 1X V (x ; h ) = max E (k ) fu(x k ; h k ; ¼(x k ; h k ))g ¼ k= 2 [0; 1) s dscoun facor. If dynamcs are known soluon for Bellman s equaons s known Polcy eraon, value eraon 27

28 Challenges for solvng he foresghed opmzaon Bellman s equaon: V (x; h) = maxfu(x; h; ¼(x; h)) + E a;h 0j h V (x ¼(x; h) + a; h 0 )g ¼ Sascal knowledge of he underlyng dynamcs - unknown Unknown raffc characerscs Unknown channel (nework) dynamcs Couplng beween he maxmzaon and expecaon 28

29 Explo naure of foresghed decson - Separaon Prncple 1 Normal sae (x ; h ) (x (x +1 ; h +1 ) y ; h ) V (x ; h ) U(~x ; h ) V (x +1 ; h +1 ) Sae-value funcon Pos-decson sae Exogenous dynamcs Decson y a, h +1 Pos-decson sae-value funcon Normal sae Sae-value funcon Foresghed decson V (x; h) = maxfu(x; h; y) + U(x y; h)g y Expecaon over dynamcs U(x; h) = E a;h 0j h V (x + a; h 0 ) Pos-decson sae separaes foresghed decson from dynamcs. Expecaon over dynamcs Foresghed decson 29

30 Onlne learnng usng Separaon Prncple 1 U(x; h) = E a;h 0j h V (x + a; h 0 ) V (x; h) = max fu(x; h; y) + U(x y; h) g y Onlne adapaon U (x; h 1 ) = (1 )U 1 (x; h 1 ) + V (x; h ) Onlne updae Tme-average e.g. = 1= Foresghed decson V (x; h ) = maxfu(x; h y2y ; y) + U 1 (x y; h )g Theorem: Onlne adapaon converges o he opmal soluon when! 1 Expecaon s ndependen of backlog x bach updae (fas convergence). Bach updae ncurs hgh complexy. Curse of dmensonaly 30

31 Srucural properes of opmal soluon U(x; h) = E a;h 0j h V (x + a; h 0 ) V (x; h) = maxfu(x; h; y) + U(x y; h)g y Srucural properes of opmal soluon Assumpon: u(x; h; y) s jonly concave and supermodular* n (x; y). Expecaon over dynamcs ¼(x; h) s monoonc n x U(x; h) s concave n x Foresghed decson How can we ulze hese srucural properes n onlne learnng? * u(x 0 ; h; y 0 ) u(x 0 ; h; y) u(x; h; y 0 ) u(x; h; y) f x 0 x; y 0 y 31

32 Adapve approxmaons How o compacly represen pos-decson sae-value funcon and monoonc polcy? U(x; h) Adapve approxmaon operaor ( A ) (Pece-wse lnear) 1 x 1 x 3 x 2 x 4 x For each channel sae h, we approxmae he pos-decson sae-value funcon such ha he wors-case performance degradaon s bounded and performance-complexy radeoffs can be easly performed. 32

33 Onlne learnng wh adapve approxmaon ^U (x; h 1 ) = A f(1 ) ^U 1 (x; h 1 ) + V (x; h )g ¼(x; h ) Onlne updae ^U 1 (x; (x; h h) 1 ) Foresghed decson V (x; h ) = maxfu(x; h y2y ; y) + ^U 1 (x y; h )g Theorem: Onlne learnng wh adapve approxmaon converges o an -opmal soluon, where Varan: Updae U(x; h) and ¼(x; h) every T me slos 1 33

34 Performance of learnng wh approxmaon 34

35 Comparson wh sably-consraned opmzaon Sably-consraned opmzaon [Tassulas 1992, 2006, Neely 2006, 2007] Objecve - rade-off beween Lyapunov drf and energy consumpon max y 2 mn ½(h ; y ) + (x y ) 2 x 2 y Lyapunov drf (x y + ½(h ; y )) + x y (x y )2 + x 2 Uly funcon Pos-decson sae value funcon u(x ; h ; y ) U(x y ; h ) Does no consder he channel sae ranson and he ransmsson cos Does no consder he effec of he uly funcon on pos-decson sae value funcon Only ensures queue sably, bu resuls n poor delay performance 35

36 Comparson wh sably-consraned opmzaon Sably consraned opmzaon Mnmze Lyapunov drf Mnmze delay Our proposed soluon Mnmze queue sze = Mnmze delay 36

37 Comparson o Q-learnng Q-learnng: learn drecly sae-value funcon (no separaon appled) Onlne learnng based on Separaon Prncple 1 37

38 Roadmap Separaon prncple 1 separaon of foresghed decson and compuaon of unknown dynamcs Separaon prncple 2 separaon of foresghed decson across daa packes for heerogeneous mulmeda Separaon prncple 3 separaon of foresghed decson across users for mul-user communcaon 38

39 Heerogeneous meda daa Meda daa represenaon: Daa Un (DU) - Each DU has he followng arbues: Arrval me: me a whch he DU s ready for processng: Delay deadlne: Sze : L d q Dsoron mpac: per packe Inerdependency beween DUs: Augmened Dreced Acyclc Graph (A-DAG) Uly funcon: vdeo qualy (PSNR) vs. energy radeoff 39

40 Conex DU 1 DU 2 DU 3 DU 4 DU 5 DU 1 DU 2 DU c Conex ( ) a each me slo Include he DUs whose deadlnes are whn a me wndow W e.g. W = 3 DU 2 DU 2 DU 4 DU 4 DU 4 DU 1 DU 3 DU 3 DU 5 DU 5 DU 5 DU 1 c 1 c 2 c 3 c 4 40

41 Foresghed opmzaon DU 2 DU 3 DU 4 DU 5 DU 4 DU 5 Sae: (c ; x ; h ) c x = (x2 ; x3 ; x4 ; x5 ) c +1 Mul-DU Foresghed decson max fu(c ; x ; h ; y ) + U(c ; x y ; h )g y;2c where Curren uly u(c ; x ; h ; y ) = Pos-decson sae-value funcon X X qy + ½(h ; 2 c - - Whch DU should be ransmed frs? How much daa should be ransmed for each DU? 2 c y ) 41

42 Prory-based schedulng Prorzaon Based on dsoron mpacs, delay deadlnes and dependences DU 2 DU 4 DU 2 DU 4 DU 3 DU 5 DU 3 DU 5 c Prory graph 42

43 Separaon prncple 2: Separae foresghed decson across DUs Theorems If here s only one DU wh he hghes prory, s opmal o ransm he daa n hs DU by solvng he foresghed opmzaon. If here are mulple DUs wh he same prores, s opmal o frs solve he foresghed opmzaon for each DU and ransm he daa from he DU wh hghes long-erm uly. Sngle-DU foresghed decson: X V = max f~u (x y 2Y ; h ; (h ) j C DU 2 DU 3 DU 4 DU 5 jc : DU j has hgher prory han DU. y j ; y ) + U (c ; x y ; h )g One dmensonal concave funcon gven c and h. I can be updaed usng he proposed onlne learnng. Mul-DU foresghed decson Mulple sngle-du foresghed decson 43

44 Separaon prncple 2: Separae foresghed decson across DUs DU 2 DU 4 DU 1 DU 3 DU 5 conex Mul-DU foresghed decson Mulple sngle-du foresghed decson 44

45 Smulaon resuls for vdeo ransmsson Dynamcs of boh source and channel consdered (separaon prncple 2) Dynamcs of only source consdered No dynamcs consdered (myopc) Foreman 45

46 Roadmap Separaon prncple 1 separaon of foresghed decson and compuaon of unknown dynamcs Separaon prncple 2 separaon of foresghed decson across daa packes for heerogeneous mulmeda Separaon prncple 3 separaon of foresghed decson across users for mul-user communcaon 46

47 Delay-sensve mul-access communcaon Goal: maxmze sum of long-erm ules across all users Resource consran (e.g. ransmsson me consran n TDMA) 47

48 Foresghed opmzaon formulaon User x, h x 1, h 1 Nework coordnaor Resource allocaon Resource allocaon Resource allocaon User x, h Tme slo Curren allocaon x 1, h 1 Fuure allocaon Resource requremen nformaon Resource allocaon Formulae as Mul-user MDP (MUMDP) and perform foresghed decson Depends on all users sae X V (x ; h ) = max f M u (x ; h; y) + U(x y ; h )g y 2 (h ) =1 Goal: decouple pos-decson sae value funcon across users 48

49 Separaon Prncple 3: Decomposon of pos-decson sae-value funcon User x, h x 1, h 1 Nework coordnaor Resource allocaon Resource allocaon Resource allocaon User, Curren allocaon x h Fuure allocaon Relax he resource consrans (e.g. TDMA-lke access) 1; 8 k = + 1; P 1 k k=+1 P M =1 y k R(h k ) P M =1 y k R(h k ) 1 1 Inroduce scalar resource prce, and compue pos-decson sae-value funcon U (x; h) ndvdually, based on sngle-user MDP: U (x; h) = Upper bound max fu (x; h; y) y=r(h) + U (x y; h)g 0 y x Access me 49

50 Separaon Prncple 3: Decomposon of pos-decson sae-value funcon User User x, Resource allocaon, Curren allocaon x h h 1, h 1 Fuure allocaon Relax he resource consrans (e.g. TDMA-lke access) 1; 8 k = + 1; P 1 k k=+1 P M =1 Nework coordnaor y k R(h k ) x x 1, h 1 Resource prce Resource prce P M =1 y k R(h k ) 1 1 Inroduce scalar resource prce, and compue pos-decson sae-value funcon U (x; h) ndvdually, based on sngle-user MDP: U (x; h) = Upper bound max fu (x; h; y) y=r(h) + U (x y; h)g 0 y x Access me 50

51 Mul-user resource allocaon Resource allocaon MX max fu (x ; h; y) + U (x y; h)g y 2 (h ) =1 Sub-graden mehod o updae resource prce Sub-graden User Nework coordnaor User x, h x 1, h 1 Resource allocaon x, h x 1, h 1 Curren allocaon Resource prce Resource prce Fuure allocaon 51

52 Resource prce updae Subgraden mehod o updae resource prce The resource prce s updaed by X k+1 = [ k + k( M Z 1 1 )]+ subgraden Z =1 where s he expeced consumed resource by user and s ndvdually compued by user. 52

53 Relaonshp of our mul-user framework o exsng mehods Our framework Longes Queue Hghes Possble Rae (LQPHR) - Assgn hgher rae o longer queue o acheve opmal average delay Assume symmerc..d. channels 53

54 Resuls for mul-user ransmsson Each user uses mulple queues o represen vdeo daa Markov chan model for Raylegh fadng channel TDMA-ype channel access 54

55 Oher applcaons developed n our lab based on he same framework Cross-layer opmzaon va layer separaon Each layer performs dynamc opmzaon ndvdually Message exchange across layers Meda-TCP: Congeson conrol for real-me mulmeda ransmsson Energy-effcen mulmeda processng Wreless vdeo over cooperave, mul-hop and msson-crcal neworks Dsrbued, real-me mulmeda daa mnng applcaons Scalable and reconfgurable vdeo codng va layer separaon Thank you Claude! 55

56 Our research webse: hp://medanelab.ee.ucla.edu 56