A Cloud-based Collaborative Video Story Authoring and Sharing Platform

Transcription

1 Chen Wng, et. l. 8 : 69 A Cloud-bsed Collbortive Video Sry Authoring Shring Pltform Chen Wng 1, Rjiv Rnjn 1, Xingmin Zhou 1, Krn Mitr 1, Suhit Sh 1, Meng Meng 1, Dimitrios Georgkopoulos 1, Lizhe Wng 2 Peter w 1 1 Informtion Engineering Lborry, CSIRO ICT Centre. Emil: firstnme.lstnme@csiro.u Corresponding Author: Rjiv Rnjn (rjiv.rnjn@csiro.u) 2 Center for Erth Observtion & Digitl Erth, Chinese Acdemy of Sciences Emil: LZWng@ceode.c.cn growing ubiquity of Internet cloud computing is hving significnt impct on medirelted industries. se industries re using Internet cloud s medium enble cretion, serch, mngement, consumption of ir content. Rich web pges, softwre downlods, interctive communictions, ever-exping universe of digitl medi require new pproch multimedi (e.g., udio,, imges, etc.) content shring mngement. Size volume of multimedi content is growing exponentilly. Though re re mny online hosting services (YouTube, flickr, dilymotion, etc.) for sring viewing multimedi content, re is cler lck of softwre pltforms tht cn help end-users in content shring ctivities in socil setting. This pper introduces principle concepts of multimedi content shring in socil setting presents novel pltform tht supports cloud-bsed collbortive telling. pltform leverges socil networks for serch recommendtion for purpose of telling. Socil Content Authoring Shring Pltform (SCASP) leverges MediWise Cloud Content Orchestrr (MCCO) tht supports do-it-yourself cretion, serch, mngement, consumption of multimedi content. MCCO supports dynmic content delivery using CPU srge services vilble in public cloud. pltform exploits flexible content distribution cpbilities of MCCO for group of possibly geogrphiclly distributed end-users who collborte on specific tsk such s telling. Index Terms - Content Delivery Network, Cloud Computing, Medi Mngement, Medi Delivery, Medi Consumption, Personliztion, Qulity of Service I. Introduction re is lrge number of s produced sred in public (e.g., YouTube) privte (e.g., Smrtphones) dt reposiries on dily bsis. Sry-telling is common use-cse for using se s where n end-user composes set of s ger tell, eir for lerning purposes or shring experiences. For exmple, news edir my serch bsed on collected from multiple privte public reposiries; n instrucr my produce teching mterils using set of publicly vilble clips. Existing serch engines hve limittion on identifying useful contents for users with different needs [7]. For user who ttempts using vriety of s, re re number of few chllenges with existing technologies. A serch engine often returns long list of s tht re relevnt keywords end-user enters in serch engine. How s in list re suitble for line requires end-user s furr investigtion mount of work involved often overwhelms end-user considering number of s returned serch engine. It is often cse tht highly rnked s re not most pproprite ones for line under. In ddition, s re not orgnized in structured mnner bsed on content, which mkes identifying s tht mtch pic difficult time consuming. Mny existing work tckle this problem integrting textul visul concepts group s. Our method trets shring s collbortive process. It is different or methods minly in sense tht we integrte collborrs contributions vi socil networking services help -telling. se contributions include dding comments, recommending

2 8 : 70 A Cloud-bsed Collbortive Video Sry Authoring Shring Pltform relevnt s etc. With certin umted informtion processing, method cn effectively reduce worklod of n individul when consisting of lrge number of s from vrious sources. We ssume tht hs number of friends or collborrs who hve knowledge on pic re willing contribute. It is common in rel world tht se people prticipte in sme event or hve mutul interests on pics of. As sries re diverse group of people who my be interested in contributing m re dynmic, mnging se users with different interests is chllenging problem. We ddress this problem leverging power of socil networks orgnize dynmic group of people who re contributing. min technology tht helps in mnging distributing udio content over Internet is content distribution networks (CDNs). Current pproches provide content delivery networks wrrnt provider eir setup his own computer, srge, brodb network, or IT infrstructures or lese m from estblished 3rd prty ISPs or telecommuniction network providers (e.g., Akmi Telstr). Both of forementioned pproches hve been proven be economiclly non-vible, hence out of rech for ll (e.g., public librries, universities, schools, SMEs, socil network-bsed users etc.) but lrge enterprises. For exmple, group of people (e.g., urists or school clss on field trip) intending shre sries of s describing points of interest would need cess ffordble mngement distribution technology. se s my hve been cptured vi ir smrtphones during ir trip. Such typicl use-cses of telling often require significnt mount of effort from s identify relevnt s, orgnize m bsed on pics or timelines edit m fit in lines. Existing CDN serch distribution technologies re not suitble cope with needs of se use-cses. two spects re intertwined. Dt loclity plys n importnt role in trditionl CDNs. Dt sred in surrogte server in CDN is likely be ccessed frequently end-users ner geogrphiclly in short time period. However, in collbortive scenrio, dt loclity does not necessrily hold, e.g., in Austrli my hve collborrs round world. MediWise Cloud project in CSIRO hs developed MCCO pltform [5],[10],[11] tckle problem of trditionl CDN technologies, with focus on supporting such dynmic content shring distribution using chep public cloud services (CPU, srge, network). In prticulr, MCCO supports following novel cpbilities: (1) Exclusively utilize public Internet public cloud infrstructure. MCCO cn utilize cloud srge CPU services from virtully ny public cloud provider, while not requiring ownership of privte networks servers. (2) Supply content mngement services tht cn be used content mngers ( clss of end-users (e.g., school going kids, school techers, etc.) tht is currently not supported CDNs) mnge content delivery workflows for ir end users. (3) Support dynmic content delivery enble collbortive ctivities. (4) Provide semless personlized end-user experience. proposed Socil Content Authoring Shring Pltform (SCASP) leverges MCCO demonstrtes MCCO s cpbilities s regrds content mngement distribution. MCCO SCASP pltforms refore trets cloud s n intermediry between sources endusers. It mkes use of ubiquiusness of public cloud services help distribution. In our pltform, cloud is both n infrstructure provider socil portl on which users interct with ech or. min objective of developing proposed SCASP pltform on p of MCCO is trnsprently dynmiclly plce dt mong geogrphiclly distributed cloud services speed up dt ccess leverges user s socil connections collbortively sries. MCCO plys role of n orchestrr of cloud services relted production, delivery consumption. This pper builds on our previously published ppers [5] [12]. As compred our previous ppers, we mke following novel contributions here: 1) It mkes -telling collbortive process supports min of interctively integrte collborrs input including comments recommendtions; 2) collbortion is dynmiclly orgnized leverging services provided socil networks, in our cse, Fcebook services; 3) pltform ggregtes collborrs recommendtions comments. It is cpble of processing certin informtion in n umtic mnner, e.g., it cn orgnize informtion bsed on pics timeline furr help shring. 4) It supports collbortive editing; It exploits flexible content distribution of MCCO for group of possibly geogrphiclly distributed end-users who collborte on specific tsk such s ; rest of pper is orgnized s follows: Section 2 summerises relted work; Section 3 introduces rchitecture of our MCCO; Section 4 focuses on SCASP editing in MediWise Cloud; Section 5 presents detils relted integrtion of SCASP, MCCO, socil network (Fcebook); Section 6 rticultes our experiments; Section 7 concludes pper. 2. Relted Work This section surveys existing work for content, including similrity serch techniques. Mny serch techniques hve been proposed identify relevnt s of given end-user query. Typicl serch techniques cn be clssified in two ctegories: textbsed content-bsed [14]. In text-bsed pproches, tgs or keywords re usully extrcted from titles or

3 contributions vi socil networking services help -telling. se contributions include dding comments, recommending relevnt Chen Wng, et. l. s etc. With certin umted infor- 8 : 71 mtion processing, method cn effectively reduce worklod of n individul when surrounding descriptions for serching relevnt s. composing developed cloud-bsed out generic of lrge sclble number softwre of In content-bsed pproches, visul fetures, such s color frmework clled MCCO for supporting end--end s from vrious sources. hisgrms, grey intensity or some locl interest descriprs, lifecycle opertions required for mnging content vi re extrcted used in similrity mtching obtin clouds Internet. MCCO exploits public clouds more objective list or re-rnk results obtined from for offloding computing, srge, network, content text-bsed serch. In [15], question nswering techniques re 3distribution MEDIAWISE functionlities CLOUD in cost CONTENT effective mnner. ORexploited support personlized news retrievl. In [9], CHESTRATOR Detiled informtion on MCCO cn be found in publictions Neo et. l proposed leverge extrctble semntics [10] [11]. However, for completeness of this pper we with relevnt externl informtion sources for event-bsed briefly describe technicl detils of MCCO here. nlysis, which discovers pic hierrchy supports Lyer Front End question nswering. In [18], hierrchicl content Medi Applince Instnce Srge summriztion technique ws proposed describe ctegories, events detiled informtion bout in different levels for efficient skimming mngement. Content Monir Security In [13], textul visul concepts re integrted for browsing udocumentry. In [17], Zhou et.l Progrmming Lyer Bck End proposed convert ech in compct word sequence Medi Applince Instnce Srge tht cn be furr used for mtching effectively Mnger Mnger Mnger efficiently extending string mtching techniques. In Monir Security Content Mnger Mnger Mnger [16], Zhou et.l proposed exploit tensor series model for serch over shring community contining s without lrge vritions. Existing techniques hve gretly improved performnce of retrievl from different SOAP/Restful API spects. However, while collbortion of users re not Infrstructure Lyer hrnessing public clouds considered in [15], [9], or retrievl techniques [18], [13], [17], [16] cn not process interction between endusers system t ll. Fig. 1. MediWise Orchestrr Cloud Architecture Content Orchestrr Archi- Fig. 1 : MediWise Cloud Content Video techniques hve been proposed edit s for different pplictions such s e-lerning entertinment. In [8], Meng et.l proposed two-step tecture MCCO offers enhnced flexibility elsticity s it inherits py-s-you-go model from public cloud services. compressed domin editing pproch which includes MCCO content orchestrtion opertions include: (i) production stge post-production stge. originl production: crete edit; (ii) srge: uploding scling Recll tht in MediWise Cloud project we s from cmers re compressed in production stge of srge spce; (iii) keyword-bsed content tgging furr compressed in postproduction stge. editing hve serching developed (iv) distribution: cloud-bsed streming generic downloding. sclble At Cloud softwre service frmework level, MCCO clled cpbilities MCCO spn for cross sup- ols llow end-users perform different functions trnsformtions for different visul porting rnge of opertions end--end such s selection, lifecycle ssembly, opertions deployment required for mnging content vi clouds effects. In [2], techniques hve been proposed compose of cloud services moniring ir runtime QoS sttistics piece of long line with smooth flow of informtion (e.g., ltency, utiliztion, throughput). MCCO orchestrte cusmize under temporl plyout constrints. In [3], Binchi presented AuAudirium System technology opertionl chrcteristics, which cn crete multi-cmer progrm of lecture in rel time umticlly. In [4], s presented system for cpture live trnsmission on mobile phones. se techniques were proposed for effectively production in public cloud services vi open-source RESTFul APIs. It supports deployment, configurtion moniring of content cloud services using Web-bsed widgets. se widgets hide underlying complexity relted cloud services provide n esy do-it-yourself interfce for content mngement. high level rchitecture of MCCO is shown in Fig. 1. lyer presents unified front end for single user opertion environment, while interction of end-users perform forementioned content orchestrtion multiple users is not considered in process of editing. opertions. It hides complexities relted ll se Our method trets -telling s collbortive process. It is different or methods minly in tht we integrte collborrs contributions vi socil networking services help -telling. se contributions include dding opertions using plethor of inhouse open source APIs. se APIs re implemented t Progrmming lyer mnge opertions for Infrstructure lyer. For exmple, strting spping virtul mchine (CPU service). comments, recommending relevnt s etc. With certin umted informtion processing, method cn effectively reduce worklod of n individul when composing out of lrge number of s from vrious sources. 3. Mediwise Cloud Content Orchestrr Recll tht in MediWise Cloud project we hve 3.1 Infrstructure Lyer This lyer provides cloud-bsed hrdwre resources s virtuliztion enbled services such s CPU, srge, routers switches tht hosts content mngement pplinces such s streming server, indexing server, editing server. Hrdwre resources expose certin configurtion tht cn be tgging stremi level, M operti ment o time Qo through services ports d ing of bsed w lying co provide tent m of MCC present perform operti ll s house implem mnge exmpl chine (C 3.1 In This l sources CPU, s med indexin resourc be lloc stre

4 8 : 72 A Cloud-bsed Collbortive Video Sry Authoring Shring Pltform llocted content mngement pplinces. For exmple, streming server pplince vilble from Wowz 1 cn be ssigned following Amzon EC2 CPU configurtions: 7.5 GB memory, 4 EC2 Compute Unit, 850 GB instnce srge, 64- bit ddressing moderte I/O. More detils on optiml hrdwre pplince selection cn be found in following pper. In generl, cloud providers mnge resources t Infrstructure lyer through hrdwre virtuliztion technologies such s Xen, Citrix, KVM (open source), VMWre Microsoft Hyer- V. Virtuliztion llows providers get more out of hrdwre resources llowing multiple instnces of virtul services run t sme time. Ech virtul service believes it hs its own hrdwre. Virtuliztion isoltes hrdwre resources from ech or, re mking fult lernt isolted security behviour possible. 3.2 Progrmming Lyer This lyer implements logic for interfces exposed lyer. For exmple, Medi Applince Mnger implements Cloud service API tht llows Applince list set of content mngement pplinces (e.g., streming, indexing editing servers) ssocited with owner s ccount. Progrmming Lyer is lso designed llow engineers plug-in different Cloud service APIs. Notbly, ech of mngers t this lyer hs perform certin orchestrtion opertion on Infrstructure lyer cloud services, such s provisioning of streming server pplince over n Amzon EC2 or indexing of contents over Amzon S3. Currently, our implementtion works with Amzon Web Service (AWS) is being extended support or Cloud providers. 3.3 Lyer Lyer encpsultes user interfce components in form of six principle widgets including Medi Applince, Instnce, Srge, Monir, Content, Security. Next, we provide brief detils bout ech widget. 1) Medi (Content Mngement) Applince : It lists set of medi pplinces ssocited with content owner s ccount. In generl, n pplince is pre-configured, selfcontined, virtuliztion-enbled, pre-built softwre resources or pplinces (e.g., streming, indexing editing servers) tht cn be integrted with or comptible pplinces for rchitecting complex pplictions such s -ondem CDN (Content Delivery Network). 2) Instnce : Content owners re required describe medi pplinces deployment configurtions tht will ffect drive its instnce s plcement performnce. Configurtion prmeters include number of instnces, ir types, security setting, moniring preference. In context of Amzon s EC2, different instnce types provide different minimum performnce gurntees depending on ir memory, srge, processor configurtions Additionlly, content owners or CDN dministrrs cn lso consider non-functionl ttributes relted deployments such s hosting cost, ltency, throughput, sclbility, vilbility. 3) Srge : It llows content owners uplod content medi pplinces srge service (e.g., Amzon S3). Cloud srge services provide highly durble vilble srge for vriety of content types, including web pplictions multi-medi files. 4) Content : It enbles functionlity for tgging, indexing, personlizing content with metdt. It exposes drg drop interfce for mpping of n udio/ content from cloud srge medi pplince. Content cn be tgged with one or more keywords. 5) Monir : It supports moniring sttus of medi pplince instnces, network srge services. Monired dt such s medi pplince throughput, utiliztion, disk I/O re mde vilble in form of twodimensionl chrts. 6) Security : It mnges ll untiction iztion credentils relted orchestrting content (e.g. content ccess secret key) cloud services (ccess key secret key). MCCO includes security credentils provided Amzon EC2 which is red directly from file sred in web folder. In future, we will implement more security mechnisms better mnge security credentils from different cloud providers. 4. Socil Content Authoring Shring Pltform (SCASP) re re following roles in SCASP: 1) Content provider: A content provider owns dt objects, such s /udio dt records tht cn be shred mong group of end-users. A content provider hs dt reposiry but limited cpbility effectively distribute its dt objects end-users. It refore delegtes dt distribution functionlity third prty content distribur. content provider my request informtion flow control when shring certin dt objects different groups of end-users. content distribur needs tke cre of this spect s well. dt objects in dt reposiry re indexed titles, s, publishing dtes tgs. 2) Sry s: A content mkes use of content from vrious content provider compose sries for lerning curting purpose. re is lrge number of s in dt reposiries. It is time consuming process for compose. system enbles group of contriburs collborte on. A content enduser hs list of socil links mintined socil informtion ggregr. 3) Sry contriburs: A contribur eir comments on under composition or recommends s relted.

5 Chen Wng, et. l. 8 : 73 4) Socil informtion ggregr: A socil informtion ggregr is n ppliction running on Fcebook like system tht ggregtes socil links of subscribers mintins ir content ccess informtion. ggregr is ble ccess public informtion of n end-user s well s some personl informtion grnted end-user. In ddition, it is cpble of ggregting informtion bout group of users. 5) Content distribur: content distribur (MCCO in our cse) is service provider tht mkes use of enduser informtion from socil informtion ggregr optimize content ccess experience of end-users of content provider. It replictes content from content provider using virtulized services from cloud infrstructure provider optimize ccess experience of content end-users. 6) Cloud infrstructure provider: A cloud infrstructure provider offers geogrphiclly distributed physicl resources s virtuliztion enbled services, including CPU srge services for MCCO orchestrte. content distribur (MCCO) is in center connects content provider, socil informtion ggregr cloud infrstructure provider. design is mke use of cloud infrstructure socil informtion for following purposes: 1) better content plcement bsed on end-users loctions. 2) better content cusmiztion for group of end-users of prticulr Fig. 2. Overview content provider of SCASP. curte dt objects of certin pics. In this pper, we focus on collbortion between of socil networks contriburs. orgnize dynmic group 4.1 System of people Overview for contributing. overview SCASP of system minlyis consists illustrted ofin Fig. following 2. We two ssume components: tht hs number of friends or collborrs 1) A Web who hve userknowledge interfce on s working bench re willing contribute for. It is compose common sries. in rel world tht se user people interfce prticipte requires in sme event or hve mutul interests on pics of. As sries re login using her Fcebook ccount. diverse group of people who my be interested in untiction is done using OAuth 2.0 contributing m re dynmic, mnging se end-users with different procol interests [6]. is chllenging ccessproblem. of user informtion We ddress this problem is through leverging Fcebook power Open of socil Grph networks API. orgnize 2) dynmic A socil group informtion of people contributing ggregr tht. does SCASP following minly consists tsks: of following two components: ) publishes s ctivities s socil working networking bench for site. 1) A Web user interfce compose ctivities sries. include user interfce metdt requires of login under using her Fcebook s well ccount. s ongoing editing ctivities; untiction is done using OAuth 2.0 procol [6]. ccess of user informtion is through Fcebook Open Grph API. b) retrieves relevnt informtion contributed collborrs, including 2) A socil informtion ggregr tht does following tsks: collborr s comments ) publishes recommended s ctivities relevnt socil s networking site. collborr; ctivities include metdt CSI JOURNAL COMPUTING, VOL. XX, NO. XX, XXXXXXXXX editing ctivities; CSI JOURNAL COMPUTING, VOL. XX, NO. XX, XXXXXXXXX 7 of c) processes under informtion s well s ongoing discovers certin pics s well s timelines present. Fig. 3 gives n exmple tht shows bsic informtion flow in pltform. In this scenrio, n cretes bout Sm pltform using her Fcebook ccount. Secondly, she submits query with keyword Sm Ssur serch for s strt. pltform psses query serch services of reposiries. Ech reposiry returns list of relevnt s. my select one dd it bord. Thirdly, pltform umticlly publish ction Fcebook. Fourthly, collborr Fig. 2 : Overview of SCASP. who Fig. 2. is in Overview s of SCASP. friend list in Fcebook my dd comments or recommend s tht b) retrieves relevnt informtion contributed re relted collborrs,. including In collborr s next step, comments recommender of socil networks subsystem recommended orgnize processes dynmic relevnt se comments of people collborr; for recommendtions. contributing Itn. presents s group recommendtions c) processes SCASP minly informtion consists of discovers who following my certin tke two components: pics recommendtion s well s timelines mke present chnges on bord. 1) A Web user interfce s working bench for compose sries. user interfce requires login using her Fcebook ccount. untiction is done using OAuth 2.0 procol [6]. ccess of user informtion is through Fcebook Open Grph API. 2) A socil informtion ggregr tht does following tsks: ) publishes s ctivities socil networking site. ctivities include metdt of under s well s ongoing editing ctivities; Fig. 3 b) : An retrieves exmple of relevnt bsic informtion flow Fig. 3. An exmple of bsic informtion flow in con- tributed in pltform. pltform. collborrs, including collborr s comments Fig. 3 gives n exmple tht shows bsic informtion Apprently, nrecommended ctive collborr relevnt is likelys flow in pltform. In this scenrio, n cretes recommends collborr; tht re more relevnt bout Sm Ssur, n Austrlin tennis plyer. Firstly, c) line processes or dd comments informtion tht cn help, e.g., sports news edir, signs pltform discovers ccount. using her Fcebook improve certin Secondly, line. picsshe s submits However, well s timelines query with when keyword re re number of collborrs, reviewing recommendtions pltform psses query comments serch my services Sm Ssur present serch for s. strt. of become Fig. reposiries. 3 gives time consuming nech exmple process. reposiry tht shows In our returns pltformsic relevnt informtion we provide s. flow recommendtion inmy select pltform. one service In dd forthis it b- list of esing scenrio, n tsk. cretes bout Sm Ssur, n Austrlin tennis plyer. Firstly, 4.2, CSI Recommender Journl e.g., of sports Computing news edir, Vol. 1 signs No. 3, in2012 recommendtion service fits in system s shown in Fig. 4. pltform she subm sur s pltform of returns my sele Thirdly, t ction who is in my dd re relt recomme ments n recomme tke r Fig. 3. A pltform. Appr recomme utho when th viewing become form, we esing th 4.2 Rec reco tem s sh re text dt

6 8 : 74 A Cloud-bsed Collbortive Video Sry Authoring Shring Pltform bord. Thirdly, pltform umticlly publish ction Fcebook. Fourthly, collborr who is in s friend list in Fcebook my dd comments or recommend s tht re relted. In next step, recommender subsystem processes se comments recommendtions. It n presents recommendtions who my tke recommendtion mke chnges on bord. Apprently, n ctive collborr is likely recommend s tht re more relevnt line or dd comments tht cn help improve line. However, when re re number of collborrs, reviewing recommendtions comments my become time consuming process. In our pltform, we provide recommendtion service for esing tsk. simultneously. Generl opertions include merging splitting. We only focus on tht cretes s bsed on existing short segments in system. Video is n importnt process in production, which delivers end-users coherent presenttion of n event or nrrtive bsed on cidte set of vilble segments. This service hs been driven its wide pplictions in e-lerning entertinment domins. For exmple, in e-lerning, techers my compose progrmming lecture using severl existing relted lectures. In entertinment, it is common tht movie producers compose whole movie bsed on number of short segment scenes. In socil community, end-users my produce new clips bsed on existing s for recretion. However, trditionl techniques for re conducted 4.2 Recommender single user over single mchine. Although recent system 8 CSI JOURNAL COMPUTING, VOL. XX, fits NO. XX, recommendtion service inxxxxxxxxx system s hs been developed permit multiple users compose shown in Fig. 4. single ger [1], it is still chllenging crete tht reflects n inherent in event collbortive wy. coherent presenttion of n or nrrtive CSI JOURNAL COMPUTING, VOL. XX, NO. XX, XXXXXXXXX Motivted wek points of existing bsed on cidte set of vilble segtechniques, we perform collbortive production in our ments. This service hs been driven its wide shring pltform (SCASP), which tkes service using pplictions in e-lerning entertinment following chrcteristics in considertion. i.e.xuggler system Fcebook editing frmework. s web ppliction. supports multi-user collbortion in in cn obt domins. For exmple, in e-lerning, techers socil community. Interctive production is performed editing splitlecture merge my pplince compose supports progrmming using ut receiving number of clips tht re recommended functionlity, s two different function severl existing relted lectures. contriburs selected of In enter opertions. of tht url loction of such s tinment, it conducting is detils common movie producers telling pltform, editing opertions using API functions provided Xuggler editing frmework, content or request dt re pssed on compose whole movie bsed on number in Fig. returning finl new In of composite Fig.Fig recommender rchitecture in SCASP. of Xuggler APIproduced block, where ctul : recommender rchitecture in SCASP. short segment scenes. socil com- in. We choose Xuggler frmework for our editing opertion hppens, content creted ison u munity, users my produce new clips bsed implementtion becuse of its dvntges over or ols. recommender refines both text dt from kept in which pssed ontrdi coll existing s recretion. However, For exmple, itinstnce, llowsfor Jv progrms is decode, encode, sources socil informtion ggregr. A informtion ggregr. A dt source experience lmost ny formt without codecs problems. srge cloud, vi MCCO srge mnger. such s r tionl techniques for re condt source cn be privte dt reposiry or public dt cn simplify progrmming from Jv, perform commen cn be privte reposiry or mintins public It reposiry. socil dt informtion ggregr ducted single user over single mchine. editing in resonble fst speed. informtion such s s creting ctivities, dt reposiry. socil informtion ggre- Although recent system hs been developed ommend collborr list mnges input from collborrs. gr mintins informtion such s u- permit multiple users compose single bsed on recommendtion service produces following three thor s cretingctivities, collborr ger [1], it is still chllenging compose types of recommendtions : list mnges from collborrs. 1) Relevnt pics: listinput of s ctegorized pics crete tht reflects n inherent in bs cn be derived fromservice line under. tht recommendtion produces fol- collbortive wy. A collborr is cpble identifying pics mong lowing three types of ofrecommendtions set Motivted wek points of existing of s recommending se s. : techniques, we perform col2) Timeline recommendtion: group of collborrs 1)my Relevnt pics: list of s ctegolbortive production in our unnotte s bsed on event time in s. service cn tht present relted longfrom rized pics cn be s derived thoring shring pltform (SCASP), which time line help. line under. A col- tkes following chrcteristics in con3) Or recommendtion: minly contins comments lborr is cpble of identifying pics sidertion. i.e. system supports multi-user collected from socil informtion ggregr tht mong set of s recommending collbortion in in sohve no clerstructures. se s. cilfig. community. Interctive production is 5 : Architecture of Video Authoring Service. 4.3 Video Editing or Authoring 2) Timeline recommendtion: group of col- Fig. performed receiving number of clips flow of ppliction when n end-user 5.Architecture of Videostrts, Authoring Service Video editing service core component in SCASP lborrs myisnnotte s bsed on intercts tht rewith recommended contriburs SCASP vi its from Web Appliction tht intercts with content end-users enbles multiple Interfce (see Fig. 5) (which we lso cll s MediWise event time in s. service selected of telling end-users perform collbortive Portl) re mkes cllopertions MCCOusing tht cn present relted s long time Web pltform, conducting editing 4.4 Usge Fig. 6. U line help. API functions provided Xuggler typiclediting scenrio for using returning system is composin 3) Or recommendtion: minly contins A frmework, below: new of composite comments collected from socil in- shown finl s produced 1) We signs frmework in system formtion ggregr tht hve no cler. choose Xuggler for our using her Fcebook ccount; structures. editing implementtion becuse of its Video Authoring Interfce ON SPLIT / MERGE click events End-User EDITING REST API (SPLIT AND MERGE) Medi detils re pssed on Xuggler APIs for mnipultion (split merge functionlity) Xuggler APIs Srge API returns new URI for s creted CPU Service Metdt <content> tble insert for new s creted. < end-user_hs_content> tble insert for reltionship b/w client. <content> <> reltionship updted Srge Service Editing service updtes dtbse with new content detils (BY CALLING REST APIs)

7 .4 Usge Fig. 6. User interfce: serching for s for A typicl scenrio for using system is composing Chen Wng, et. l. 8 : 75 hown s below: 1) signs in system controls orchestrtion opertions relted cloud-bsed usingcpu herservices Fcebook ccount; tht hosts editing pplince. editing pplince implements service using 2) uses serch functionlity Xuggler editing frmework. s web ppliction. editing find s. This willshow how s two pplince supports split merge functionlity, different opertions. serch results re presented detils how of url loction of content or request dt re pssed on Xuggler is initited; API block, where ctul opertion hppens, 3) collborrs Fcebook content cretedsign is keptin in ir instnce, which is pssed on srge cloud, vi MCCO srge mnger. ccount see ctions of 4.4 Usge ; A typicl scenrio for using system is shown s below: 4) A collborr recommends relted 1) signs in system using her s comments on ; Fcebook ccount; 9 2) uses serch functionlity find s. Fig. 7 : User interfce: commenting recommending 5) bord orgnizes collborrs This will show how serch results re presented Fig. 7. User interfce: commenting recomrelted s. input bsed on isdifferent pics. Wen, will she howccount initited; er Fcebook pssword. mending relted s 3) how collborrs sign in ir Fcebook showobtin uses ccount he cn collection 5. System Implementtion see ctions of ; cidtes for recommendtions furr develop 4) A collborr recommends relted s comments ge tsk performing serch MediWise Cloud [5] consists of portl, MCCO,. onin ; SCASP. SCASP includes collbortive ng function shring socil communities, 5) bord orgnizes collborrs input bsed5 on S YSTEM I MPLEMENTATION component, recommender whole pics. is s perofsuch s different Youtube Metcfe etc. shown We will show how uses editing components, which re implemented s Web services MediWise [5] components consists cn of beportl, ormed severl steps. tht, First, develop u- sign recommendtions furr. t SS-level.Cloud Ech of se hosted on in Fig. 6. Following collborrs whole is performed severl efficiently MCCO [10][11] on Amzon EC2 Amzon MCCO, SCASP. SCASP includes hor signs in system inputting her l in system, where opertions of S3 cloud infrstructure. content providers using MCCO steps. First, signs in system inputting is her visible n, collborrs, Fcebookre ccount pssword. she cn obtin cn host medi-relted content such s udio-visul collection cidtes for tsk collborrs prticipte in ctivities files t different geogrphicl loctions. s performing of serch function in shring socil contriburs use MediWise portl ccess, such s recommending serch, mnipulte discuss content. communities, such s Youtube components, Metcfe etc. s shown in Fig. 6. Following tht, s collborrs commenting shownsign in in Fig.7.system, Rec MediWise Portl uses strd Model-Viewwhere opertions of re visible Controller (MVC) pttern respond requests from ommendtions re orgnized in bord collborrs, collborrs prticipte in ctivities web ppliction using Spring frmework provide bsedsuch onsir pics. Finlly, support MVC interctions. controller mnges recommending components, commenting s shown in Fig. 7. Recommendtions orgnized processing from Web ppliction including home pge, composes s produces re complete in bord bsed on ir pics. Finlly, plyer pge, cretion pge, content bsedcomposes on recommendtions. s produces complete msh up pge locl dtbse. All opertions bsed on recommendtions. 5.1 MediWise Cloud dtbse e is m ty supported MediWise Portl MCCO re implemented using RESTful Web APIs. For exmple, user login comments re implemented using Spring- Socil Frmework APIs, MCCO opertions re supported Amzon EC, Amzon S3 SmrtGWT APIs. We lso developed number of APIs mnge bckend system, i.e., perform complex opertions such s performing content serch -relted opertions. Our bckend system comprises of MediWise Dtbse. Fig. 6 : User interfce: serching for s for Fig. 6. User interfce: serching composing. for s for composing MediWise dtbse is MySql dtbse tht hs its own REST service query, fetch updte dt relted user, content sries. REST service provides services fetch metdt in form of keywords for s using queries such s: select ll s or select ll s with specific keyword. Fig. 8 show three tble out of tl of eight from MediWise dtbse used mnipulte content its relted

8 8 : 76 A Cloud-bsed Collbortive Video Sry Authoring Shring Pltform sries. Using se tbles, informtion relted content such s where it is sred, its relted keywords owner is XX retrieved. Furr, content relted prticulr sries cn 10 lso be esily mnipulted. compoediting s Web compo- CCO [] infrs- MCCO ch l lociburs serch, trd reliction ide ntroller pplicplyer t msh opertl ESTful d Springrtions zon S3 loped nd sysns such -relted rises of tbse y, fetch er, conrovides form of es such os with e tble iwise its rmtion sred, trieved. Fig. 8 : dtbse schem for content sries. Fig. 8. dtbse schem for content As mentioned previously, re re severl or tbles sries. in MediWise dtbse include dt relted As mentioned previously, re re severl or tbles in MediWise dtbse include dt relted cloud providers, qulity of service sttistics users. Due ske of brevity, we do not discuss m in pper Exmple: serching keyword In this exmple, consider cse wher user using MediWise portl, enters keywords relted prticulr s he/she is looking serch for. using user MediWise submits portl. query ppliction. controller mnges processing of query: first sending query dtbse server vi REST cll. dtbse server, using utility clss, queries MySql dt converts SQL result set in n XML pckge tht is returned bck controller. Lstly, MVC model is populted with objects derived from XML n displyed on web pge with results. Fig. 10 shows returned XML response fter mking REST cll. Fig. 9 : Sequence digrm showing process of content 5.2Fig. Fcebook 10 : Returned integrtion XML response regrding content As mentioned using previously, REST cll. s contriburs logs in MediWise Portl comments on using socil medi, CSI Journl in our of Computing cse Fcebook. Vol. 1 Our No. web 3, 2012 ppliction intercts with Fcebook updte user s timeline retrieve comments dded cloud providers, qulity of service sttistics end-users. Due ske of brevity, we do not discuss m in pper Exmple: serching keyword In this exmple, consider cse wher n end-user using MediWise portl, enters keywords relted prticulr s he/she is looking for. user submits query ppliction. controller mnges processing of query: first sending query dtbse server vi REST cll. dtbse server, using utility clss, queries MySql dt converts SQL result set in n XML pckge tht is returned bck controller. Lstly, MVC model is populted with objects derived from XML n displyed on web pge with results. Fig. 10 shows returned XML response fter mking REST cll. 5.2 Fcebook integrtion As mentioned previously, s contriburs logs in MediWise Portl comments on using socil medi, in our cse Fcebook. Our web ppliction intercts with Fcebook updte user s timeline retrieve comments dded timeline post. When is viewed vi web ppliction, n entry is creted Fig. 11 : Sequence digrm showing process of MediWise Portl interction vi web browser with Fcebook. 5.3 Content mshup editing Anor novel feture of MediWise Cloud is

9 Chen Wng, et. l. 8 : 77 bility ggregte content from locl remote sources such s YouTube. metdt relted new s is n sred in dtbse such tht end users cn esily serch new content, ssocite content sries comment on m. In prticulr, metdt from selected, such s title, description keywords, is extrcted used populte editing web pge. end-user cn n modify this metdt s y require sve informtion in locl dtbse. n becomes prt of system. contributers cn n mnipulte new content. Fig. 12 shows sequence digrm for metdt retrievl for YouTube s its srge in MediWise dtbse. 6. Experimentl Anlysis We considered scenrio where n end user t CSIRO, Cnberr, Austrli, performed merge split opertions exmple, we performed experiments merging 2 5 files ger. For results evlution, we merged files of different sizes romly considered two metrics i.e., file merge time file uplod time. merge split opertions were performed using McBook Pro running OSX on Intel i7 2.2 GHz processor with 4GB RAM. Tble 1 shows results relted merge uplod opertion s performed using MediWise portl. We conclude from results tht both merge uplod time depends minly on file sizes not on number of files considered for opertions. After ech merge opertion, file ws umticlly uploded Amzon S3 dtcenter in USA i.e.,us-est-1. We note tht s loction of dtcenter is fr from Austrli uplod time is quite high rnging from 124 seconds pprox seconds pprox. depending on file sizes. Tble 2 shows results relted split opertions.we considered three files of vrying sizes durtion. We uploded performed split opertions Amzont S3 different dtcenter time durtions. in USAFor i.e.,us-est-1. exmple, for file Wewith note 2624 tht seconds s durtion, loction we performed of split opertion crete new file of length 262 seconds, dtcenter is fr from Austrli uplod i.e., 10% of file ws considered. We concluded from our time results is tht quite high time rnging perform split from (split 124 time) seconds depends on pprox. length of 2435 finl seconds pprox. required depending end user. onit is file lso sizes. depended on qulity of. For exmple, lower qulity Tble 2 shows (file 1)will tke results lesser relted time compred split opertions. higher qulity We considered (file 3). Finlly, three fter files of split vrying opertion, new files were uploded Amzon S3. uplod time sizes durtion. We performed split is proportionl file size. opertions t different time durtions. For exmple, 7. Conclusion for file with 2624 seconds durtion, we performed growing split ubiquity opertion of Internet crete cloud newcomputing file ofis length hving significnt 262 seconds, impct i.e., on 10% medi-relted of fileindustries, ws considered. which re using Wem concluded s medium from enble our results cretion, tht serch, mngement, time perform consumption split (split of ir time) contents depends online. We clerly rticulte rchitecture of socil content on length of finl required shring pltform its integrtion with MediWise end user. It is lso depended on qulity cloud content orchestrr pltform. proposed pltform ofenbles. collbortive For exmple, lower qulity system over socil (file networks, 1)will tke demonstrted lesser time our cpbility compred of hling higher qulity issues in cloud (file environment. 3). Finlly, We lso fter conducted split some opertion, experiments, which newfurr filesproves were uploded fesibility of our Amzon pproch. S3. uplod time is proportionl In this rticle, we presented some initil thoughts on file size. collbortive multimedi cloud computing our preliminry reserch in this re. reserch development efforts in multimedi cloud computing is t erly stge mny 7 CONCLUSION problems still need investigtion. For exmple, ensuring QoS for different growingmultimedi ubiquityopertions of Internet (e.g., production, cloud srge, computing digitliztion, is hving consumption) significnt needs impct furr onreserch. medi-relted Or open reserch industries, problems which in this re re using includes m medi security, loction wre medi plcement, medi overly, s medium enble cretion, serch, mngement, consumption of ir contents peer-peer rchitecture for engineering medi services. online. We clerly rticulte rchitecture of socil content shring pltform its integrtion with MediWise cloud content orchestrr pltform. proposed pltformcsi enbles Journl collbortive of Computing Vol. 1 system over socil networks, demon- No. 3, 2012 strted our cpbility of hling u- CSI JOURNAL COMPUTING, VOL. XX, NO. XX, XXXXXXXXX 12 Fig. 12 : Sequence digrm showing process of Fig. 12. Sequence digrm showing process of fetching serch. srge of YouTube met- fetching srge of YouTube metdt for dt for serch. using MediWise Portl. We considered different file sizes: 2 MB, 20 MB, 40 MB, 351 MB, 478 MB. We lso considered different combintions of merge split opertions. For split opertions using MediWise Portl. We considered different file sizes: 2 MB, 20 MB, 40 MB, 351 MB, 478 MB. We lso considered different combintions of merge split opertions. For exmple, we performed experiments merging 2 5 files ger. For results evlution, we merged files of different sizes romly considered

10 8 : 78 A Cloud-bsed Collbortive Video Sry Authoring Shring Pltform Video File1 (2MB) TABLE 1 Performnce nlysis of merge uplod opertions perting 5 different files. Y signifies tht file is used for merge opertion. Video File2 (20MB) Video File3 (40MB) Video File4 (351MB) Video File5 (478MB) No of Videos File Merge Time (sec) File Uplod Time (sec) Y Y N N N Y N Y N N Y N N Y N Y Y Y N N Y Y Y Y N Y Y Y Y Y TABLE 2 Performnce nlysis of split uplod opertions. Video % of split (durtion) Split Time(sec) Uplod Time(sec) Video 1, 40 MB, 60 sec 10 (60 sec) Video 1, 40 MB, 300 sec 50(300 sec) Video 1, 40 MB, 540 sec 90(540 sec) Video 2, 367 MB,2624 sec 10(262 sec) Video 2, 367 MB,2624 sec 50(1312 sec) Video 3,713 MB,4957 sec 10(496 sec) Video 3,713 MB,4957 sec 50(2479 sec) References [1] [2] Gulrukh Ahnger Thoms D. C. Little. Aumtic composition techniques for production. IEEE Trns. on Knowl. Dt Eng., 10(6): , November [3] Michel Binchi. Aumtic production of lectures using n intelligent wre environment. In Proceedings of 3rd interntionl conference on Mobile ubiquius multimedi, MUM 04, pges , New York, NY, USA, ACM. [4] A. Engstr om, M. Esbj ornsson, O. Juhlin. Mobile collbortive live mixing. In Proceedings of 10th interntionl conference on Humn computer interction with mobile devices services, MobileHCI 08, pges , New York, NY, USA, ACM. [5] D. Georgkopoulos, R. Rnjn, K. Mitr, X. Zhou. Mediwise designing smrt medi cloud. In Interntionl Conference on Advnces in Cloud Computing, [6] E. Hmmer-Lhv, D. Recordon, D. Hrdt. outh 2.0 iztion procol. Network Working Group Internet- Drfts, [7] Richng Hong, Jinhui Tng, Hung-Khoon Tn, Chong-Wh Ngo, Shuicheng Yn, Tt-Seng Chu. Beyond serch: Eventdriven summriztion for web s. ACM Trns. Multimedi Comput. Commun. Appl., 7(4):35:1 35:18, December [8] Jinho Meng Shih-Fu Chng. Cveps - compressed editing prsing system. In Proceedings of fourth ACM interntionl conference on Multimedi, MULTIMEDIA 96, pges 43-53, New York, NY, USA, ACM. [9] Shi-Yong Neo, Yunyun Rn, Hi-Kit Goh, Ynto Zheng, Tt-Seng Chu, Jinto Li. use of pic evolution help users browse find nswers in news corpus. In ACM Multimedi, pges , [10] R. Rnjn, K. Mitr, D. Georgkopoulos. Mediwise cloud content orchestrr. Journl of Internet Services Applictions (Accepted Ocber 2012). [11] R. Rnjn, K. Mitr, S. Sh, D. Georgkopoulos, A. Zslvsky. Do-it-yourself content delivery network orchestrr. Web Informtion Systems Engineering-WISE 2012, pges , [12] C. Wng, M. Meng, X. Zhou, R. Rnjn. A socil network bsed collbortive composition pltform. Interntionl Conference on Service Oriented Computing-ICSOC [13] Xio Wu, Chong-Wh Ngo, Qing Li. Threding udocumenting in news s. IEEE Signl Processing Mgzine, 23(2):59-68, [14] Bo Yng, To Mei, Xin-Sheng Hu, Linjun Yng, Shi- Qing Yng, Mingjing Li. Online recommendtion bsed on multimodl fusion relevnce feedbck. In Proceedings of 6th ACM interntionl conference on Imge retrievl, CIVR 07, pges 73-80, New York, NY, USA, ACM. [15] Hui Yng, Lekh Chisorn, Yunlong Zho, Shi-Yong Neo, Tt-Seng Chu. Videoq: question nswering on news. In ACM Multimedi, pges , [16] Xingmin Zhou, Lei Chen, Xiofng Zhou. Structure tensor series-bsed lrge scle ner-duplicte retrievl. IEEE Trnsctions on Multimedi, 14(4): , [17] Xingmin Zhou, Xiofng Zhou, Lei Chen, Ynfeng Shu, Athmn Bouguetty, John A. Tylor. Adptive subspce symboliztion for content-bsed detection. IEEE Trns. Knowl. Dt Eng., 22(10): , [18] Xingqun Zhu, Jinping Fn, Ahmed K. Elmgrmid, Xindong Wu. Hierrchicl content description summriztion using unified semntic visul similrity. Multimedi Syst., 9(1):31-53, 2003.

11 Chen Wng, et. l. 8 : 79 About Authors Chen Wng received his Ph.D. from Nnjing University. He is senior reserch scientist t CSIRO ICT Centre, Austrli. His reserch interests re primrily in distributed, prllel trustworthy systems. His current work focuses on ccountble distributed systems, resource mngement in cloud computing dem response lgorithms in smrt grid. Rjiv Rnjn received his Ph.D. from University of Melbourne (2009). He is scientist t CSIRO ICT Centre, Austrli. His reserch interests re primrily in cloud, grid, peer--peer, service computing systems. His current work focuses on QoS optimiztions in multimedi cloud computing systems. Xingmin Zhou received Ph.D. degree in computer science from University of Queensl, Brisbne, Austrli, in Currently, she is Reserch Scientist t CSIRO ICT Centre, Austrli. Her reserch interests include multimedi dtbses strems, query processing query optimiztion, socil network nlysis mining, cloud computing. Krn Mitr is Reserch Intern in CSIRO Cnberr ICT Center, Austrli. His reserch interests include qulity of experience modelling, mesurement prediction, next genertion wireless networks, mobility mngement procols, cognitive wireless networks context-wre computing. Suhit Sh is Reserch Intern in CSIRO Cnberr ICT Center, Austrli. He is Msters student t College of Engineering Computer Science t Austrlin Ntionl University, hs been in softwre development industry for pst 3 yers, in which he hs mde contributions Softwre Systems mde for Insurnce firms, fter which he hd been prt of tem developing PC cross-pltform mobile gmes. Meng Meng is Reserch Intern in CSIRO Cnberr ICT Center, Austrli. She hs completed Msters degree in informtion technology from University of New South Wles (2012). Dimitrios Georgkopoulos is currently Reserch Direcr t CSIRO ICT Centre, Austrli, where he leds Informtion Engineering Lborry. Prior CSIRO, Dimitrios held reserch mngement positions in severl industril lborries in US, including Telcordi, where he helped found Telcordi s Reserch Centers in Austin, Texs, Poznn, Pol, Microelectronics Computer Corportion, GTE (Verizon) Lborries, Bellcore. He is Adjunct Professor t Austrlin Ntionl University. Dimitrios hs led mjor (multiyer/multi-million) projects in four continents he hs extensively published in res of business process mngement, complex event processing, collbortion mngement, dt mngement. He served s Generl or Progrm Chir of dozen mjor conferences s well s mny or smller conferences or workshops. Dimitrios twice received outsting pper wrds from IEEE Computer Society. He ws nominted for Computerworld Smithsonin Awrd in Science, received Verizon Excellence Awrd. Lizhe Wng is professor t Center for Erth Observtion Digitl Erth of Chinese Acdemy of Sciences (CAS) ChuTin Scholr Chir Professor of Chin University of Geoscience (CUG). Dr. Wng currently leds group in CAS with focus of HPC dt-intensive computing for erth observtion pplictions. He lso co-leds scientific computing lb in CUG with focus on scientific cloud computing GPGPU computing. Dr. Lizhe Wng is n IEEE senior member ACM professionl member. Peter w hs been in computing industry for over 30 yers, hs worked in Cnberr, interstte overses. During his creer he hs worked on lrge number of diverse projects including stellite trcking fishing vessels in South Pcific, sving hisricl documents from World Wr Two working on severl personnel systems. He hs spent lst three yers s project mnger for Flood Extent Moniring which stellite imgery identify surfce wter produce dily flood mps for Austrli. flood mps re combined with CSIRO s Digitl Elevtion Model of Austrli estimte volume of wter within flood. He is currently working on number of projects including Mediwise furr flood extent modelling work long Murry River.