VIDEO TRANSCODE SERVICE PLATFORM WITH WORKLOAD PREDICTION

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1 5 th December 0. Vol. 46 No JATIT & LLS. All rghts reserved. ISSN: E-ISSN: VIDEO TRANSCODE SERVICE PLATFORM WITH WORKLOAD PREDICTION GAODE QIN, GAOJIN WEN Shezhe Polytechc, Shezhe 58055, Guagdog, Cha Natoal Laboratory of Patter Recogto, Isttute of Automato of the Chese Academy of Sceces, 95 Zhogguacu East Road, 0090, Beg, Cha ABSTRACT Wth the rapd expadg of the tegrato of telecommucatos etworks, cable TV etworks ad the teret, the effcet ad eergy-savg vdeo trascode servce platform s great demad. Such a platform together wth workload predcto s proposed ths paper. A ew accurate mult-scale predcto method s used to predct the executo tme of trascode tasks ths platform. Specal dstrbuto ad maagemet scheme s appled to serve the purpose of eergy optmzato. Wth several expermets, t s proved that the proposed platform ca be used to predct the workload of the trascode tasks accurately ad save the eergy effcetly by task schedulg optmzato. Keywords: Mult-scale Predcto, Vdeo Trascode, Task Schedulg. INTRODUCTION Trascode hgh-bt-rate vdeo stream to low bt rate vdeo stream, by the demad of equpmet ad software dfferet etwork badwdth ad play termal s oe of the most mportat applcatos of vdeo trascodg [,,3,4,5]. Therefore, the Harley 009 troduced workflow-based dstrbuted trascodg system Carbo Server, the system uses a workflow ad hgh-speed vdeo trascodg servces. I 00, Mcrosoft released a vdeo trascodg ege (Trasform Maager Trascode Ege) based o the cluster system, the system software has good scalablty, support prorty task schedulg ad fault tolerace mechasm. By far, vdeo trascodg systems do ot predct the task workload of vdeo trascodg order to mprove trascodg performace. Thus, ths paper proposes a vdeo trascodg servce platform system based o workload predcto, whch uses a ew type of mult-scale predcto method to estmate the executo tme of trascodg tasks, ad also perform optmzato of trascodg task schedulg optmzato based o the predcted tme. Uder the premse of the ormal operato, the system ams at optmze the eergy cosumpto, allocates ad maages data ceter odes to maxmze the effcecy of the odes.. ARCHITECTURE OF VIDEO TRANSCODING SERVICE PLATFORM SYSTEM Vdeo trascodg servce platform system cossts of oe maagemet ode ad may compute odes sharg storage. System software rug o the maagemet ode s resposble for the etre platform system maagemet ad schedulg, whle vdeo trascodg program o compute odes s resposble for the executo of the trascodg task. Vdeo trascodg servce platform system software cossts of the followg seve modules llustrated Fgure : ) vdeo data maagemet module, s maly resposble for the vdeo data mport, export, backup ad delete operatos; ) task plag modules geerate vdeo trascodg batch task prmarly accordg to the user's requremets (vdeo data, processg tme, format trascodg parameters); 3) log module records trascodg task executo tme ad ode status formato; 4) workload predcto module, s maly resposble for geeratg task workload predcto model. I the ext secto, we wll dscuss the mult-scale workload predcto algorthm detals; 63

2 5 th December 0. Vol. 46 No JATIT & LLS. All rghts reserved. ISSN: E-ISSN: ) task schedulg optmzato s maly resposble for makg task schedulg program based o the task workload of predcto model ad the status of odes; 6) ode motorg module motors the realtme performace of odes; 7) task schedulg executo module s maly resposble for schedulg tasks accordg to the real-tme status of the odes whch cludes ode wake-up ad sleep operato ad the task mgrato of fault ode. Task Plag Task Schedulg Optmzato Workload Predcto Task Schedulg Executo Log Vdeo Data Maagemet Node Motorg Fgure : Archtecture of vdeo trascodg servce platform system 3. MULTI-SCALE WORKLOAD PREDICTION ALGORITHM After performg oe vdeo trascodg task, you ca get a hstorcal log data vector x of whch the compoet are ma parameters whch affect the vdeo trascodg tme: put vdeo ecodg format, output vdeo codg format, put vdeo resoluto, output vdeo resoluto, vdeo legth, audo format, frame rate, the executo tme, etc. Accordg to the dfferet put ad output vdeo formats, we ca classfy the hstorcal log data. Therefore, we ca predct the task executo tme of each class of tasks. Algorthm : mult-scale workload predcto algorthm x Iput: hstorcal log data of task { } =, the maxmum matchg scale layer MaxLevel = 3 or 4, the predcto error accuracy ε = 0e 6 Output: sequece Ω of mult-scale predcto model Step : Set curret matchg scale CurLevel=, Ω = θ Step : Aalyze usg a varety of tme-seres regresso aalyss method of whch the procedure s as follows: () Use four models for workload predcto of { x } =, geeral autoregressve model AR ( p ), the movg average model MA (q), autoregressve movg average model ARMA ( p, q) ad autoregressve movg average model ARIMA ( p, q). Let { y } be the predctve value of each model where =,,3, 4 represets the model; () Calculate the absolute error of predcted results from the models (). The formula for the -th model s E = = y x ; (3) Select the model of whch relatve error s mmum to be the fal predctve model the curret scale. Suppose the k-th model bears mmum relatve error. Add t to the sequece Ω Ω = Ω k ). (Set { } Step 3: Update the put data of curret tme k sequece, x = x y, where =,...,. Compute the error of curret matchg ω = x = Step 4: IF ( ω < ε ) THEN goto Step 6; ELSE goto Step 5 Step 5: IF (CurLevel < MaxLevel) THEN goto Step ; ELSE CurLevel = CurLevel + ad goto Step 6 Step 6: Output Ω 4. TASK SCHEDULING OPTIMIZATION If we ca get relatve accurate predcto of the task workload, the uder the premse of the vdeo trascodg task executo tme, we ca acheve a 64

3 5 th December 0. Vol. 46 No JATIT & LLS. All rghts reserved. ISSN: E-ISSN: eergy savg target usg the mmum ode to complete the task of trascodg. Assumes that a set of trascodg tasks eed to process batches vdeo data, a hgh-defto vdeo put data eeds to be coverted to k formats. Ths group trascodg tasks workload s k = = t. If the user requres the vdeo trascodg task executo tme s T, t eeds at least k = N t T odes. I addto, trascodg = = tasks schedulg costras that the vdeo fles processed by N odes are ot the same aytme. We ca amed the Best Ft Decreasg method [6] to solve the vdeo trascodg task schedulg problems. The procedure s as follows: Algorthm : costrat best ft decreasg algorthm Iput: a sery of vdeo trascodg tasks k k k t,t,...,t,t,t,...,t-,t,...,t Output: the umber of odes Num, task assgmet Nds ; Step : sort the trascodg tasks decreasgly t t t3... t ad set Nds = θ ; Step : fd the value of Num ad the task lst o Num each ode { Task } = by best ft method; Step 3: Isert the tasks of the frst ode to Nds ; Step 4: For = to Num s { record the task Task by, Pos=; s=0; Whle ( s! = s ) { For = to s t, { If ( t does ot coflct wth Nds ) { Nds = Nds {(t,,pos)} ; else = s ; } } } pos=pos + t ; s++; } Fgure The Vdeo Trascodg Servce Platform Motorg Iterface 65

4 5 th December 0. Vol. 46 No JATIT & LLS. All rghts reserved. ISSN: E-ISSN: SYSTEM IMPLEMENT AND RESULTS ANALYSIS 5. Vdeo Trascodg Servce Platform We gve oe C++ mplemetato of our vdeo trascodg servce platform system. The hardware system comprses oe maagemet ode ad four compute odes. Ope source vdeo trascodg program FFMPEG s deployed o the compute odes. Fgure ad Fgure 3 show the terface of our system. 5. Task Workload Predcto We mplemet the mult-scale task workload predcto module algorthm. We use 80 groups of Fgure 3: The Vdeo Trascodg Task Developg Iterface hgh-defto vdeo data trascodg ob as a testg task. We extract the put / output vdeo formats, stream frames, frame sze, CPU occupacy rate, as the aalyss of elemets of the executo tme. The expermet employs 70 batches of HD vdeo data for modelg ad 0 batches of data for model valdato. Compared wth Weka [6] four dfferet algorthm (decso tree, M5P, Gaussa process, lear regresso), our method ca acheve better predcto show Table. Table : Comparso Of Vdeo Trascodg Task Workload Predcto Results Item Mult-scale Decso Gaussa Lear M5P predcto tree process regresso Relato coeffcet Average absolute error RMS error Relatve error 9.05% 9.63% 4.87% 5.8% 4.95% RMS relatve error 3.9% 4.6% 7.94% 54.73% 49.97% 5.3 Task Schedulg Optmzato We also mplemet ad test costrat best ft decreasg algorthm (Algorthm ). Fgure 4 s the task workload predcto map for the task of covertg 80 groups of hgh-defto vdeo data to fve dfferet meda formats. The total workload s 605 mutes. Assumes that the user requres the vdeo trascodg task executo tme s 500 mutes. The, t eeds = odes. The task schedulg table obtaed by Algorthm s show Fgure 5. After performg may tests, t shows that Algorthm s able to deal effectvely wth the task schedulg problem of vdeo trascodg servce platform. Task schedulg pla s gve Fgure 5. The logtudal legth of the lattce the fgure stads for the task executo tme ad the umber lattce s the meda data processg label. By Fgure 5, t appears that there are o odes processg the meda data wth same label, smultaeously. 66

5 5 th December 0. Vol. 46 No JATIT & LLS. All rghts reserved. ISSN: E-ISSN: Fgure 4: The Task Workload Predcto 6. CONCLUSION Ths paper presets a vdeo trascodg servce platform system archtecture. Its vrtue s to adopt oe mult-scale workload predcto algorthm whch ca accurately predct vdeo trascodg task workload. It helps to mprove the vdeo trascodg servce platform effcecy of resource usage ad eergy cosumpto. REFERENCES: [] X J, Hlbert Trasform-Based Workload Predcto ad Dyamc Frequecy Scalg for Power-Effcet Vdeo Ecodg, Computer- Aded Desg of Itegrated Crcuts ad Systems, IEEE Trasactos, Vol.3, No. 5, 0, pp [] Bao Sheg, Dua Baotog, Shao Fegu, Research ad Applcato of Real-Tme Trascodg Based o Cloud-Computg Techology Trple-Play, Telecommucatos Scece, Vol. 7, 0, pp.-6. [3] Wag Bo, Lu Dche, L M, A Mxture Vdeo Trascodg Method for H. 64, Joural of Image ad Graphcs, Vol. 6, 009, pp [4] Ha Zhe, Hu Rum, Che Hao, L Mg, Error Reslet Vdeo Trascodg Based o Layered Rate Dstorto Optmzato,Chese Joural of Computers, Vol. 3, 009, pp [5] Zhou Yra Che Yaowu, Error-reslet vdeo trascodg based o adaptve redudat slce serto, Joural of Huazhog Uversty of Scece ad Techology (Natural Scece Edto), Vol. 39, 0, pp Fgure 5: The Task Schedulg Pla [6] Yue, My, A smple proof of the equalty FFD(L) (/9)OPT(L) +, for all L, for the FFD b-packg algorthm, Acta Mathematcae Applcatae Sca, Vol. 7, 99, pp