A Green Video Control Plane with Fixed-Mobile Convergence and Cloud-RAN

Transcription

1 27 29th Internatonal Teletraffc Congress A Green Vdeo Control Plane wth Fxed-Moble Convergence and Cloud-RAN Ramon Aparco-Pardo and Lucle Sassatell Unversté Côte d Azur, CNRS, I3S - Sopha Antpols, France Emals: {raparco,sassatell}@3s.unce.fr Abstract Vdeo traffc s a key-challenge for fxed and moble operators facng varable and massve load and varety of Over- The-Top (OTT) vdeos. Energy consumpton s also a heavy Opex component, where the Fxed-Moble Convergence s a promsng soluton, bult on on economcal optc fbers and LTE baseband operatons consoldaton. By combnng Future Internet Archtectures (FIA) prncples such as ubqutous cachng, SDN and NFV, and FMC, we propose a complete, fully dynamc setup whch optmzes both for power consumpton and Qualty of Experence (QoE), by choosng proper nfrastructure (turnng on a mnmum number of computng and networkng equpments) and operatonal (routng and cachng) confguratons. Our control plane named s scalable thanks to data analytcs technques, and fully reactve to the dynamcs of load and catalog both n tme and space. Numercal assessments n realstc settngs show power gans of up to 3% whle the scores on dfferent QoE metrcs are maxmzed. Enablng elastc co-locaton of caches and rado base-band operatons turns out to be crucal for both power and QoE objectves. I. INTRODUCTION ISPs, and Moble Network Operators (MNO) specfcally, are facng servces varety and traffc ncreases. Telcos nfrastructure needs to smultaneously support these servces, ensure ther requred QoE, and possbly monetze them. Vdeo traffc s n partcular a key-challenge for telcos, due to the share of vdeo streamng n the Internet traffc expected to reach 82% of all IP traffc by 22 []. For MNOs, the network segments vulnerable to congeston and hamperng QoE are the backhaul and Rado Access Network (RAN). Ths s specfcally due to IP tunnelng used to make the backhaul transparent (3GPP standard). To meet the QoE requrements, a frst strategy s to skp the congested areas by employng n-network cachng (e.g., Qstream startup), where content s stored at PDN-GWs or enodebs n order not to break the moblty management mposng tunnellng between those end-ponts. Proxy solutons are also used by telcos, n partcular to resze web content for moble devce to save bandwdth. The Evolved Packet Core (EPC) nfrastructure shall be overhauled to meet the challenges brought by 5G servces. Optcal nfrastructure and fne-graned (flow-based and locaton-based) montorng to feed real-tme decsons are key aspects. To ths am, Bg data s seen as a top strategc nvestment by a number of telcos 2. Key-enablers to ths much Ths work was partly funded by the French Government through the Investments for the Future Program reference ANR--LABX-3-. e.g., 2 McKnsey: needed automated orchestraton are SDN and NFV whch, by decouplng control plane from data plane and software from hardware, respectvely, allow to leverage the flexblty and scalablty of cloud resources to provde a fne-graned and responsve control of the flows, whle scalng up or down the computatonal resources (e.g., the Elemental TM company provdes software-defned vdeo deployment to IPTV provders). To solve the problems (nterference due to spectrum lmtatons and base statons densty, moblty, etc.) at the wreless lasthop, software-defned centralzed control s also planned to enable 5G, and s generally referred to as Cloud-RAN, where both controllers and rado elements are hosted n the cloud [2]. The concept of Fog computng brngs together these prncples to add a herarchy of elements between the cloud and endpont devces [...] to meet these challenges n a hgh performance, open and nteroperable way 3. Energy consumpton on another hand s a heavy Opex component. One promsng soluton for energy-effcent aggregaton/access s the Fxed-Moble Convergence (FMC) prncple [3] (see Fg. : Left). The dea s to manage jontly the heterogeneous access technologes (e.g., FTTH/B, WF, 4G) to consoldate wthn the cloud/fog fxed and moble optcal head-ends as well as most of Base Staton (BS) (base-band) processng. Ths s known as Base Band Unt (BBU) hostng, to mutualze usage of the physcal resources (optcal/electronc networkng equpment and coolng). However, dong so n turn entals costs n bandwdth and opto-electronc-opto conversons (to compute the base-band dgtal sgnals up n the network way before the BS), thereby requrng fne control to truly yeld energy savngs wthout loss of performance. We desgn a control plane addressng OTT vdeo dstrbuton for ISPs/MNOs facng varable and hgh vdeo loads. By combnng FIA prncples such as ubqutous cachng, SDN and NFV, and FMC, we propose a complete, fully dynamc setup whch optmzes both for power consumpton and QoE, by choosng proper nfrastructure (turnng on a mnmum number of computng and networkng equpments) and operatonal (routng and cachng) confguratons. Our contrbutons are: Based on a realstc power model of mcro-data Center (DC) and networkng equpments, hgh-level vdeo transcodng and low-level base-band LTE operatons, we frst model the mult-objectve (QoE and power) optmzaton problem whch accounts for reactve cachng. Indeed, consderng recent /7 $3. 27 ITC DOI.9/ITC

2 fndngs advocatng for server-controlled vdeo rate through contnuous-rate encodng (as opposed to DASH representatons selecton), we address the case of massve and volatle OTT content for whch ISPs do not plan pre-fetchng as for Subscrpton-based VoD (SVoD), and rely on Fog computng. A dynamc orchestraton wth nfrastructure-level and operaton-level re-optmzatons s devsed from a prmal decomposton to track load varatons n tme, space and content features. The scalablty for massve vdeo data s addressed wth clusterng technques whch prove effcent n smulatons. Extended numercal smulatons n realstc settngs show power gans of up to 3% whle the scores on dfferent QoE metrcs are maxmzed thanks to elastc consoldaton of caches/transcoders and rado base-band operatons. A comparson wth ICN s also drawn. After the related works are presented n Sec. II, Sec. III and IV detal the node and power consumpton models. Our control plane s detaled n Sec. V and numercal results are analyzed n Sec. VI before the concluson. Fg. : Left: the FMC concept. Rght: A mcro-dc node. II. RELATED WORKS We organze ths secton nto three paragraphs, each coverng one aspect n our work: jont routng and cachng management, congeston mnmzaton and energy-effcent cachng. To mnmze transmsson costs, [4] expresses the cachng and routng subproblems separately, and derves routnes for each. So-called tme-slot routng s used to avod greedly routng each ncomng request by delayng t a bt. In [5], Ruz et al. desgn a management system for a telco-cdn to serve a certan set of channels/contents. They express separately dfferent subproblems amed at reconfgurng the vrtual resources to mnmze the lnk costs, HTTP server and packager resources, establshng connectons and re-allocatng users. Whle we also explctly optmze for vdeo qualty wth a more refned QoE model, we consder easer-to-mplement reactve cachng for non-svod content and proper prmal decomposton from the mult-objectve problem to come up wth subproblems wthout heurstcs, as well as bundle-ofrequest routng (see Sec. V). In [5], vdeo load predcton s used (whch our plane does not encompass for smplcty, but can easly beneft from). In [6], the ablty to cache and transcode s consdered at the enodeb for MPEG DASH dscrete representatons. Only targetng the sum of the vdeos qualtes, the authors determne heurstcally the cachng and rate decsons, and the schedulng both between the core and the enodeb, and the enodeb and user va the wreless LTE lnk. In [7] (close to [8]), the problem of whch vdeo representaton to cache s thoroughly nvestgated when MPEG DASH s used. The authors show the effectveness of cachng the one hghest representaton per vdeo, so that popular vdeos are served wth hgher qualty from the cache. The work however consders nether the last-hop bandwdth lmtaton where lower rates vdeos are needed nor the ncurred cost of transcodng. By consderng the Proxy soluton [9] (each proxy s a server/cache/transcoder generatng contnuous vdeo rates), our framework s releved from the burden of DASH representaton choces. In [], the concept of Informaton-Centrc Networks (ICN) s employed to show that, n the backhaul of Orange France, HTTP traffc can be reduced by 6% or more by addng only a few hundreds of GBs of storage overall. However Mult-Path (MP) transfer for vdeo streamng has been shown to be trcky [], [2], the more so wth mult-source ([3] resorts to H.264/SVC and not AVC for that reason). Energy-effcent cachng (or CDNs) has also been nvestgated (not for vdeo specfcally), n [4] analytcally, and n [5] who consder that the statc power component can be taken off by turnng off lnks and network cards, whch we also consder among other levers. In [6], an onlne cache-cooperaton mechansm s desgned so that the nodes make ther cachng decsons based on ther local estmate of the global energy beneft. In [7], we sketched the dea of leveragng FMC wth vdeo dstrbuton. We however dd not consder cachng, transcodng, decomposton and reactveness to handle real requests and reconfgure. Fnally the potental of 5G Cloud-RAN archtectures co-locatng vdeo and BBU processng was suggested n [8]. From ths dea we buld a complete control plane enablng such potental. III. NODE AND POWER CONSUMPTION MODELS We assume future access/aggregaton networks based on fog computng, therefore assumng each node s a mcro-dc (represented n Fg. : Rght) equpped wth a few servers (wth storage and CPUs), electronc and optcal swtchng capabltes. The power consumpton models of each of these elements s detaled n our recent survey [9]. In a nutshell, each s the sum of a statc and dynamc component. For servers, the latter s dependent on the number of actvated cores, counted n number of Vrtual CPUs (vcpus). For electronc swtches and routers, t depends on traffc, and owng to the values n [9], we neglect t. The whole consumpton of the optcal equpment can be neglected as well. The number of vcpus, entalng the number of servers swtched on, and the number of actvated swtches and IP routers (where not only the optcal cross-connect s used) therefore determne the total power consumpton. 29

3 Each server may perform vdeo transcodng and base-band tasks, where a task s a Vrtual Machne (VM) requestng d vcpus. A base-band VM hosted at a server s smply the vrtualzed BBU, or Vrtual Dgtal Unt (VDU), of at least one BS (co-located at the BS n today s LTE RAN). The computatonal cost assocated wth each type of task s derved n GOPS and pass-marks, then translated nto number of vcpus n [9]. The LTE traffc s transported between the moble end node and the possbly deported BBU by means of Common Publc Rado Interface (CPRI). That mples to transform the rado band sgnals from analog to dgtal, entalng CPRI bt rate C CPRI hgher than C LT E. IV. VIDEO PROCESSING AND QOE MODEL We characterze the users QoE as a functon dependng on the codng parameters (rate and resoluton) and on the vdeo content, by means of the Vdeo Qualty Metrc (VQM) [2], shown to correlate wth human vsual percepton. We consder four resolutons: 36p, 72p, 8p, and26p (4K), and three content types wth ncreasng complexty (toon, move, and sport). From the QoE functons (VQM vs. encodng rate), we extract a lnear approxmaton to later obtan a lnear problem (Sec. V-A). The reader s referred to [9] for detals. Whle MPEG-DASH ams at adaptng the served vdeo qualty to the avalable network resources by provdng versons encoded at certan dfferent rates, the storng overhead and dffculty of representaton choces wth a dscrete set has led to a sequence of works snce 23 showng that contnuous nstead of dscrete bt rate adaptaton enables hgher QoE at the clent, specfcally n moble networks [9], [2], [22]. In our work, we leverage these fndngs and assume the clent adaptaton and cache polcy management of the Proxy soluton [9] (also detaled n Sec. V-B). We enforce that, upon handlng a request, the Proxy nstance fetches (f needed) the hghest-btrate vdeo n resoluton 8p (resp. 4K) fthe request s for 32p, 72p or 8p (resp. 4K). DCT-based representaton s stored [9], from whch transcodng to any lower resoluton and btrate can be made. V. DESIGNOFTHEVIRCA CONTROL PLANE The key components of the Vrtualzed Infrastructure, Routng and Cache Assgnment () control plane are presented. Frst s formalzed as Mxed Integer Lnear Programmng (MILP). We next show how the vdeo catalog only serves as a formalsm and s not a scale lmtaton thanks to data analytcs. The dynamc orchestraton s then desgned based on a prmal decomposton. Fnally we detal how the optmzaton outcome s used to handle requests and how montorng s performed. The terms Proxy, server, transcoder and cache are used nterchangeably thereon. A. Optmzaton formulaton of For an access/aggregaton network usng the mcro-dc node archtecture n Fg. and for a gven vdeo catalog, we search for the allocaton of the VMs performng rado and vdeo processng, the content cache placement (proxy nstance selecton wth reactve cachng) and the vdeo stream routng maxmzng users QoE and mnmzng consumpton, jontly. Let G(N, L) be the graph of a vrtual topology of optcal paths (lghtpaths). The capacty of vrtual lnk l L s the number c l of lghtpaths n the bundle. We consder a set E F N of optcal head-ends connectng FTTH/B subscrbers, and a set E M N of cellular BS, and E = E F E M.The (regonal) Pont of Presence (PoP) s the hghest herarchy level. All the nodes are assumed to be composed as n Secton III. We consder the CPRI for transportng these baseband sgnals from the BBU hotels (VDU) to the base statons. The constrants on CPRI routng are the same as n [23]. As moble processng n the cloud requres strct delay lmts, any lghtpath from node N to e E cannot exceed a certan reach. The possble set of lghtpaths s L CPRI L. Thesetof IP paths s referred to as P. The set of paths: jonng a node par (, j) N N s denoted as P j, traversng a vrtual lnk l L as P l, traversng a node N as P node,comng nto a node N as P n, and gong out of a node N as P out. The catalog of contents s M. The demand s the average number v ems of parallel requests for content m M ssued from e E, at resoluton s S = (36p, 72p, 8p, 4K). Instead of havng a request for a commodty, a commodty {ems} s hence a bundle of those. Ths coarser granularty allows havng MP routng at the level of bundles, f not at the level of request (each usng a sngle path, see Sec. V-D). As shown n [9], we consder the transcodng power only depends on the output resoluton and vdeo type, whereby the defnton of d ms n Table I. A content requested after the last catalog update (.e. not present n the current verson) s denoted as u / M. Its explct consderaton enables usng optmally the resources not actually allocated for the foreseen catalog contents. Tables I and n II gve all detal. Tradng between power savngs and QoE mprovements s a mult-objectve optmzaton that we address by scalarzaton n Eq. (a) wth parameter γ. We could use a Nash Barganng Soluton (NBS) formulaton to remove the parameter by mnmzng the objectves rato, but keepng the problem lnear helps convergence speed even n hgh-dmensonal problems. The motvaton of model n Eq. () s ntroduced n SectonsIIIandIV,anddetaledn[9]. mn power γqoe {x,y,f,vf,h,z,g,r,k,w,t} power = P CPU + P EGS + P IPR (a) (b) P CPU = [6.4k +.47(w + t )] (c) N P EGS = 22g, P IPR = 455r (d) N N QoE = α ms j + α s x eus j (e),j,j m M ) QoE constrants: The lnear approxmaton of the QoE does not provde ntrnsc farness among the bundles, as the concave functon does. We therefore add the next constrants for boundng the QoE for each trplet {ems}: j j b ms mnv emsf ems, N,e E,m M,s S (2a) 3

4 Name Descrpton λ m R + Posson arrvals ntensty of request for content m M at node N v ems R + Average number of parallel vdeo requests for content m M at resoluton s S from end pont e E wd ems R + Average watchng duraton of content m M at resoluton s S from end pont e E o ems R + Traffc estmate needed for reactve cachng at node N for vdeo requests of content m M at resoluton s S from end pont e E b R + Mnmum bandwdth to have between PoP and node b ms mn (bms max ) R+ α ms(α s) R + N to ensure connectvty Encodng bt rates correspondng to the mnmum (resp. maxmum) qualty for content m M at resoluton s S (n Mbps) Parameter for QoE lnear approx. at resoluton s S (resp. for unknown content) Ce LT E R + Overall capacty of LTE rado lnks at base staton e E m (n Mbps) C WDM R + WDM channel capacty (n Mbps) C IPR R + IP router swtchng capacty (n Mbps ) C EGS R + Ethernet ggaswtch swtchng capacty (n Mbps) c l Z + Capacty of vrtual lnk l (n number of lghtpaths) s m R + Sze of content m M (Proxy representaton sze) S R + Total storage capacty at node N d e R + Number of vcpus (CPU fracton) requred for the BBU tasks for BS e E m (.e. number of VDUs processng the traffc destned to node e) d ms R + Number of vcpus (CPU fracton) requred to produce a representaton of content m M at resoluton s S from ts stored verson d us R + Number of vcpus (CPU fracton) requred to produce a representaton of content u/ M at resoluton s S from ts stored verson T Z + Number of cores (vcpus) per physcal server K Z + Number of physcal servers per data center node j j Name TABLE I: MILP notaton. Input Parameters Descrpton j R + Total traffc rate for request bundle {ems} served from node N to node j N (n Mbps) p R + Traffc rate for request bundle {ems} served on path p P (n Mbps) x eus j R + Total traffc rate for request bundle {eus} served from node N to node j N(nMbps) x eus p R + Traffc rate for request bundle {eus} served on path p P (n Mbps) y p R + Background traffc on path p P out PoP (n Mbps) f ems [, ] Fracton of requests v ems served from node N vf eus R + Number of requests for non-cataloged content served from node N h m [, ] Ht rato of content m M at node N (probablty for to store m) z e {, }, f node N hosts BBU of base staton e E, otherwse g {, }, f node N s swtched on;, otherwse r {, }. f IP router used at node N;, otherwse k Z + Number of actve servers at node N w Z + Number of vcpus at node N performng the BBU processng tasks t Z + : Number of vcpus at node N performng the vdeo transcodng (Proxy) tasks j x eus j TABLE II: MILP notaton. Decson varables b ms maxv emsf ems, N,e E,m M,s S (2b) b s mnvf eus, N,e E,s S (2c) j x eus j b s maxvf eus, N,e E,s S (2d) 2) Routng constrants: N, m M u p P j p p p P j C LT E e z e j, e E M,j N (3a) = j, j =, y p p P PoP y p =, ( p P l p P node m M u p P n Pout ( m M m M u e E,m M u s S, (, j) N N e E F,m M u, s S N,j N \{e} (3b) (3c) o ems f ems + b, N (3d) j N,p / P IP PoPj (3e) p + y p ) C WDM c l l L, (3f) ( m M u ) p + y p C IPR r N, (3g) ) p + y p C EGS g N, (3h) In (3d), b s set to a low value (e.g. Mbps) for fxed end nodes and BBU hotels, to ensure connectvty wth the PoP. Contrary to prevous works [5], [7], [24], [6], we are able to model the reactveness of our cachng polces (whch releves from pre-fetchng to consder any OTT servce) by estmatng the requred bandwdth w PoP from PoP to cache to serve cache msses. It was shown n [25] that FIFO and LRU cachng polces can have ther ht ratos modeled by those of TTL caches wth proper tmer value. Let us consder LRU to approach the consdered Proxy polcy. From Lttle s law we get (see notaton n Table I): λ m = es v ems f ems wd ems The ht rato s gven by [25]: h m = exp( λ m T ), wth T such that m ( exp( λ mt )) s m = S. We therefore get w PoP m ( h m)λ m s m. Approxmatng ( h m ) wth a constant κ (between.5 and.8 as n Orange traces [, Sec. II]), we obtan o ems 3) VDU placement constrants: = κ vemssm wd ems. z e =, e E F, N (4a) z e =, e E M, E \{e} (4b) z e =, e E M, N L CPRI e = { } (4c) z e =, e E M (4d) N M d ez e w, N (4e) 3

5 4) Transcoder and cache placement constrants: f ems, e E,m M,s S (5a) N h m f ems, N,e E,m M,s S (5b) s mh m S, N (5c) m M ( m M d msv emsf ems 5) VM placement constrants: + d usvf eus ) t, N (5d) w + t Tk, N (6a) k Kg, N (6b) B. Scalng up catalog wth vdeo analytcs Our system targets OTT dstrbuton, where ISPs/MNOs are faced wth varable and hgh vdeo loads of large and varable catalogs, for whch content pre-fetchng s not planned. Other works employng MILP formulaton for cachng and routng assgnments are plagued wth the curse of dmensonalty: a catalog of only content s consdered n [24], [5] consders a lmted number of channels and heurstcs, whle [7] desgns a heurstc reactve cachng polcy based on the nsghts from the low-dmensonal MILP. We take a dfferent approach. We post that key features mpactng the optmzaton problem (and not the content ds) are necessary and suffcent. Ths approach to manage hgh and varable volumes of very dverse vdeos calls for data analytcs to extract the only nformaton necessary for the problem at hand, as exposed below. It proves hghly effcent to fnd QoE-power trade-offs wthn a few seconds to mnutes as detaled n Sec. VI. The sze of the MILP depends on the number of ems {end node d,content d,resoluton}. Before each optmzaton round, a clusterng s performed to collapse the requests nto groups meanngful to the optmzaton by revealng organzaton of the requests nto patterns [26]. Ther consdered features are those mpactng the resource allocaton: content type, duraton, sze, resoluton ndcator ( for up to 8p, for 4K), number of parallel requests for each end node. As an ordnal varable, type s normalzed as a rank, duraton, sze and parallel requests as z-scores, whle the 4K flag s left as bnary [26, Chap. 4]. A K-Means clusterng wth Eucldean dstance s then nvoked on the normalzed observaton matrx made of all the requested content. The maxmum number of centrods can be set to control the MILP solvng complexty, whch scales as (number of paths) (number of contents). We set ths max product to 6 n the results below and deduce the maxmum number of clusters n each case. The obtaned centrods are then de-normalzed. The sze of each centrod s replaced wth the sum of szes of cluster s members, whch guarantees that all ndvdual contents n the same cluster can be stored n the ntended cache. The 4K flag of each centrod s set to the majorty. The hence obtaned synthetc contents are representatve of the actual demand. We do not consder predcton of the vdeo demands, though ths can be ncluded to make the synthetc content even more accurate, as n [5]. The second data analytcs tool s provded wthn Proxy [9]: each cache stores a content-based hash (from DCT coeffcents) of each vdeo,.e. a vdeo dctonary. It prevents from storng replcates requested from dfferent URLs. C. Decomposton and dynamc orchestraton A prmal decomposton s typcally used n resource allocaton problems (as the problem) where a master problem allocates the exstng resources by drectly gvng each subproblem the amount of resources that t can use [27]. We dentfy here the vrtualzed nfrastructure allocaton varables (z,g,r,k,w,t)asthecouplng varables, leadng to a two-level structure where corresponds to the master problem and the subproblem, called Routng and Cachng Assgnment (RCA), solves routng and content cache placement (Proxy nstance selecton n our reactve framework). Snce the physcal and vrtualzed network nfrastructure (VMs deployment and actve routers) s an nput parameter of the RCA problem, the computatonal and swtchng budgets are the resources RCA allocates to maxmze QoE only: max QoE, s.t. (2), (3), (5) (7) {x,y,f,vf,h} where the nteger varables z,g,r,k,w,t are now nputs set to the last soluton s values. Another beneft s that, snce all the nteger varables n the problem are couplng varables, the RCA subproblem becomes a smpler Lnear Programmng (LP) problem solvable n polynomal tme. Algo. shows how the network and computatonal resources dynamcally reconfgure by means of and RCA. s not meant to be solved as often as RCA, as t mples actvatng and deactvatng DC nodes ntroducng non neglgble control overheads, whle RCA smply conssts of lghter routng and Proxy selecton updates. Before runnng or RCA, to reduce the unnecessary executons of the solver (typcally, CPLEX), two re-optmzaton condtons are dentfed. For, the actual number of used vcpus #vcpus n all the mcro-dc nodes s smply verfed to decde whether one of the nodes can be swtched off (meanng VMs can be better consoldated). For the RCA case, the reoptmzaton condton s based on the valdty of the last RCA optmal soluton wth respect to the current network state. In a few words, we compare f the vdeo demand varaton n terms of traffc unts between two consecutve RCA re-optmzatons can be accommodated n the budgets of bandwdth ( varables) and transcodng resources (t varables) found at the last RCA or run, respectvely. To do so, the slackness of the constrants (2) and (5d) are saved. These slackness (referred to as Σ ) represent the spare bandwdth and transcodng resources for the optmal allocaton. Then, from the montorng descrbed n the next paragraph, we compute the varatons Δ{v ems f ems } and Δ{vf eus } between two consecutve RCA re-optmzatons (dscrepancy between the planned and actual number of parallel requests of type 32

6 ems served from ). We obtan the approxmate slackness Σ. If Σ does not exceed Σ, the last optmzed bandwdth and transcodng allocatons can stll hold the actual vdeo demand despte the Δ devatons. Otherwse, RCA s trggered agan. If ths RCA re-optmzaton provokes a sgnfcant drop n QoE (larger than 2% of the maxmum QoE), the routne s trggered agan. Ths enables the resources to be scaled up when only re-confguraton wth RCA s mo more suffcent. Algorthm : Routne to trgger re-optmzatons f tme lastreopt tme + reoptmperod then 2 lastreopt tme = tme ; f #vcpus N (w + t) KT then 4 Trgger power-qoe optm. by runnng ; else f tme lastrcareopt tme + RCA reoptmperod then 8 lastrcareopt tme = tme ; 9 f Σ Σ then Trgger QoE optm. by runnng RCA; f RCA sol.8 maxqoe then Trgger power-qoe optm. by runnng ; D. Implementaton We consder a SDN management, where each clent ncomng request s ntercepted and sent to the controller. As descrbed n Algo. 2, based on the p budgets, the controller decdes whch Proxy s gong to serve the clent through whch path (njectng the approprate rules nto the swtches), unlke n greedy or tme-slot routng [4]. Another key-component s the montorng process. The average number vf ems (k) of parallel requests of type ems (defned over synthetc content used for the optmzaton) served and rejected by node s montored each perod k of duraton T sample.the RCA re-optmzaton s hence trggered dependng on the value vf ems = max k=,...,k vf ems (k), where K s the number of samples snce last optmzaton. Pror to runnng RCA or, the content clusterng s performed wth the new vdeo set (whch may have changed) snce last optmzaton. VI. NUMERICAL RESULTS In order to get frst assessments, we create a Matlab dscreteevent smulator (wth CPLEX as solver). By lack of space, ths choce s more thoroughly motvated n [28]. However to consder reproducble research standards, we make our smulator publcly avalable at [28] and are plannng a full deployment wthn ns-3. A. Smulaton settngs The FMC and backhaul target scenaros are represented wth 2 topologes: FMC tree from [23] and Moble backhaul from [] depcted n Fg. 2. Dashed lnks are redundant lnks only used n case of falure n today s confguraton, but meant to be actvated n FIA, such as SDN or ICN for 5G; we thus Algorthm 2: Routne to handle requests Data: request for vdeo wth attrbutes [type,duraton,sze,4k flag,orgnatng end node e], B(p): avalable bandwdth on each path p, CPU(): avalable CPUs at, cluster centrods used for the last optm., budgets p from last optm. Result: server ndex and path p Fnd ems by classfyng request nto the approprate cluster based on attrbute vector; 2 Fnd p wth p b ms mn, B(p) b ms mn and CPU(src(p)) d ms ; 3 f p empty then Fnd p wth 4 ms xems p b ms mn, B(p) b ms mn and CPU(src(p)) d ms ; 5 6 else f p empty then 7 Fnd p wth dest. e (f fxed) or BBU(e) (fmoble)s.t. B(p) b ms mn, andcpu(src(p)) d ms ; 8 else 9 Reject request; f p not empty then return p, = src(p); 2 consder them actvated permanently. FMC tree s a four-stage tree wth PoP, 2 level- and 4 level-2 aggregaton nodes. The latter connect the end nodes (5 fxed, moble). Fg. 2: The Moble backhaul topology from [] The load and catalog assumptons are the same as n the lterature [], [4], [5], [24]. Users demand follows a Posson process of rate λ =.4 (requests per end node per second). We later nvestgate the mpact of the load on performance, through a load factor appled on λ. A catalog s made of vdeos, wth a Zpf-dstrbuted popularty wth parameter.8. Let us re-state that the catalog sze does not mpact the onlne operaton of our optmzaton (the catalog s never assumed to be known a pror), but only serves to generate the event trace. To be representatve of YouTube-lke servces where short vdeos preval [29] and other OTT servces lke Netflx, we consder 3 possble duratons of 4, 5 and 6 mn. As well, shorter vdeos preval on moble accesses, and the dstrbutons of vdeo duratons are set to [.5,.3,.2] on fxed accesses, on moble accesses to [.66,.24,.] and [.5,.3,.2] for hgh and low popularty content, respectvely. As most of YouTube vdeos are abandoned before the end, we consder the actual watchng duraton of each request 33

7 represent a random fracton of the content duraton, wth three possble modes [.72,.65,.] taken from [3] for the frst two. The last. accounts for longer vdeos such as seres or moves that people tend to watch entrely. The probabltes of each resoluton (36p, 72p, 8p, 4K) to be requested are set to [.3,.3,.3,.] for fxed accesses and [.4,.4,.2, ] for moble, based on [3]. Accordng to [32], [33], the last-hop bandwdths are pcked wthn [5, 5, 3, 4, 6, 8] Mbps and [2, 5,, 5, 2, 25] Mbps for fxed and moble accesses, respectvely. The smulaton results are obtaned wth T sample =5mn (as n MPLS-TE), RCA reoptmp erod = 5mn, VIRCA reoptmp erod =3mn, κ =.5. The pernode storage s 5GB. To objectvely assess the gans of each component of (flexble cachng, transcodng and BBU consoldaton), we defne the followng compettors: 2: wth no possble deportaton of BBU (entrely flexble cachng wth no FMC); 3: wth cachng only at end node (or BBU f moble node) and PoP as consdered n the PDNCache/ENodeBCache soluton n []; corresponds to constraned cachng complant wth today s 4G functonng (also wth BBU deportaton here). Fnally the QoE metrcs retan only the mpacts of elements controls: () rejecton rato, () average relatve rate: for each served request, (served vdeo rate)/ mn(b ms max, last hop bw) and () startup delay to fetch the frst 5s of vdeo (non-zero, when a Proxy s forced to fetch the content through the PoP; and neglgble, otherwse, as the cache-to-clent delay s mantaned almost fxed by the Proxy vdeo rate adaptaton to bandwdth). B. Pareto analyss of optmal solutons We frst consder the results of the optmzaton alone and analyze the QoE-power tradeoff. For concseness, we cannot show all results of both topologes. They however yeld qualtatvely smlar results analyzed thereon. Fg. 3 represents the Pareto curves where each pont s obtaned for a certan value of γ (see Eq. a), whch denotes below the normalzed value once the dfference of unts between the QoE and power component has been corrected. The frst asset s that our plane allows to fnd the mnmum power to reach the hghest QoE, by consderng γ >. The gans n power range from % for FMC tree to 3% for Moble backhaul, compared to 2 whch does not allow BBU deportaton. Whle 3 exhbts gans n power too, when the load ncreases t s unable to mantan QoE. Indeed, as the number of cacheable content s ncluded n the QoE formulaton, 3 s unable to spend more power to cache (and transcode) more content beyond the last blue pont, as t allows for cachng only at BBU and PoP., wth all degrees of freedom both n terms of BBU deportaton and flexble cachng, obtans the best of both lmted solutons 2 (no convergence) and 3 (no ubqutous cachng). QoE load factor = load factor =.2 load factor = power (W) 5 Fg. 3: Pareto fronter, FMC tree Fg. 4 depcts the breakdown of consumed power between CPU (for transcodng and BBU operatons), swtchng (for mn-dcs hostng a BBU or cache) and IP routng (for mn- DCs addng, droppng or smply swtchng IP traffc va a vrtual lnk). When the load factor grows from.5 to.2, the CPU consumpton almost doubles (more requests must be transcoded) whle the routng and swtchng reman almost constant as no more nodes are used. The ncrease n actve nodes s seen when reachng a load factor of. Proper consoldaton s therefore crucal for power gans. Fnally, Fg. 5 shows the geographc breakdown of each power tem. Under low load (. and.5), most of the power s located hgh n the network, showng a hgh level of consoldaton: co-locaton of the varous computatons and hgher number of unnterrupted lghtpaths. The load ncrease makes more lower-level nodes and end nodes to be used, to explot the computatonal and cachng abltes. power (W) load factor: 5 CPU power Routng power Swtchng power Fg. 4: Breakdown of power, Moble backhaul, γ = C. Smulaton results: fxed load The followng results are obtaned for Moble backhaul. Fg. 6 to 7 are obtaned for a constant load factor of and γ =. Whle the model reduces the QoE to a sngle metrc (the lnearzed VQM) for the sake of tractablty, t s remarkable that outperforms ts compettors on other metrcs (startup-delay and rejecton rato). Fg. 6 ndeed shows that acheves a number of rejected requests lower than those of 2 and 3, an ntermedate startup delay for a power consumpton close to that of 3. The relatve 34

8 fracton of nodes load factor: PoP and agg. level agg. levels 2 and 3 end nodes IP routers caches BBUs IP routers caches BBUs IP routers caches BBUs IP routers caches BBUs..5.2 Fg. 5: Breakdown of power, Moble backhaul, γ =.4 vdeo rate (for the accepted requests) saturatng at reveals that the lmtng resource n ths confguraton are the avalable servng Proxes. As exposed before, the delay s zero when the selected Proxy does not need to fetch content va the PoP, and 3 s more often n such stuaton, snce Proxes are more constraned to be at the PoP, yeldng to lower startup delays. Fg. 7 shows the startup delay (top fgure) per class of popularty (from hgh to low: 5% most popular, next 5%, next 8%). Let us frst specfy that the other two QoE metrcs, fracton of rejectons and relatve rate, are not correlated wth the popularty. Indeed, there s ntentonally no (costly) cache lookup at the tme of assgnng a request to a cache and no resource reservaton (only plannng as n MPLS-TE). Frac. of rejectons Startup delay (s) Startup delay (s) 2 3 Relatve vdeo rate Power (W) Fg. 6: Global performance metrcs 2 3 low pop. medum pop. hgh pop. Fg. 7: Metrcs per content popularty D. Smulaton results: varyng load and catalog 2 3 We then consder the load vares over tme and space, correspondng to flash crowds. To make sure to provde maxmum QoE under mnmum correspondng power, γ s set to. The end nodes are dvded n halves. The 4 successve quarters of requests are generated wth a load factor of [.5,,.5,.5] respectvely for the frst half of end nodes, [.5,.5,,.5] for the second half. The plotted fracton of nodes are wth respect to the maxmum value over tme. Fg. 8 shows that the fracton of (vrtual) CPUs dedcated to BBU operatons remans constant as the vcpus demand d e s ndependent from load [9]. The red dots denote the reason for a rejecton (: not enough CPU, 2 and 4: not enough bandwdth cacheend node and PoP-cache, resp.). When the load suddenly ncreases, the drop n QoE (relatve rate) trggers a optmzaton rght after the more frequent RCA. The number of VCPUs for transcodng gets hgher, and the QoE goes back to maxmum. Another drop s experenced when the load shfts to the second half of end nodes, and proper reoptmzaton s agan performed to re-locate the resources. After the flash crowds, resources are scaled back down agan whle mantanng maxmum QoE. Our proposal les wthn the FIA trends, n partcular for centralzed control and MP ablty. If MP to serve a sngle request s not consdered as n ICN, optmzng budget over bundles of smlar (ems) requests s meant to leverage MP at the bundles level. To verfy f ths ablty s ndeed exploted by the system, Fg. 9 shows that durng the perod of flashcrowd about 5% to 25% of concurrent same-type requests follow two dfferent paths from the same cache to the same end nodes. Ths fracton may be ncreased n case the man lmtng resource s not the cache s CPUs (as shown n Fg. 8) but the bandwdth (f lnks capacty are lowered to current values where optc fbers are not n the whole backhaul yet). Indeed, let us change the number T of CPUs per machne from 2 to 48 to compare wth an ICN soluton such as that presented n [] for ths same backhaul topology. Fg. 9 shows that the gans n rejectons between (meant to encompass the ICN abltes) and 3 (restrcted soluton smlar to the PDNCache/ENodeBCache solutons of []) are about 5%. Ths s the same order of magntude as the delay performance shown n [, Fg. 3]. We compare to delay performance as our smulator does not nvolve retroactve bandwdth sharng wth congeston control and merely rejects excess requests. Ths comparson thereby demonstrates that our scheme s able to leverage the ICN prncple, whle beng more complete by ncorporatng formally vdeo QoE and power objectves through the transcodng and FMC capabltes. Fnally, we consder that the catalog vares over tme and space. The load factor s set to.5. The event trace s generated wth 2 dstnct catalogs. The share of the frst catalog over the 4 quarters s [,,.8, ] for the frst half of end nodes, and [,.8,, ] for the second half. Fg. shows that the system s able to keep up wth the content features changes. The above analyss therefore demonstrates the ablty of the proposed system to handle hghly dynamc envronments. VII. CONCLUSION We have desgned a control plane for telcos to address the massve ncrease of OTT vdeo demand. Brngng to- 35

9 Flash crowd on frst half of end nodes load relatve rate reason for QoE drop frac. of CPUs for BBU ops frac. of CPUs for vdeo transcodng ops Flash crowd on second half of end nodes tme 4 Fg. 8: Load varyng n tme and space Fg. 9: Left: Instantaneous number of paths per request type. Rght: Performance wth T =48nstead of 2 (statc load) relatve rate frac. of CPUs for BBU ops frac. of CPUs for vdeo transcodng ops tme (s) Fg. : Catalog varyng n tme and space gether BBU deportaton (FMC), mcro-dc wth vrtualzaton, transcodng, reactve cachng and data analytcs, the most power-effcent confguraton of actve nodes, routng and cachng s found to get the hghest QoE. A dynamc orchestraton wth nfrastructure-level and operaton-level reoptmzatons s devsed from a prmal decomposton to track load varatons n tme, space and content features. Smulatons show power gans of up to 3% whle the scores on dfferent QoE metrcs are maxmzed. 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