Algorithm for Advance Bandwidth Reervation in Media Production Network Maryam Barhan 1, Hendrik Moen 1, Jeroen Famaey 2, Filip De Turck 1 1 Department of Information Technology, Ghent Univerity imind Gaton Crommenlaan 8/201, B-9050 Gent, Belgium 2 Department of Mathematic and Computer Science, Univerity of Antwerp imind Middelheimlaan 1, 2020 Antwerpen, Belgium Email: maryam.barhan@intec.ugent.be Abtract Media production generally require many geographically ditributed actor (e.g., production houe, broadcater, advertier) to exchange huge amount of raw video and audio data. Traditional ditribution technique, uch a dedicated point-to-point optical link, are highly inefficient in term of intallation time and cot. To improve efficiency, hared media production network that connect all involved actor over a large geographical area, are currently being deployed. The traffic in uch network i often predictable, a the timing and bandwidth requirement of data tranfer are generally known hour or even day in advance. A uch, the ue of advance bandwidth reervation (AR) can greatly increae reource utilization and cot efficiency. In thi paper, we propoe an Integer Linear Programming formulation of the bandwidth cheduling problem, which take into account the pecific characteritic of media production network, i preented. Two novel optimization algorithm baed on thi model are thoroughly evaluated and compared by mean of in-depth imulation reult. Index Term Advance bandwidth reervation, media production network, video treaming, deadline-aware cheduling. I. INTRODUCTION The production of media content i a complicated proce involving a wide range of actor, uch a production houe, facility provider, broadcater and advertier. Throughout the production proce, huge amount of raw video and audio content need to be tranferred between geographically ditributed location (e.g., from an external filming location, to the production tudio, to the broadcater). Currently, the ditribution of media production content i generally performed by either people tranporting the content on a phyical torage medium or over dedicated point-to-point high-peed optical link. Clearly, thee are highly inefficient and cotly method. Connecting the different actor involved in the media production proce to a hared network ubtrate would greatly reduce capital expenditure and increae network reource utilization. Currently, uch hared media production network, connecting many actor acro a large geographical area (e.g., a country), are being deployed. A key characteritic of traffic in media production network, i it predictability. The timing, locality and bandwidth requirement of data tranfer are often known hour and ometime even day in advance. A uch, the ue of advance bandwidth reervation (AR) technique [1] would reult in greatly increaed bandwidth utilization and reduced cot. In AR network, uer ubmit requet for future data tranfer, generally encompaing a tart time (either immediately or at ome point in the future), a deadline, and total data tranfer ize (or rate). Subequently, a cheduling algorithm allocate the neceary bandwidth reource to enure that all admitted requet finih before their pecified deadline, while admitting a many requet a poible. Clearly, AR ha everal advantage for next generation media production network. It allow network operator to better plan reource uage, leading to greatly increaed reource utilization and guaranteed Quality of Service (QoS). In order to implement AR cheduling, the underlying network ha to upport bandwidth reervation. Current reearch on the topic motly focue on optical network in combination with wavelength diviion multiplexing [1]. However, Software Defined Networking (SDN) technique, uch a OpenFlow, provide high-level bandwidth reervation abtraction, hiding the detail of the underlying phyical mechanim. A a firt contribution, thi paper preent an AR-baed media production platform that i generic in term of the underlying reervation technique (e.g., wavelength- or time-baed multiplexing), which can be ued in conjunction with SDN. A a econd contribution, we propoe a et of novel AR cheduling algorithm, optimized for media production network. Such network impoe requirement not upported by exiting AR cheduling technique. Firt, the tart time of requet i generally flexible, the deadline i fixed, and the reerved bandwidth may vary over the lifetime of the reervation. Thi combination of flexible tart time and elatic bandwidth allocation ha not received much attention in reearch to date [1]. Second, in media production network, multiple requet may depend on each other (e.g., the tart time of ending edited material to the broadcater depend on the end time of ending recorded material to the production office). Until now to the author knowledge, thi apect remained unexplored. Third, it hould be poible to plit requet over multiple path, in order to further optimize bandwidth utilization. We preent an Integer Linear Programming (ILP) model to olve thi variant of the AR cheduling problem. Baed on thi model, two cheduling algorithm are preented. The Static Advance Reervation
Algorithm (SARA) aume all requet are known at the tart of the reervation period (e.g., at the tart of the day). In contrat, the Dynamic Advance Reervation Algorithm (DARA) upport recheduling in order to incorporate new requet at runtime. We provide a thorough analyi of both algorithm baed on in-depth imulation reult. They are compared and the impact of their parameter on the olution i evaluated. The remainder of thi paper i tructured a follow. In Section II, we dicu related work. Section III decribe the architecture and component of our propoed SDN-baed media production network. In Section IV, the concept, aumption and AR cheduling problem for media production network are detailed. Subequently, the deigned AR cheduling algorithm are decribed in Section V. Section VI provide imulation reult, comparing the propoed algorithm. Finally, Section VII conclude the paper. ReervaBon Interface Datacenter On- ite filming Media Network Management Layer SDN Controller AR Scheduling Algorithm Temporal roubng policie and bandwidth reervabon OpenFlow SDN witch SDN Controller OpenFlow Recording tudio Broadcater Fig. 1: Media production network architecture and component II. RELATED WORK AR ha been a popular topic of tudy in the area of optical network with WDM throughout the lat decade. Recently, Charbonneau et al. urveyed the tate of the art in WDMbaed AR, and claified exiting approache baed on a novel taxonomy [1]. Three type of AR cheduling algorithm were identified; STSD demand pecify a tart time and a duration (or deadline), STUD demand pecify a tart time but no duration, and UTSD demand pecify a duration but no tart time. STSD, on which mot work to date focue, can be further claified into fixed and flexible tart time. In the former cae, the bandwidth reervation of the requet need to tart at the pecified tart time or be blocked. In the latter cae, the reervation need to tart within a window of poible tart time. Another variation i referred to a elatic reervation, where the bandwidth allocated to a requet may vary over time. The algorithm we propoe for media production network can be claified a STSD with flexible tart time and elatic reervation. According to Charbonneau et al. only two AR cheduling algorithm have been propoed that upport elatic reervation [2], [3]. However, they both aume a fixed tart time. Current reearch on AR cheduling motly focue on recheduling [4], [5], [6], multi-domain reervation [7], and real-life deployment [8], [9], [10], [11]. Rajah et al. [4] propoe an AR cheduling algorithm for tranferring large volume of data in e-cience network. The algorithm perform an admiion control and cheduling tep. During admiion control, active requet may be rerouted in order to increae requet admiion. For the cheduling tep, two alternative objective are evaluated: quick finih (i.e., chedule all requet a oon a poible) and load balancing (i.e., minimize maximum link load). One of the objective ued in our work i inpired by the quick finih approach. Xie et al. [5] tudy the recheduling problem in more detail. They propoe an ILP-baed model, a well a a fat heuritic for re-routing flow in AR network in order to maximize admittance of new requet. Their ILP model form a tarting point for the model preented in thi paper. In concluion, the work preented in thi paper differ from tate of the art reearch in three way. Firt, to our knowledge, we are the firt to tudy the STSD problem with both flexible tart time and elatic reervation [1]. Second, in contrat to mot exiting work, we employ SDN a an enabler for a generalized AR cheduling approach, rather than one pecifically aimed at optical WDM technologie. Third, tate of the art reearch doe not conider dependencie among requet, which are important in the cae of media production network, and are explicitly incorporated in our model. III. MEDIA PRODUCTION NETWORK ARCHITECTURE The enviioned media production network i depicted in Figure 1, which we aume to be SDN-baed. The different actor and location involved in the media production proce, uch a for example recording tudio, on-ite filming crew, broadcater, and torage datacenter, are connected to a hared wide-area network, coniting of interconnected witche. The network upport the exchange of raw and encoded multimedia content between an arbitrary et of actor (i.e., unicat, multicat or broadcat), both in the form of file tranfer and treaming. The management layer provide a reervation interface, that allow the uer of the network to reerve bandwidth over certain time period in the future. The AR cheduling algorithm are reponible for reerving the required amount of bandwidth reource for all requet. With each requet, they aociate one or multiple path from ource to ink with a pecific amount of reerved bandwidth. Note that in cae of file tranfer, the reerved reource for a requet may vary over time, a long a the delivery deadline i atified. In cae the deadline of a requet cannot be guaranteed, the reervation interface reject it. When multiple requet depend on each other, either all or none of them are admitted. The output of the cheduling algorithm take the form of a et of temporal routing policie (i.e., the path aociated with all requet over time) and bandwidth reervation (i.e., the amount of bandwidth reource to aociate with each flow over time). Thi information can be tranferred to the network controller, that ue it to configure the witche in
the media production network. The controller keep track of the temporal apect of the policie, adjuting configuration when neceary. For performing the configuration, a protocol uch a OpenFlow can be ued. The remainder of thi paper focue on the AR cheduling algorithm. IV. AR SCHEDULING MODEL We firt preent a formal model for the advance reervation cheduling of network bandwidth. The model can be ued to chedule collection of requet, that conit of multiple interdependent and deadline-contrained network tranfer. The network i repreented a a graph with network node N and edge E. The requet of all cenario are tored in R. The model upport two type of network tranfer: video treaming and large file tranfer. Conequently R conit of both type. To make ditinction between two type R f which refer to file-baed flow and R which refer to the treaming requet are defined. Requet are grouped into cenario, contained in the et S, that repreent a complex workflow. Thee workflow mut be executed in their entirety, o when a cenario i admitted, all requet mut be executed. The model only admit thoe cenario for which ufficient bandwidth can be guaranteed during the reervation period. When a cenario i rejected, none of it requet are executed. The variou requet within a cenario may depend on each other, meaning that one requet can only tart when other requet have finihed. In thi model the n th requet i denoted by r n = ( n, d n, t n, t n e, i n, b n ) compriing of the ource of the requet n, the detination node d n, the time when the data for filebaed requet i ready to tranfer t n (or fixed tart time for video treaming requet), the deadline for the tranmiion of the data of file-baed requet t n e (or fixed end time for video treaming requet), the duration of each requet i n and finally the bandwidth demand of the requet b n. In particular, rf n and rn refer to file-baed and video treaming requet repectively. Moreover the volume of the file are denoted by v n and the time lot ize by I. Table I lit the notation which ha been ued to define the model. A. Deciion variable The goal of the model i to determine when and how requet are tranferred over the network. Binary deciion variable A and A n are ued to repreent whether or not cenario or requet n are admitted. When the cenario i admitted, a collection of deciion variable β n,e,k determine the amount of bandwidth for a requet n that i ent over edge e during time lot k. A n [0, 1] A [0, 1] β n,e,k R + r n R S r n R, e E, k [t min e ] For ome requet their tart and end time are not pecified and dependent on the tart or end time of other requet. In thi cae, the t n, t n e or both of a requet n may become TABLE I: Symbol and notation ued in the formal model Variable Decription N Phyical node et. E Phyical link et. S Set of all cenario ( S). R f Set of file-baed video requet. rf n The n th requet of et R f. R Set of video treaming requet. r n The n th requet of et R. R Set of all requet (R f R ). R o Set of all old requet. r n The n th requet of et R, denoted by r n = ( n, d n, t n, tn e, in, b n ). n Source node of requet r n. d n Detination node of requet r n. t n Start time for the requet r n. Deciion variable when not pecified. t n e Deadline for the requet r n. Deciion variable when not pecified. i n Duration of requet r n. b n Required bandwidth of r n. v n Volume of rf n for file-baed requet (in bit). β n,e,k Deciion variable. Dedicated Bandwidth over link e, requet r n and time interval k. SU n,k Binary deciion variable. 1 iff in time lot k any reervation i done for requet n, 0 otherwie. A n Binary deciion variable. 1 iff requet r n i admitted, 0 otherwie. A Binary deciion variable. 1 iff cenario i admitted, 0 otherwie. I Duration of each time interval (in econd). t min Minimum tart time of all reervation. t max e Maximum end time of all reervation. B e Bandwidth capacity of link e. Ev out Thi collection contain all edge tarting from node v (egre). Ev in Thi collection contain all edge ending in node v (ingre). deciion variable of which the value i determined during the optimization proce. To upport thee kind of cenario additional deciion variable and contraint need to indicate whether a requet i active during a given time lot. Therefore, we define the binary time lot ue deciion variable SU n,k that take on value 0 when a requet n i inactive during time lot k. Thee variable are defined for all requet where t n, t n e or both are deciion variable, but not for requet of which tart and end time are known. SU n,k [0, 1] t n R + t n e R + B. Objective function r n R, k [t min e ] r n R if tart time i variable r n R if end time i variable We conider two different objective function which we refer to a maxa and ASAP. By the former, hown in Expreion 1 we aim at maximizing the rate of requet admittance which i determined by umming the A n variable. max A n (1) r n R The alternative ASAP objective function, hown in Expreion 2 maximize the number of admitted requet, but alo trie to chedule requet a oon a poible. Thi i done by adding a econd factor to the objective function that achieve higher value when requet are cheduled in earlier timelot. Thi econd term i normalized to enure it will not
interfere with the primary objective of maximizing the number of accepted requet. max β n,e,k A n r + n R e E out n k [t n,tn e ] k (2) r n R C. Flow contraint r n R e E out n k [t n,tn e ] Be k Requet are cheduled over a network, which mean they are ubject to capacity and network flow contraint. The capacity contraint, hown in Expreion 3, enure that the cumulative bandwidth reervation over each link doe not exceed it bandwidth capacity. Thi contraint i pecified for every edge, and for every time lot. β n,e,k B e e E, k [t min e ] (3) r n R All network node that are not ource or ink of a flow are ubject to a flow conervation contraint, hown in Expreion 4, which enure the incoming flow equal outgoing flow. The network entering and leaving the ource and ink of the flow i dependent on the type of requet. For a file tranfer requet, an entire volume v n mut be tranferred between the tart and end time, which i hown in Expreion 5. For thee requet, the amount of data tranferred can vary between timelot. Video treaming requet are handled behave differently, a they require a contant amount of reource during all time interval between the tart and end time of the requet. Thi i hown in Expreion 6. To minimize the occurrence of loop within the network, contraint preventing incoming flow in the ource node and outgoing flow in the ink node i added. Thee contraint are hown in Expreion 7 and 8. β n,e,k = β n,e,k (4) e E out v e E in v r n R, k [t min k [t min,t max e ] e E out e ], { v N v / { n, d n }} e E out β n,e,k I = v n A n rf n R f (5) β n,e,k = b n A n r n R, k [t min n e ] (6) β n,e,k = 0 r n R, k [t min e ] (7) e E in n β n,e,k = 0 r n R, k [t min e ] (8) e Ed out n D. Interdependent requet Start and end time of requet may either be input variable or deciion variable. Dependencie between different requet are handled by Expreion 9, 10, 11, 12, 13 and, 14 and 15. Firt, Expreion 9 enure either all or none of the requet of a cenario get admitted. A n = A r n R (9) Expreion 10 i defined to connect β n,e,k and SU n,k value, which i needed if either the tart or end time of a requet i a deciion variable. Thi contraint enure that SU n,k can only become zero if β n,e,k = 0. β n,e,k SU n,k B e e E, k [t min e ], r n R (10) If the tart time i known and predefined a an input variable, then Expreion 11 enure that no bandwidth i dedicated to requet r n before t n. β n,e,k = 0 e E, r n R, k [t min, t n ) (11) If the tart time i not pecified and depend on other requet, then t n i a deciion variable. In that cae, the contraint hown in Expreion 12 i ued to enure SU n,k become 0 for value of k < t n, enuring nothing i tranferred. Dependencie between time variable can than be added a hown in Expreion 13, which enure that the requet n i tarted only when all the requet on which requet n depend are finihed. t n k + (1 SU n,k ) t max e r n R, k [t min e ] (12) t n t n e + 1 { r n R r n depend on r n } (13) When the end time i an input variable, then Expreion 14 enure that no bandwidth i dedicated to requet n after t n e. β n,e,k = 0 e E, r n R, k (t n e e ] (14) If the end time i not pecified, t n e i a deciion variable. In thi cae, a contraint i added to enure SU n,k become 0 for value of k > t n e, enuring nothing i tranferred after the end time. Thi i contraint hown in Expreion 15. t n e k (1 SU n,k ) t max e E. On-line model r n R, k [t min e ] (15) The model decribed in the previou ection can be ued to tatically compute a chedule for the execution of a collection of cenario, provided all cenario are known beforehand. In practical media production network, the requet however arrive at variou time in on-line manner. Therefore, a dynamic, on-line approach i needed that adapt the chedule at runtime. We preent thi on-line model a an extenion of the previouly dicued tatic model, meaning i implemented with the previouly defined deciion variable and objective function. The on-line model aume that a previou chedule exit, and that one or more requet are added that mut be cheduled. Thi reult in a new chedule that contain both the original requet, and the new requet. We aume that a requet may not be canceled once it ha been accepted, meaning that while old requet may be recheduled, they may not fail. Beide the contraint of the original model, one additional contraint (hown in Expreion 16) i therefore added to enure that previouly admitted requet remain accepted. A = 1 r n R o (16)
V. ILP BASED ADVANCE RESERVATION ALGORITHMS In thi ection we define two algorithm baed on the model preented in the previou ection. The Static Advance Reervation Algorithm (SARA) can be ued to generate a chedule when all requet are known before execution, and i baed on the tatic model. When not all requet are known from the tart, and new one are added throughout the day, the Dynamic Advance Reervation Algorithm (DARA), which make ue of the on-line verion of the model, can be ued. The propoed algorithm are implemented with both the maxa and ASAP objective function, reulting in four algorithm variant: SARA maxa, SARA ASAP, DARA maxa and DARA ASAP. Both algorithm are implemented in Java 1.7 and make ue of Integer Linear Programming (ILP) that are olved uing the IBM ILOG CPLEX Optimization oftware package. A. Static Advance Reervation Algorithm (SARA) The SARA algorithm i baed on the formal model that wa preented in the previou ection, which wa implemented a an ILP uing CPLEX. In thi algorithm we aume that all the cenario arrival are known beforehand, which reult in an optimal chedule. Having jut the previouly defined contraint, the multipath model i likely to reult in feaible but undeirable olution, a cycle may potentially occur in intermediate network node. A the model i implemented uing an ILP, thee cycle will never impact the optimality of the reult a pecified by the objective function. There are two poible approache to addre thee cycle. 1) Firtly, it would be poible to modify the model by changing the objective, adding an additional factor that minimize the edge ue. Thi would however increae the complexity of the model and conequently lead to an increae in execution duration. Futhermore, thi would make it more difficult to balance the different optimization objective. 2) Alternatively, the reult of the algorithm can be pot-proceed by removing the cycle after the ILP ha been olved. Thi approach ha the advantage of limiting the complexity of the ILP model, and a tated previouly ha no impact on it optimality. Becaue of thee conideration, we ue the latter olution. Therefore, we ue a pot proceing algorithm after the ILP optimization. During thi pot-proceing phae, we look for cycle in each reerved path and to get rid of extra reervation in each cycle the reerved bandwidth are modified. B. Dynamic Advance Reervation Algorithm (DARA) In practice, ome requet may not be known from the tart of the cheduling, making it impractical to ue the SARA. Therefore, a dynamic verion of the reource reervation algorithm i needed. The DARA invoke the ILP formulation of the model multiple time whenever new cenario arrive. When thi happen, the DARA re-optimize the reervation by re-routing exiting reervation in order to accommodate new cenario requet. Thi re-optimization i performed for the entire chedule tarting from the next time lot. We aume that loc1 loc5 Service Provider Broadcater Production Studio Fig. 2: Media production network topology ued in the evaluation new incoming cenario have lower priority a the previou requet are already admitted and rejecting them violate the agreed SLA. In the DARA algorithm, an initial chedule i generated uing the tatic model, which then iteratively updated uing the on-line model a new requet arrive. The input of the online model mut however be modified at every trigger point to take into account the work that ha already been executed. Therefore, requet are divided in three categorie baed on their progre: Scheduled: When a requet i cheduled, it will tart to execute during ome time lot in the future. A the requet i not yet running during the trigger point, no pecial conideration are needed. Finihed: A requet i conidered finihed when it ha finihed executing at the time of the trigger point. The requet itelf can therefore be removed from the on-line model input. If the tart or end time of other requet depend on the end time of thi requet, the final end time can be added a an input the the model. In progre: A requet i in progre when it ha tarted, but ha not finihed yet at the time of the trigger point. Thee requet mut till be conidered in the on-line model input, but the amount of data that wa already tranferred mut be removed from the total requet demand. loc4 VI. RESULTS & DISCUSSION Thi ection evaluate the propoed AR cheduling algorithm. The DARA algorithm and it two propoed objective function are compared uing the optimal SARA algorithm a a benchmark. The influence of the available bandwidth, the percentage of requet known in advance, and the time granularity are aeed. A. Evaluation Setup The media production network topology ued for the evaluation, depicted in Figure 2, contain 12 node. The network conit of media production actor ite, SDN-enabled witche and bidirectional WAN link. 8 out of 12 node are devoted to loc2 loc3
Percentage of admitted requet Production Studio Recording1 (P1) 1,4,7 Production Studio 17 Live Recording.1 (P3) Live Recording.2 (P4) 4 5 6 Live Recording.3 (P5) 1 2 3 4 Recording2 (P2) 12,13,14 Broadcater Pre production (P1) 1 Recording 2 (P2) Broadcater 7 Service Provider Recording (P1) Broadcater 5 Service Provider Recording3 (P3) 18 Service Provider 3 Production Studio (archive) 8 Production Studio (Delayed view) (a) Ue cae 1: Soccer dicuion program (b) Ue cae 2: Infotainment how (c) Ue cae 3: New broadcat Fig. 3: Interaction between media production actor in the three conidered ue cae cenario different media production actor e.g. the production tudio, broadcater, ervice provider and recording location. The 4 remaining node are the intermediate SDN witche, connected in a full meh topology. It hould be noted that thi topology i choen becaue of the limited calability of ILP-baed algorithm. In future work, by propoing calable near-optimal heuritic, higher calability will be achieved. Baed on interview with everal Belgian media production actor, including a broadcater, ervice provider, and recording facility provider, a et of ue cae cenario wa defined that erve a a bai for the evaluation. Figure 3 depict the interaction between actor in the three defined ue cae. Ue cae 1 repreent a occer after-game dicuion program and comprie 5 different file tranfer requet. Ue cae 2 i a 30 minute infotainment how and conit of 18 file tranfer requet. Finally, ue cae 3 i a new broadcat, coniting of 4 file tranfer and 4 video treaming requet. Several intance of each ue cae are generated, baed on randomized input parameter. A detailed overview of the randomized variable of each ue cae and it requet i hown in Table II. The variable name ued in the table header are motly defined in Table I. #t n dep. refer to the number of requet on which the tart time of the requet (i.e., t n ) depend. If a requet doe not depend on other, t n /dep.on i defined a the tart time of the requet, otherwie it point to thoe interdependent requet. The variable #t e dep. and t e /dep.on are imilarly defined for the end time of the requet. The variable ued in the table repreent the earliet time on which the filebaed requet could be tarted. In addition, t, d, and et deal with the treaming requet and refer to the tart time of the broadcat on televiion, the deadline of the requet to get tarted, and the end time of the requet repectively Each imulation run cover a 24 hour period. When uing the SARA algorithm, it i aumed that all cenario are known in advance. When uing DARA ome ue cae intance are aumed to be known only throughout the day, at leat one hour before t n of it earliet requet. Throughout thi ection, DARAXX%[YY] denote that XX% of the ue cae intance are known at the tart of the imulated day and the objective YY i ued (i.e., ASAP or MaxA). We found that both ASAP and MaxA objective function yield identical reult for the SARA algorithm, which i why thi algorithm i denoted by SARA without mention of the ued objective function. All reult are averaged over 50 run with different randomized input, error bar denote the tandard error. B. Impact of available bandwidth 1) Scenario: The media network infratructure ha been configured for different available bandwidth to invetigate the impact of network capacitie on the performance of our algorithm. Bandwidth capacity per link varie from 900Mbp to 1.5Gbp. The number of ue cae intance equal 20, of which 7, 7 and 6 are of ue cae 1, ue cae 2 and ue cae 3 repectively. Thi reult in a total of 209 requet. A fixed time interval granularity of 1 hour i ued. 2) Reult: Figure 4 compare the percentage of admitted requet of SARA to the maxa and the ASAP objective function of DARA, where for the latter either 0% or 50% of ue cae intance are known in advance. A expected, SARA outperform DARA, a knowing all requet give more freedom to chedule everything, making it eaier to determine the ubet of requet to reject. From the figure, we can conclude that the ASAP outperform MaxA in a dynamic cenario, a it chedule requet a oon a poible, freeing up more reource for requet that may arrive in the future. 100 95 90 85 80 75 70 900 1000 1100 1200 1300 1400 1500 Phyical bandwith (Mbp) SARA DARA50%[maxA] DARA0%[maxA] DARA50%[ASAP] DARA0%[ASAP] Fig. 4: Impact of bandwidth capacity on requet admiion rate, comparing objective function maxa and ASAP
Percentage of admitted requet Percentage of admitted requet TABLE II: Detail of the ue cae requet Ue cae 1 T ype n d n #t n dep. t n /dep.on #t n e dep. t n e /dep.on i n b n Req1 r f P 1 Production tudio 0 rand( + 1hr, + 5hr) 1 Req3 90min 200Mbp Req2 r f P 1 Production tudio 0 rand(, + 6hr) 1 Req3 90min 200Mbp Req3 r f Broadcater Production tudio 0 Req1, 2 1 Req4 90min 200Mbp Req4 r f Production tudio Service provider 3 Req1, 2, 3 0 t 180min 15Mbp Req5 r f Service provider Broadcater 0 t + 3hr 0 24hr 180min 15Mbp P 1 = rand(loc1, loc2, loc3, loc4, loc5); = rand(1, 9) hr; t = rand(17, 19) hr Ue cae 2 T ype n d n #t n dep. tn /dep.on #tn e dep. tn e /dep.on in b n Req1,9 r f P 1 Production Studio, Service Provider 0 rand(, 17hr) 1 Req17 (50 60)min 200Mbp Req2,10 r f P 2 Production Studio, Service Provider 0 rand(, 17hr) 1 Req17 (50 60)min 200Mbp Req3,11 r f P 3 Production Studio, Service Provider 0 rand(, 17hr) 1 Req17 (50 60)min 200Mbp Req4,12 r f P 1 Production Studio, Service Provider 0 rand(, 17hr) 1 Req17 (50 60)min 200Mbp Req5,13 r f P 2 Production Studio, Service Provider 0 rand(, 17hr) 1 Req17 (50 60)min 200Mbp Req6,14 r f P 3 Production Studio, Service Provider 0 rand(, 17hr) 1 Req17 (50 60)min 200Mbp Req7,15 r f P 1 Production Studio, Service Provider 0 rand(, 17hr) 1 Req17 (50 60)min 200Mbp Req8,16 r f P 2 Production Studio, Service Provider 0 rand(, 17hr) 1 Req17 (50 60)min 200Mbp Req17 r f Production tudio Broadcater 16 Req1..16 1 Req18 60min 200Mbp Req18 r f Broadcater Service provider 1 Req17 0 t 60min 15Mbp P 1, P 2, P 3 = rand(loc1, loc2, loc3, loc4, loc5); = rand(1, 15) hr; t = rand(18, 22) hr Ue cae 3 T ype n d n #t n dep. t n /dep.on #t n e dep. t n e /dep.on i n b n Req1 r f P 1 P 2 0 rand(, 9hr) 1 Req2 (30 50)min 200Mbp Req2 r f P 2 Broadcater 1 Req1 0 rand(10, 12)hr (30 50)min 200Mbp Req3 r f Production tudio Broadcater 0 rand(, 9hr) 0 rand(10, 12)hr (30 50)min 200Mbp Req4 r P 3 Broadcater 0 rand(t, d) 0 et (8 10)min 15Mbp Req5 r P 4 Broadcater 0 rand(t, d) 0 et (8 10)min 15Mbp Req6 r P 5 Broadcater 0 rand(t, d) 0 et (8 10)min 15Mbp Req7 r Broadcater Service provider 0 t 0 t+0.5hr 30min 15Mbp Req8 r f Broadcater Production tudio 0 t+0.5hr 0 24hr 30min 15Mbp P 1, P 2, P 3, P 4, P 5 = rand(loc1, loc2, loc3, loc4, loc5); = rand(1, 7) hr; t = rand(12, 16) hr; d = (t + 0.5 i n ) hr; if (i n < I) then (et = T n +1) ele (et = T n + in ) 100 68 95 90 85 80 75 70 SARA[ASAP] DARA90%[ASAP] DARA70%[ASAP] DARA50%[ASAP] DARA0%[ASAP] 900 1000 1100 1200 1300 1400 1500 Phyical bandwith (Mbp) Fig. 5: Impact of bandwidth capacity and percentage of known requet on admiion rate 66 64 62 60 58 56 54 52 50 SARA[ASAP] DARA90%[ASAP] DARA70%[ASAP] DARA50%[ASAP] DARA0%[ASAP] 1200 1800 2400 3000 3600 Time lot ize () Fig. 6: Impact of timelot granularity on requet admiion rate Thi how that a naive approach that merely maximize requet admiion work well in a tatic cenario, but not in a dynamic one. ASAP outperform MaxA up to 3.27% when 0% of requet are known in advance, and up to 1.97% when 50% are known. A ASAP reult in a higher number of accepted requet, we continue the evaluation with thi function only. Figure 5 depict more detailed reult for ASAP, howing the influence of the percentage of requet known in advance on the olution. A expected, more known requet ignificantly increae performance. When no requet are known in advance, SARA outperform DARA[ASAP] by up to 6.02%, while when 90% are known thi i reduced to 2.19% at mot. C. Impact of time lot granularity 1) Scenario: In thi ection we evaluate the effect of time lot granularity on the performance of the algorithm, both in term of olution optimality and execution complexity. The ize of the time interval parameter wa varied between 1200 and 3600 econd. The number of ue cae intance i 15 (5 of each ue cae) and the number of total requet i 155. A link capacity of 400Mbp i ued. 2) Reult: Figure 6 tudie the impact of time lot granularity on SARA and DARA for the ASAP objective function. A hown in thi figure, the fine-grained experiment with hortet time lot reult in the bet performance. However, although more granularity increae the performance of the model, the complexity of the model ignificantly increae a well. Fine-grained time lot ize increae the number of deciion variable and contraint. The reult of complexity meaurement i provided in Table III. The table how the average, minimum and maximum number of contraint and deciion variable of any olved problem. In cae of DARA, everal ILP problem need to be olved throughout the day. The average number of invocation of the algorithm throughout the day i depicted a #k. The variable I repreent the time lot granularity. When comparing performance for
TABLE III: Complexity of the olved ILP problem, in term of number of contraint and number of deciion variable SARA DARA90%[ASAP] DARA70%[ASAP] DARA50%[ASAP] DARA0%[ASAP] I (ec) AVG MIN MAX #k AVG MIN MAX #k AVG MIN MAX #k AVG MIN MAX #k AVG MIN MAX #k Contraint Variable 1200 317774 306360 329103 1 226255 87407 280811 2.88 183321 66951 246462 5.62 154957 66981 229945 8.1 98525 9775 189878 11.94 1800 211326 203642 218957 1 150267 57590 187301 2.88 121658 43893 164845 5.42 103631 44573 153981 7.52 67183 6579 126375 10.92 2400 158130 152281 163828 1 113172 43436 141007 2.82 91661 33475 124094 5.2 77909 33775 116050 7.1 50621 5956 95854 9.84 3000 127119 122540 131684 1 90667 35773 113279 2.74 73667 27790 100297 5 63249 27712 93268 6.72 41773 4100 76460 9.18 3600 104862 101024 108640 1 75286 28143 94265 2.72 61218 21862 82471 4.72 52455 21782 77953 6.3 34827 3352 63925 8.42 1200 321265 321265 321265 1 240018 118473 290616 2.88 197887 99639 266422 5.62 168117 97107 253304 8.1 108556 10230 213600 11.94 1800 214225 214225 214225 1 159564 76853 194087 2.88 131508 64521 176793 5.42 112678 65652 169407 7.52 74269 6832 142796 10.92 2400 160705 160705 160705 1 120391 58300 146246 2.82 99322 49286 134374 5.2 84916 50002 128002 7.1 56164 5735 108176 9.84 3000 129485 129485 129485 1 96647 47380 117900 2.74 79926 40680 108725 5 69135 40599 102202 6.72 46307 4182 86235 9.18 3600 107185 107185 107185 1 80462 38236 97706 2.72 66671 32434 89002 4.72 57537 31523 85909 6.3 38901 3462 71836 8.42 SARA, an interval ize of 1200 econd yield 4.4% better reult than a ize of 3600 econd. However, the complexity of the problem alo increae, a both the average number of contraint and deciion variable are increaed threefold. Given the exponential time complexity of ILP olving algorithm, thi increae in problem complexity reult in an exponential increae in execution time. For DARA, a imilar trend i oberved. Moreover, it hould be noted that in cae fewer requet are known in advance, the complexity of a ingle DARA algorithm invocation decreae ignificantly. For example, when 0% of requet are known, both the number of contraint and variable are about 3 time maller than when all requet are known in advance. In the former cae, the algorithm need to be executed between 8 and 11 time on average, while in the latter only once. However, due to the exponential time complexity, it i generally fater to olve a large number of mall problem, rather than a mall number of big one. VII. CONCLUSION In thi paper, we propoed an architecture for an SDNbaed media production network, and a et of cheduling algorithm for advance bandwidth reervation (AR) atifying the pecific requirement of uch network. The algorithm are baed on an ILP formulation that incorporate multipath routing, time-variable bandwidth reervation, flexible tart time, and requet dependencie. It upport both file-baed tranfer and treaming eion. The two propoed algorithm variant operate in an offline (i.e., SARA) and online (i.e., DARA) manner repectively. Baed on imulation reult, the viability of AR cheduling in media production network wa aeed. Reult how that when a ignificant portion of requet i known at the tart of the day, AR ignificantly increae bandwidth efficiency and requet admittance. Concretely, in cae all requet are known at the tart of the day, requet admittance can be increaed up to 6.02% compared to when requet are only known one hour before their deired tart time. Additionally, the impact of time interval granularity on performance wa evaluated. Time granularity increae algorithm accuracy and optimality in term of requet admittance. However, it alo affect the ILP problem ize, reulting in an exponential execution time increae. Concretely, a time lot ize of 1200 econd reulted in up to 4.4% more requet admittance than a ize of 3600 econd. In ummary, we have proven the viability of uing AR cheduling in media production network to ignificantly improve bandwidth efficiency and requet admittance. A future work, we plan to tudy very large cale cenario in detail. ACKNOWLEDGMENT The computational reource (Stevin Supercomputer Infratructure) and ervice ued in thi work were provided by the VSC (Flemih Supercomputer Center), funded by Ghent Univerity, the Hercule Foundation and the Flemih Government department EWI. The reearch leading to thee reult wa performed within the context of ICON MECaNO. 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