Architecture and Task Scheduling of Video Streaming on Multi-core Platform

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1 TELKOMNIKA Indonesan Journal of Electrcal Engneerng Vol.12, No.3, March 2014, pp ~ 2235 DOI: Archtecture and Task Schedulng of Vdeo Streamng on Mult-core Platform Jun L* 1,2, Hong N 1, Lngfang Wang 1, Jun Chen 1 1 Natonal Network New Meda Engneerng Research Center, Insttute of Acoustcs, Chnese Academy of Scences, Bejng, Chna Unversty of Chnese Academy of Scences, Bejng, Chna *Correspondng author, e-mal: lj@dsp.ac.cn Abstract Compared wth tradtonal vdeo streamng server, streamng on mult-core platform has many advantages: flexble and confgurable on the number of executng core accordng to system requrements; fault-tolerant; and fttng well to future process technologes, more cores wll be avalable n advanced process technologes, meanwhle the complexty per core does not ncrease. In ths paper, we focused on the vdeo streamng ssues on mult-core processor, ncludng archtecture and task schedulng. We proposed a ppelne-parallel hybrd mult-core archtecture and servce mgraton based task schedulng strategy on mult-core processor to mprove the effcency of vdeo streamng and ncrease the number of concurrence. We mplemented the task schedulng algorthm wth proposed archtecture, and provded evdences of 48% outperformance based on Cavum OCTEON CN5860 mult-core processor than full parallel archtecture, meanwhle, request success rate hgher than REM algorthm. Keywords: mult-core platform, ppelne-parallel hybrd archtecture, servce mgraton, task schedulng Copyrght 2014 Insttute of Advanced Engneerng and Scence. All rghts reserved. 1. Introducton VoD system provdes servce to termnal users by streamng tradtonal vdeo contents. It s usually composed of center task scheduler and a number of multmeda streamng servers. The center scheduler s responsble for task assgnment and management, user nteractve operaton and servce status mantenance, whch ncludes sesson parameters, vdeo contents dstrbuton and playng statstcal nformaton. Meanwhle, streamng server takes change of multmeda streamng wth nstructon from scheduler. As a man component, vdeo streamng server always s the key module n current nteractve on demand ndustry standards, lke ISA (Interactve Servce Archtecture) [1], NGOD (Next Generaton on Demand) [2] and so on. Both n ISA and NGOD, vdeo streamng system s desgned and used to push multmeda to STB (Set-Top-Box). VSS (Vdeo streamng system) works wth others modules lke SM (Sesson Manager), VRM (Vdeo stream Resource Management) STB (Set-Top-Box) and so on. Termnal users send ther VCR (Vdeo Cassette Recordng) sgnalng to VSS by STB, and then VSS parses the sgnalng, replaces current streamng wth correspondng content. For a vdeo streamng applcaton, lke VSS, the server may servce thousands of users concurrently, whch brngs n huge pressure to VoD system. Meanwhle, computer hardware manufacturers have moved decsvely to mult-core and are currently expermentng wth ncreasngly advanced many-core archtectures. And more and more network acceleraton devces use mult-core processor to acheve much hgher targets. But on mult-core platform, how to ntegrate those cores and scheduler modules on each core s stll under research, whch s also the most sgnfcant to fgure out. In the long term, wrtng portable, effcent and correct parallel programs targetng mult-core archtectures must become no more challengng than wrtng the same ones for sequental computers. To date, however, most applcatons runnng on mult-core machnes do not explot fully the potental of these platforms. In ths paper, based on mult-core platform, we proposed a ppelne-parallel hybrd streamng server mult-core archtecture to address problems lke concurrence, stablty and so on. There exsted several tools and lbrares avalable for constructng streamng applcatons, but many of them are orented to coarse gran computatons, such as StreamIt [3], Brook [4], and CUDA [5]. Some other tools, as TBB (Threadng Buldng Block) [6], provde explct mechansms for parallel Receved August 22, 2013; Revsed September 26, 2013; Accepted October 17, 2013

2 2228 ISSN: paradgm, whle some others, as openmp [7] and Clk [8] manly offers mechansms for data parallelsm and Dvde&Conquer computatons. These mechansms can also be exploted to mplement vdeo streamng system, as we shall compare our method wth them n Secton 3. Some other efforts also have been made to study streamng archtecture on mult-core platform. As n [9] proposed a desgn of a flexble dataflow archtecture amed at addressng many of the shortcomngs of exstng systems ncludng a unfed executon model for both demand drven and event drven models; t desgned a resource scheduler that can automatcally make decsons on how to allocate computng resources; and support for more general streamng data structures whch nclude unstructured elements. As n [10] ntroduced and dscussed FastFlow, a programmng framework specfcally targetng cache-coherent shared-memory mult-cores. FastFlow s mplemented as a stack of C++ template lbrares. The lowest layer of FastFlow provdes very effcent lock-free and memory-free synchronzaton base mechansms. Snce vdeo streamng applcaton can be constructed based on FastFlow and some other mult-core tools, but all of those methods dd not optmze. In ths paper, we proposed ppelne-parallel hybrd system archtecture, by analyzng and makng full use of features of vdeo streamng applcaton, to promote system concurrency performance. Vdeo streamng servers usually works lke a cluster, whch has to be load banlancng and fault tolerant. When the scheduler receves a new request from user, t has to allocate the request to a vdeo server wth some strategy whch s used for load balancng. In tradtonal task schedulng algorthm, the scheduler mgrate request to another server when a server has reached ts servce capacty, ths method s called last-mnute mgraton [11]. In REM algorthm, Yngqng Zhan [12] proposed double thresholds, mn_th and max_th, for servce mgraton. When the load of current vdeo server s larger than max_th, new request must be mgrated out because of that current vdeo server s n hearvy load. The request wll be mgrated out wth a knd of possblty, whch s the functon of system load, when load of current vdeo server s between n mn_th and max_th. New request wll be accepted by vdeo server f ts load s lower than mn_th, snce t has enough servce capacty. Based on ppelne-parallel hybrd archtecture on mult-core platform, we ntroduced the servce mgraton based task schedulng strategy. And mplemented them on Cavum OCTEON CN5860 [13] mult-core processor. 2. Vdeo Streamng on Mult-core Platform 2.1 Ppelne-parallel Hybrd Archtecture SMP (Symmetrcal Mult-Processng) systems are tghtly coupled multprocessor systems wth a pool of homogeneous processors runnng ndependently, each processor can executng the same program or dfferent programs and operatng on dfferent data and wth capablty of sharng common resources (memory, I/O devce, nterrupt system and so on) and connected usng a system bus or a crossbar. In ths paper, we wll construct a ppelne-parallel hybrd vdeo streamng archtecture based on SMP platform. Data streamng procedure n VoD system can be consdered as a data transmsson unt under sgnalng control. There may be thousands of users requre moves on demand s multaneously, vdeo streamng server should parse the sgnalng, allocate sesson and streamng resources, obtan content from CDN or local dsk caches and reply all of those requests n a very short tme, such as less than 500ms. Wth the objectve of vdeo streamng server, we partton the system nto two parts: control-plane and data-plane. Control-plane responsable for control related operatons, lke task construct, sgnalng parsng, and resources allocaton and soon. And data-plane takes charges of data related operatons, lke data obtanng, cachng and transmsson. In control-plane, each sgnalng s processed n ppelne style. Under pressure from large concurrence, data-plane s not easly to perform well. Wth mult-core processor, more cores can be easly confgured to run data-plane programs accordng to the system load stuaton. Almost all exsted VSS systems, such as ISA and NGOD, have used RTSP protocol to transfer sgnalng between vdeo streamng server and other modules lke STB, Sesson Manager and so on. After the scheduler recevng a sgnalng from STB, t cannot do any schedulng untl parameters parsed from sgnalng. Fgure 1 gves the parsng tme of a sngle RTSP message, and total 100 test tmes wth zero system loads. RTSP message parsng tme under dfferent system load s shown n Fgure 2(3-2310M 512M memory). TELKOMNIKA Vol. 12, No. 3, March 2014:

3 TELKOMNIKA ISSN: Fgure 1. Tme Used for Parsng a RTSP Message Fgure 2. RTSP Message Parsng Tme on Dfferent System Load RTSP message parsng tme ncreases wth the growth of system load. The frst RTSP protocol content parsng costs 2.52 mcroseconds wth zero system load, and when system load rsng to 2500, a normal RTSP request parsng costs almost 7 mcroseconds. Assumng that vdeo streamng system requres a RTSP request or response parsng tme less than 5 mcroseconds to guarantee user experence, t needs another core to process RTSP protocol parsng when the number of concurrent user rse up to In ths paper, we assgn approprate number of core to each part of vdeo streamng system. Mult-core archtecture s gven n Fgure 3. Fgure 3(a) and Fgure 3(b) show detals of control-plane and data-plane respectvely. In control-plane, cores can be confgured to process sgnalng flexbly. After RTSP sgnalng parsed, schedulng secton do task construct or task schedulng accordng to the parameters encapsulated n sgnalng, and data-planes do responses to those schedules by stoppng streamng or changng contents operatons. Data obtaned by data-plane wll be cached for ant-jtter purpose, and then, transmtted to STB through NICs (Network Interface Card). Fgure 4 gves a full parallel archtecture of vdeo streamng system. Wth the advantages of SMP system, each core on the mult-core platform runs the same program. For a vdeo streamng system, t ncludes streamng task establshng, RTSP message parsng, resources allocatng, content obtanng and data transmsson. Each core can process vdeo streamng task ndependently. In ths paper, we wll make the comparson between full parallel archtecture wth ppelne-parallel hybrd archtecture. control plane data plane sgnalng handle schedulng Task constructure Resource allocate content obtan cache transmt sgnalng handle (a) control-plane procedure (b) data-plane procedure Fgure 3. Detal of Pplelne-parallel Hybrd Archtecture CDN Local dsk STB Task constructure Task constructure Task constructure STB Sgnalng parsng Sgnalng parsng Sgnalng parsng STB Resource allocaton Resource allocaton Resource allocaton Other Modules lke SM Data obtan Data obtan Data obtan Data transmsson Data transmsson Data transmsson STB STB STB Fgure 4. Full Parallel Archtecture for Vdeo Streamng wth Mult-core Platform Archtecture and Task Schedulng of Vdeo Streamng on Mult-core Platform (Jun L)

4 2230 ISSN: task executon procedure { f(receve new sgnalng) { f(s SETUP) { //extract parameter from sgnalng sgnalng_handle(sgnalng); //establsh vdeo streamng task by allocatng resources streamng_task = task_construct(); //add streamng_task to task_lst on current core task_add(streamng_task); else{ streamng task parameter update; //terator every task n task_lst, dong streamng job for each task n task_lst { apply_data;//from CDN or local dsk; f data recved, add data to task_data_cache; sendng_data, usng funnel method to ensure stablty of rate; (a) control-plane executon procedure whle(1) { f(receve new data transmsson task) { create new data transmsson task; add new task to data transmsson task lst; apply data from CDN or local dsk; f(receve data task parameter update) { update task parameter; f(data receved) add data to task_data_cache; terator( task n data transmsson task lst) { sendng data to STB usng funnel method; update task parameter; (b) data-plane executon procedure Fgure 5. Each Execute Procedure of Hybrd Archtecture Fgure 5(a) and Fgure 5(b) provde the pseudo-code of vdeo streamng wth ppelneparallel hybrd archtecture Servce Mgraton Based Task Schedulng Streamng server always has a knd of storage ablty, but lmted. So ts servce capacty s restrcted. Based on ppelne-parallel hybrd archtecture on mult-core platform, we ntroduced the servce mgraton based task schedulng strategy, whch s used for system load balancng and vdeo server fault tolerant. Table 1 s the summary of key varables. Table 1. Key Varables symbol descrpton M Total number of content N Number of streamng server L Servce capacty of a streamng server R(j) Request for content j Current load of server l L Average load of all streamng server L th Servce load threshold of servce mgraton H Content dstrbuton matrx Streamng server connecton matrx p Mgraton possblty Request success rate T Servce delay of request Handle tme per request on streamng server Length of mgraton path Maxmum of mgraton path length () Set of mgraton path whch starts from server TELKOMNIKA Vol. 12, No. 3, March 2014:

5 TELKOMNIKA ISSN: Lth Fgure 6. Releatonshp between Mgraton Possblty and Servce Load n Our Apporach L When the scheduler receves a new request, t decdes whch vdeo server the request wll be assgned to based on current system servce load. If loads of the servers that contans requested content are lager than L th, servce mgrates out wth the possblty of p, and path length ( < ) s lmted. Calculaton of p s shown n Equaton (1). 0 p 1 1 B( l Lth) n l L L th th l L (1) Where B and n are system related parameters, we wll dscusse later. When load of current vdeo server l < L th, t s means that current server s n lght load, and can contnue to servce. If l > L th, t ndcates that current server s servng many request. In ths case, servce mgraton s needed for system load balance. Snce mgraton not only affects the requests n servce, but also ncreases the servce delay of new request. We mgrate request out wth a possblty when l > L th by usng modfed sgmod curve. From Fgure 6, t s obvous that the p s much ltter when load s less than L th, and p grows quckly f l > L th, whch means that mgraton happens wth much hgher possblty at hgh load. If p s grater than generated random p, servce mgraton happens. Long mgraton path mples hgher servce delay. We lmt mgraton path length to ensure the qualty of servce. If path length s longer than the longest tolerant length, new request wll be rejected wthout servce mgraton. Grater n s, much faster the mproved sgmod curve trends to 1. In our approach, n s a functon of mgraton path length, and t decrease rapdly wth grows up. The negatve exponental functon has been used to delver the releatonshp between n and, shown n Equaton (2). n B e (2) Because of mgraton path length drectly affects user experence n servce; mgraton path length should be lmted n practce to guarantee quanlty of servce. For the calculaton of mgraton path, every possable path can be computed before system begnnng servce under the stuaton that the multmeda content dstrbuton and vdeo server connecton are fxed. Vdeo server s taken as a node, servers contang the same content have a path connected each other, whch shapes a graph. We assumng that the system s stable, that s content dstrbuton and server connecton never change after system s runnng. By usng Djkstra algorthm [14], we can get the shortest path to all others nodes n the connecton graph, and recursve operate for each node. Durng servce mgraton, we only need to fnd the optmal path n exsted set wthout recalculaton. Based on ppelne-paralle hybrd archtecture, when the scheduler receves a new request R ( j), t wll run the followng steps to dspatch the request. Archtecture and Task Schedulng of Vdeo Streamng on Mult-core Platform (Jun L)

6 2232 ISSN: Step 1: The scheduler decdes whether servce mgraton s needed for R ( j) accordng current content dstrbuton matrx H and each server load l : a. If S contans content j and l Lth, then S wll servce R ( j), update load of S, goto step 6; b. If each servers S whch contans content j has l Lth : a) If there s at least on path n set () n whch the load of the node that after S s less than L, then computer every possable path length and mgraton possblty p accordng formula 1 and 2; b) else goto step 5; Step 2: The scheduler generates a random p, 0 p 1. If p p, then goto step 3, else goto step 4. Step 3: The scheduler send mgraton message to each server n mgraton path, and servce mgraton mplemented along the path whle R( j) s hold at scheduler. Step 4: S begn servce new request. Update load status of each server, goto step 6. Step 5: Reject R ( j). Step 6: The scheduler watng for next new request. In our algorthm, the optmal path should be found before each mgraton. Snce the new request n hold n scheduler, the mgraton path must be able to mnsze servce delay. We defne varance of load balancng as formula 3 n followng: N 2 ( l L ) 1 (3) N N Where, 1 L l, s the average load of all vdeo servers. When l1 l2 l N n a N 1 L L L determnated VoD system, s mnmum, whch means system s n a good load balancng. In ths paper, lnearty weghted aggregaton method s used to pose mgraton path length and varance of load balancng, as shown n formula 4. mp (1 ) (4) In whch s the length of each possable mgraton path, and mp wll be chosed for mgraton.. The path wth smallest 3. Results and Analyss 3.1. Experments of Ppelne-parallel Hybrd Archtecture We take Cavum OCTEON CN5860 [13] seres mult-core processor as our target platform. Cavum OCTEON CN5860 mult-core processor has 16 cores, wth 800MHz per core. It has a 10GB SPF+ port, and ts memory can be confgured as 4GB or 8GB. In order to test the performance of the proposed archtecture, we mplement ppelne-parallel hybrd archtectures usng C, and compared t wth full parallel archtecture usng openmp [7]. Fgure 7 dsplays the nter-core communcaton overhead between control-plane and data-plane n ppelne-parallel hybrd archtecture. Based on Cavum OCTEON CN5860 multcore processor, an nteractve operaton among dfferent core cost ncreases wth growth of the load on the core. Where the concurrency rses up to 3500, nter-core communcaton cost s almost three tmes of lght load. TELKOMNIKA Vol. 12, No. 3, March 2014:

7 TELKOMNIKA ISSN: Fgure 7. Inter-core Communcaton Overhead The performance of control-plane and data-plane n ppelne-parallel hybrd archtecture s shown n Fgure 8. For data-plane, each core can handle 230 data transmsson task, wth btrate of 3.75Mbps per each task. And the concurrent performance of control-plane on a sngle core up to 2300 streamng tasks. So, the stuaton of one core executng control-plane can work wth 10 cores runnng data-plane, and the total throughput of vdeo streamng reaches to 10Gbps, the top capacty of the platform. Fgure 9 shows performance comparson between ppelne-parallel hybrd archtecture and full parallel archtecture, whch mplemented by usng openmp, and t s clear that the pror method outperformance 48% than the later one. Fgure 8. Task Number per Core Usng openmp Fgure 9. System Concurrent Performance Pared wth Full Parallel Archtecture 3.2. Task Schedulng Experments We mplemented the proposed task schedulng alogrthm on ppelne-parallel hybrd archtecture. In experments, we smulated 140 vdeo contents and 12 vdeo servers. Each server has 20 content storage capacty and servce capacty s 120 concurrent requests. The user request pattern s a Posson process wth an arrval rate of request/mnute, where vares from 36 to 44. The access possblty of each of 140 vdeo contents s modeled by Zpflke dstrbuton [15] wth Besdes, we set =0.5, whch means that load balancng and servce delay have the same weght n mgraton path selecton. At the same tme, after a lot of testng, B s set to 2500, n ths case, the mproved sgmod curve changes most close to real VoD system. In order to prove the effectveness of servce mgraton based task schedulng strategy, we compared t wth REM algorthm, Fgure 10 shows the request success rate n the smulaton perod for servce mgraton based task schedulng method and REM, and the pror performance better than REM algorthm. Fgure 11 demostrates dffernecy of the servce delay between our proposed task schedulng strategy and REM. Snce the precomputaton of mgraton path, servce mgraton based task schedulng algorthm has much lower servce delay and wth smaller delay jtter. Archtecture and Task Schedulng of Vdeo Streamng on Mult-core Platform (Jun L)

8 2234 ISSN: Fgure 10. Success Rate as a Functon of System Load Fgure 11. Servce Delay as a Functon of System Load 4. Concluson The wdespread use of mult-core processor s pushng explctly parallel hgh-level programmng models to the forefront. In ths paper, we proposed a mult-core archtecture for vdeo streamng system combned ppelne wth concurrent modules. One of the man ssues of gettng an even dstrbuton of computaton across processors s dealt n an ntegrated fsson and parttonng step that breaks up computaton unts just enough to span the avalable processors. The ssue of sgnalng control overhead s overcome by an ntellgent assgnment, whch overlap all communcaton wth computaton. Total vdeo streamng task s splt nto control-plane and data-plane. Hgh performance requres much more core resources; both control-plane and data-plane can be allocated more cores to satsfy system requrements. We compared the performance of our archtecture wth full parallel ones, and the experment results show that hybrd archtecture gans much hgher concurrence than the compared one. Meanwhle, the proposed servce mgraton based task schedulng algorthm acheved hgher request success rate and smaller servce delay jtter than REM. Acknowledgements We thank the team leader, my supervsor and other members for ther contnuous help n dscusson and suggeston on the task schedulng strategy n ths paper. We are ndebted to members of the Natonal Network New Meda Engneerng Research Center for ther contrbutons to ths work. Ths work s supported by Natonal Hgh-tech R&D Program of Chna (2012AA011703), Natonal Key Technology R&D Program of Chna (2012BAH02B01) and the Key Program of Chnese Academy of Scences (KGZD-EW-103-4, KGZD-EW-103-2). References [1] Ells, Lesle. Insde Tme Warners's Interactve-Servces Archtecture. Tme Warners. Report number: [2] Comcast Corp NGOD Overall Archtecture. Phladelpha. Comcast Corp [3] Thes W, Karczmarek M, Amarasnghe S. StreamIt: A language for streamng applcatons. 11th Conference on Compler Constructon. London. 2002: [4] Buck I, Foley T, Horn D. Brook for GPUs: stream computng on graphcs hardware. ACM Transactons on Graphcs (TOG). ACM. 2004; 23(3): [5] Davd Krk. Nvda cuda software and gpu parallel computng archtecture. In Proc. of the 6th Intl. symposum on Memory management (ISM). New York. 2007: [6] Pheatt C. Intel threadng buldng blocks. Journal of Computng Scences n Colleges. 2008; 23(4): [7] Chapman, Barbara, Gabrele Jost, Ruud Van Der Pas. Usng OpenMP: portable shared memory parallel programmng. Cambrdge: The MIT Press [8] Cole C, Herlhy M. Snapshots and software transactonal memory. Scence of Computer Programmng. 2005; 58(3): [9] Vo HT, Osmar DK, Summa B. Streamng-Enabled Parallel Dataflow Archtecture for Multcore Systems. Computer Graphcs Forum. Blackwell Publshng Ltd. 2010; 29(3): TELKOMNIKA Vol. 12, No. 3, March 2014:

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