Network Slicing for Ultra-Reliable Low Latency Communication in Industry 4.0 Scenarios

Transcription

1 1 Nework Slicing for Ulra-Reliable Low Laency Communicaion in Indusry 4.0 Scenarios Anders Ellersgaard Kalør, René Guillaume, Jimmy Jessen Nielsen, Andreas Mueller, and Pear Popovski arxiv: v1 [cs.it] 30 Aug 2017 Absrac An imporan novely of 5G is is role in ransforming he indusrial producion ino Indusry 4.0. Specifically, Ulra-Reliable Low Laency Communicaions (URLLC) will, in many cases, enable replacemen of cables wih wireless connecions and bring freedom in designing and operaing inerconneced machines, robos, and devices. However, no all indusrial links will be of URLLC ype; e.g. some applicaions will require high daa raes. Furhermore, hese indusrial neworks will be highly heerogeneous, feauring various communicaion echnologies. We consider nework slicing as a mechanism o handle he diverse se of requiremens o he nework. We presen mehods for slicing deerminisic and packe-swiched indusrial communicaion proocols a an absracion level ha is decoupled from he specific implemenaion of he underlying echnologies. Finally, we show how nework calculus can be used o assess he end-o-end properies of he nework slices. I. INTRODUCTION Indusry 4.0 refers o he fourh indusrial revoluion ha ransforms indusrial manufacuring sysems ino cyberphysical sysems by inroducing modern and emerging informaion and communicaion echnologies, such as 5G conneciviy and cloud compuing [1], [2]. Specifically, one of he generic services in 5G ermed Ulra-Reliable Low Laency Communicaions (URLLC) is poised o bring wireless connecions of unprecedened reliabiliy, such as [3], [4]. This will give rise o new designs of machines and robos, released from he consrains imposed by cabled connecions and he need for physical aachmen. Neverheless, no all connecions will always require ulra-high reliabiliy. In some use cases high daa rae may be required and in ohers, simulaneous suppor of many connecions is required. In fac, he conneced indusry will feaure connecions of all hree ypes envisioned in 5G: enhanced Mobile Broadband (embb), massive Machine-Type Communicaions (mmtc) and URLLC [5]. Simulaneously saisfying diverse conneciviy requiremens wihin he same sysem is challenging since he nework canno be opimized for a specific ype of service. A promising approach o handle his problem is nework Anders E. Kalør, Jimmy J. Nielsen, and Pear Popovski are wih he Deparmen of Elecronic Sysems, Aalborg Universiy, 9220 Aalborg, Denmark ( akalar12@suden.aau.dk, {jjn,pearp}@es.aau.dk). René Guillaume and Andreas Mueller are wih Corporae Secor Research and Advanced Engineering, Rober Bosch GmbH, Renningen, Germany ( {Rene.Guillaume,Andreas.Mueller21}@de.bosch.com).

2 2 Nework slice 2 Nework slice 1 Physical nework Fig. 1: Illusraion of slicing a physical nework ino wo logical sub-neworks. Nework slice 1 offers URLLC o an edge cache and he cloud, while nework slice 2 provides an embb service o he Inerne. slicing, which refers o he process of slicing a physical nework ino logical sub-neworks which are each opimized for specific applicaions wih cerain characerisics [6]. For insance, as illusraed in Fig. 1, one nework slice may offer URLLC based informaion access by reserving communicaion and buffer resources along a pah from he end-user o an edge cache, or o a daabase in he cloud (nework slice 1). A he same ime, anoher nework slice in he same physical nework may offer an embb service beween a robo and he Inerne, e.g. o allow for firmware updaes (nework slice 2). Nework slicing is enabled by recen nework echnologies such as Sofware-Defined Neworking (SDN) and Nework Funcion Virualizaion (NFV) o decouple he nework conrol plane from he daa plane, and o cenralize he managemen of rouing, queues, ec. A. Nework Slicing for Indusry 4.0 Alhough encompassing embb, mmtc, and URLLC, Indusry 4.0 is characerized by is very sric laency and reliabiliy requiremens. For insance, conrol sysems and alarm sysems may require a delivery reliabiliy in he order of and end-o-end laencies in he range of ms [4]. As a resul, nework slicing for indusrial neworks poses several challenges. Firs, consrucing nework slices wih sric end-o-end laency and reliabiliy guaranees as required by indusrial applicaions is challenging due o he difficulies in modeling and predicing queuing delays wih high accuracy. Secondly, indusrial neworks are ofen very heerogeneous, comprising many specialized legacy proocols, which complicaes accurae end-o-end analysis [7]. Guaraneeing low laency and high reliabiliy has radiionally been accomplished hrough he use of deerminism and cyclic communicaion wih reserved resources and limied opions for dynamic configuraion. This configuraion is no ideal for Indusry 4.0 where low laency raffic no only may have o pass several links o reach he cloud, bu also is highly dynamic due o he high mobiliy inroduced by wireless echnologies. Insead, echnologies wih mechanisms for low-laency communicaions may be more favourable, such as URLLC and Eherne TSN which has received much aenion

3 3 in he communiies for indusrial communicaion [8]. However, i is unlikely ha all proocols will be replaced by new echnologies a once, and nework slices need o work across boh new and exising echnologies. Therefore, nework slicing mus be sudied and resolved a an absracion level which capures he main characerisics of he proocols bu is decoupled from he specific implemenaions, such as legacy proocols, URLLC and Eherne TSN. In his aricle, we presen mehods for slicing indusrial communicaion proocols wih focus on applicaions which require srong reliabiliy and laency guaranees analogous o hose argeed by URLLC. To his end, we invesigae he uilizaion, reliabiliy and isolaion rade-offs of he mehods in an absrac seing which is independen of he specific deails of he proocols, and we demonsrae how end-o-end properies of he proposed nework slicing mehods can be calculaed across communicaion echnologies using nework calculus, boh for a specific use case and in a general seing. The remainder of he aricle is organized as follows. Secion II inroduces mehods for slicing indusrial neworks. Secion III describes a personalized medicine manufacuring sysem, which is used o illusrae how end-o-end delivery reliabiliy and laency bounds can be obained. Finally, he aricle is concluded in Secion IV. II. NETWORK SLICING METHODS Indusrial neworks commonly follow a hierarchical srucure as illusraed in Fig. 1. The individual devices such as acuaors, sensors, ec. are conneced in a facory uni, and are ypically conrolled by a maser device in a maser/slave configuraion. The connecion may be wired or wireless, or in a combinaion where a small 5G base saion is par of he facory uni, e.g. if here is need for high synchronizaion beween he devices. The facory uni is usually based on a deerminisic and cyclic proocol, wih resources reserved o he individual devices in each cycle. The cycle imes may vary from sub-millisecond o several milliseconds depending on he sysem. The maser devices of he individual facory unis are conneced o a facory-wide nework, which may also be conneced o an exernal infrasrucure such as he Inerne. The facory nework is ypically based on swiched proocols such as regular Eherne or Eherne TSN and possibly TCP/IP. I includes general purpose hardware and cloud compuing resources which can be used by he maser devices, or even by componens in a facory uni, and may comprise one or more 5G base saions, which provide wireless conneciviy o devices in he facory. We now describe slicing mehods for cyclic proocols wihin facory unis, followed by a discussion and analysis of nework slicing in swiched neworks a he facory-wide nework. A. Facory Unis As a facory uni, we consider a single maser/slave nework wih a fixed cycle ime. Each cycle conains a number of resources (byes), which are each allocaed o a specific applicaion running on a cerain device. The allocaion is fixed and canno change during operaion. We consider a nework comprising one deerminisic applicaion which ransmis in every cycle (e.g. sensor readings for closed-loop conrol), and K sochasic applicaions which ransmi frames randomly (e.g. sensor alarms). The deerminisic applicaion ransmis R d frames of size N d in every cycle, while he number of frames ransmied by sochasic applicaion k is denoed by R k, and of fixed size N k.

4 4 R d R k R d Phy. layer Phy. layer App. k arrivals App. k arrivals Cyclic arrivals Cyclic arrivals (a) (b) Fig. 2: Two mehods of cyclic resource muliplexing: a) muliplexing of reserved resources beween gaeway arrivals; b) muliplexing by overwriing random cyclic raffic. An obvious slicing scheme is o simply assign a number of resources in each cycle o he individual applicaions based on he amoun of daa ha hey ransmi (Fig. 2a). Suppose we allocae N k byes o sochasic applicaion k. Neglecing ransmission and oher error sources, and assuming ha excess frames are no buffered bu dropped, he reliabiliy of he scheme is simply he probabiliy ha all arriving frames can be ransmied, Pr(R k N k N k ). Furhermore, assuming ha N d = R dn d byes are allocaed o he deerminisic applicaion, i canno fail due o resource shorage. The proposed scheme provides a high degree of isolaion beween applicaions and is simple o analyze, bu i resuls in a low resource uilizaion if daa is no ransmied in every cycle. This is paricularly prominen for bursy ransmissions wih high reliabiliy requiremens. Alhough he uilizaion could be improved by inroducing queuing o he sysem, his complicaes he analysis, in paricular when he arrival disribuion ake a more complex form, which makes i difficul o provide end-o-end guaranees. An alernaive slicing scheme is where resources are allowed o be shared beween he K sochasic applicaions. This resuls in an increased saisical muliplexing gain due o he increased aggregae arrivals, and hence an improved resource uilizaion. Under his scheme, an allocaion of N byes are shared beween K applicaions, so ha i fails when he aggregae arrival exceeds N, i.e. k R kn k > N. However, alhough he scheme increases he uilizaion, he gain comes a he cos of reduced isolaion due o muliplexing beween he applicaions. Specifically, he ransmissions by one applicaion influence wheher oher applicaions can ransmi. This may in paricular be problemaic when here are uncerainies in raffic models, due o is impac on he sysem reliabiliy. Furhermore, wihou inroducing scheduling mechanisms, he scheme canno ake diverse reliabiliy requiremens ino accoun, e.g. hrough applicaion prioriizing. Therefore, i is mos useful when muliple applicaions ransmi he same ype of daa, such as sensor readings. Alhough muliplexing increases he uilizaion for high aggregae arrival raes, i sill achieves a low uilizaion for apps wih low arrival raes wih high reliabiliy requiremens. To improve he uilizaion for rare ransmissions, we consider a scheme where high-prioriy applicaions are allowed o overwrie specific resources allocaed o oher applicaions (Fig. 2b). This is also referred o as puncuring in he conex of 5G. For insance, a closed-loop conrol sysem may obain feedback from a sensor in each cycle, bu remain sable during shor inerrupions of he feedback loop. Hence, he sysem provides a higher reliabiliy han needed. Suppose ha each frame ransmied

5 5 by a sochasic applicaion overwries a random (uniformly disribued) frame from he deerminisic applicaion. We assume ha wo applicaion frames canno overwrie he same periodic frame, even if he sum of frame sizes is smaller han he size of he periodic frame. The reliabiliy of he deerminisic raffic is he probabiliy ha a frame allocaed o he deerminisic applicaion is no overwrien by any of he applicaion frames, while a sochasic applicaion ransmission fails if he aggregae arrival k R k exceeds R d. B. Facory-Wide Neworks The facory-wide nework is based on packe-swiched echnologies where frames are queued a each link o increase he link uilizaion. However, queuing inroduces a random delay which depends on he raffic ha shares he link. In simple neworks, he frames may be processed as firs-in-firs-ou, while complex neworks may apply various queue schedulers o conrol he flow and prioriize cerain ypes of raffic. A precondiion for using swiched neworks in Indusry 4.0 is he abiliy o analyze he queuing delay of he raffic wih sric end-o-end laency requiremens. Several mehods for queuing delay analysis exis, including queuing heoreic approaches [9] and sochasic nework calculus (SNC) [10], which give probabilisic resuls abou he queuing delay, and deerminisic nework calculus (DNC) [11] which provides wors-case laency bounds. While he probabilisic resuls from queuing heory and SNC allow for exploiing he sysem requiremens more efficienly han DNC, he raffic arrival and server models are ofen srongly resriced in order o keep he analysis racable, which limis he usefulness of he mehods. In paricular, hey are no well suied for indusrial neworks where he raffic is generaed by a mixure of periodic and sochasic sources, and where he nework requiremens are oo sric o allow for model approximaions and uncerainies. On he conrary, DNC allows for analyzing wors-case laencies as long as he arrival processes are bounded by some funcion. Since he nework wihin a facory unis has finie resources per cycle, his provides a bound on he arrival processes. Therefore, we focus on modeling he laency using DNC, alhough he oher mehods could be applicable in some scenarios as well. We omi a deailed presenaion of DNC here, and insead refer o [11], [12] for a horough reamen. The heory of DNC is based on he noion of arrival and service curves which are funcions ha bound he cumulaive number of byes arriving o and being served by a queue. Alhough he heory of DNC is very general and resuls can be obained wih many ypes of curves, he curves are ofen resriced o affine bounds o simplify he analysis. For example, he arrivals from an applicaion ha generaes a frame of size N periodically every M ime unis would be bounded by he affine funcion A() = [N/M + N] + where [x] + = max(0, x). Similarly, he service rae of a server, modeling he serializaion of frames, may be lower bounded by he affine service funcion. Several resuls can be obained from DNC, as exemplified in Fig. 3. Here, A() defines he affine bound on raffic 32 byes arriving periodically in every cycle of duraion, and S() is he service curve defining he rae a which he arriving byes are served. From A() and S(), one may obain he waiing ime bound W () and he deparures from he queue, D(), which may in urn be used as arrival curve o he nex queue in he pah for end-o-end analysis. Furhermore, hrough he noion of lefover service, DNC allows for analyzing muliple queues wih various scheduling policies such as prioriizaion queuing. Lefover service refers o he minimum service ha is available o a queue afer oher queues have been served. This is imporan in he conex of indusrial applicaions,

6 6 Cumulaive byes A() = [ ] + S() = [50 128] + D() = [ ] + W () Fig. 3: Graphical represenaion of he quaniies in DNC. TABLE I: End-o-end requiremens in he use case Applicaion Source Des. Type Mean period Size Laency req. Reliabiliy req. Conrol Maser Robo Periodic 1 ms 128 B 1 ms Conrol Robo Maser Periodic 1 ms 128 B 1 ms Paien info reques Maser Cloud Periodic 200 ms 128 B 10 ms Paien info response Cloud Maser Periodic 200 ms 1024 B 10 ms Scale readings Scale Cloud Periodic 200 ms 512 B 100 ms Sensor alarms Receiver Cloud Poisson 60 s 32 B 5 ms HMI sream Cloud HMI Periodic 20 ms 20 kb 20 ms where scheduling, and in paricular raffic prioriizaion, is necessary o guaranee low end-o-end laencies wih high probabiliy. Oher quaniies ha can be obained from DNC include maximum queue size which can be used o dimension buffers. III. CASE STUDY: PERSONALIZED MEDICINE MANUFACTURING This secion inroduces a simple personalized medicine manufacuring sysem as a use case of Indusry 4.0 o demonsrae how nework slicing can be used o handle diverse end-o-end nework requiremens. Furhermore, he sysem will be used o sudy he rade-offs in he slicing mehods presened in previous secion. The sysem is derived o conain he main properies and realisic requiremens of an Indusry 4.0 sysem, and is based on he poenial URLLC requiremens defined by 3GPP [3]. I is described in an absrac way, which is independen of he specific communicaion echnologies used in he nework, so ha i can represen boh wired legacy proocols and URLLC echnologies.

7 7 Facory nework Facory unis Fig. 4: Personalized medicine manufacuring nework consising of 10 facory unis and a facory-wide nework. The sysem consiss of 10 idenical maser/slave facory unis conneced using an indusrial cyclic communicaion proocol. The maser devices of each facory uni are physically locaed a he base saion, and conneced o a cloud hrough a hierarchical swiched facory-wide nework as depiced in Fig. 4. Each of he 10 facory unis conrols a pipeing machine mouned on a roboic arm, which dispenses a drug produc ino a conainer. The ype and amoun of drug is deermined based on paien informaion obained from a paien daabase in he cloud. To validae he process he final produc is weighed afer he drug has been dispensed, and he weigh is sored in he cloud. Finally, he enire process can be moniored by an operaor using an Human Machine Inerface (HMI) which is conneced o he facory uni, and displays a video sreamed from he cloud. Furhermore, a number of sensors are locaed in he uni o supervise he process, which may raise alarms in case of failures. The requiremens o he nework are lised in Table I. The facory uni neworks are based on a maser/slave communicaion proocol wih a cycle ime of 1 ms. The facory-wide nework is based on swiched 100 Mbi Eherne, and he swiches are equipped wih prioriizaion queues o each ougoing link. To simplify he seing, we assume ha each link only has wo queues. Furhermore, since we are mainly ineresed in he rade-offs in using various slicing schemes, we consider a unified channel model where he frame delivery reliabiliy of he links in boh he facory unis and he facory-wide nework is A. End-o-End QoS Analysis There are numerous combinaions of he nework slicing schemes from Secion II ha may saisfy he applicaion requiremens in he medicine manufacuring sysem, and a complee reamen is beyond he scope of his aricle. Insead, we focus on a few applicaions and illusrae how he proposed slicing schemes and DNC can be used o obain end-o-end resuls of he individual nework slices, as well as o analyze he ineracion beween he slices. For simpliciy, we ignore propagaion delays and focus on queuing. Furhermore, we ignore poenial overhead added by proocol headers in he nework, and use milliseconds as he ime uni.

8 8 We firs consider he resources for he sensor alarms. Since he number of alarms in each cycle is random, we can eiher reserve a fixed number of resources in each cycle, or we can allow alarms o overwrie he cyclic conrol raffic, which has a lower reliabiliy requiremen. Allocaing a fixed number of resources resuls in a low uilizaion, while overwriing conrol raffic inroduces a decrease in he reliabiliy of he conrol raffic. Since he rae of alarms is very low compared o he cycle ime, he overwriing scheme is a promising approach for his use case. Figure 5 shows he end-o-end frame failure probabiliies of he alarm and conrol raffic for a mean number of alarm arrivals per cycle, λ. We consider he cases where he conrol raffic comprises 1 and 4 frames, R conrol = and R conrol = A a low number of arrivals, he reliabiliy approaches he reliabiliy of he links. Since here is only a single link beween he source and desinaion of he conrol raffic, compared o hree links for he alarms, is reliabiliy is significanly higher. As he number of arrivals increases, he reliabiliy decreases for boh he conrol raffic and he alarms. The decrease in he conrol raffic reliabiliy is due o a higher probabiliy of being overwrien by an alarm, while he decrease in alarm reliabiliy is due o an increased probabiliy of experiencing a shorage of resources in a cycle. In he specific use case considered in his aricle, he reliabiliy requiremen of he conrol raffic is , which can be achieved up o an arrival rae of λ = for R conrol = Consequenly, his is sufficien for he expeced iner-arrival ime of 60 s (λ ), and hence he overwriing slicing scheme would be a reasonable choice. Furhermore, he reliabiliy of he alarm raffic a his poin is very high since i is unlikely ha wo alarms arrive in he same cycle, and since he resources are used in all cycles, he uilizaion is 100 percen. By comparison, if 32 byes were allocaed in each cycle only o he sensor alarms, i would on average only be used once every 60 seconds, yielding a uilizaion of approximaely 0.02 percen, and would in addiion occupy 32 byes more of he frame han he overwriing scheme. A consequence of he prioriizaion queuing scheme in he facory nework is ha he isolaion beween he queues is limied, since an increase in he high-prioriy raffic also resuls in an increased queuing delay of he raffic of lower prioriy. Suppose now ha we in he facory unis decide o use he overwriing scheme for he alarm raffic. Furhermore, assume ha we give sensor alarms high queue prioriy in he enire pah from source o desinaion, and ha he periodic paien info requess are given second prioriy. Obviously, he queuing delay ha he paien info requess experience depends on he number of sensor alarms. The maximum number of byes ha can arrive o he facory nework from he alarms is enforced by he number of conrol frames ha can be overwrien. Specifically, in he cases considered above where 1 or 4 conrol frames are allocaed, a mos 1 or 4 alarm frames can arrive o he facory nework in each cycle (1 ms). Using his bound, we may use DNC o obain a bound on he oal end-o-end laency experienced by boh he alarm frames and he paien info requess. This is illusraed in Fig. 6 for various sensor alarm arrival bounds, R alarms. As R alarms approaches 0, he alarm laency approaches he cycle ime of 1 ms. For increasing R alarms, he laency experienced by boh he sensor alarms and he paien info requess increases due o an increased serializaion ime. Noice ha despie being unchanged, he paien info reques laency increases wih a larger slope han ha of he sensor alarms. This reflecs he conservaism of he affine bound, and is due o accumulaion of low-prioriy frames in he ime where he high-prioriy raffic is served. In a sysem wih more queuing prioriies, he accumulaion would occur a each prioriizaion queue all he way o he queue wih lowes prioriy. Alhough he laency is sill low in he shown scenario, i shows ha

9 Alarms, R conrol = Conrol, R conrol = Alarms, R conrol = 4 32 Conrol, R conrol = 4 32 Failure probabiliy λ Fig. 5: End-o-end failure rae of conrol and sensor alarm raffic in he nework slicing scheme based on overwriing for various alarm arrival raes. 4.5 Sensor alarms Paien info requess Laency [ms] R alarms Fig. 6: End-o-end laency of sensor alarms and paien info requess for various alarm arrival raes.

10 10 enforcing a limi on he number of byes enering he facory-wide nework is imporan o mainain he required laency. This can eiher be done by exploiing he reserved resources in he facory unis as done here, or by insering raffic shapers, such as oken buckes, ino he nework. IV. CONCLUSION 5G, and paricularly URLLC, will play an imporan role in ransforming indusrial manufacuring sysems ino Indusry 4.0. Furhermore, a wide range of new applicaions will emerge due o he increased conneciviy, and hey will have a diverse se of requiremens o he nework, ranging from ulra low-laency cyclic delivery guaranees o bes-effor and high daa raes. This aricle invesigaes nework slicing as a way o handle his diverse se of applicaion requiremens, wih focus on URLLC. We have presened mehods for slicing boh cyclic and swiched indusrial proocols a an absrac level, and discussed heir rade-offs in uilizaion, reliabiliy and isolaion. Furhermore, using a case sudy of an indusrial medicine manufacuring sysem wih diverse nework requiremens, we have illusraed how deerminisic nework calculus can be used for analyzing end-o-end laencies of nework slices comprising boh deerminisic and swiched neworks. ACKNOWLEDGMENT The work by J. J. Nielsen and P. Popovski has parly been suppored by he European Research Council (ERC Consolidaor Gran nr WILLOW), and parly performed in he framework of he Horizon 2020 projec ONE5G (ICT ) receiving funds from he European Union. The work by R. Guillaume and A. Mueller is suppored by he Federal Minisry for Educaion and Research (BMBF) wihin he projec Fuure Indusrial Nework Archiecure (FIND) (16KIS0571). REFERENCES [1] R. Drah and A. Horch, Indusrie 4.0: Hi or hype? IEEE indusrial elecronics magazine, vol. 8, no. 2, pp , [2] N. Jazdi, Cyber physical sysems in he conex of indusry 4.0, in IEEE Inernaional Conference on Auomaion, Qualiy and Tesing, Roboics, 2014, pp [3] 3GPP, Service requiremens for he 5G sysem, 3rd Generaion Parnership Projec (3GPP), TS v16.0.0, [4] P. Schulz e al., Laency criical IoT applicaions in 5G: Perspecive on he design of radio inerface and nework archiecure, IEEE Communicaions Magazine, vol. 55, no. 2, pp , [5] M. Shafi e al., 5G: A uorial overview of sandards, rials, challenges, deploymen, and pracice, IEEE Journal on Seleced Areas in Communicaions, vol. 35, no. 6, pp , June [6] NGMN Alliance. (2016, 9) Descripion of nework slicing concep. [Online]. Available: hps:// NGMN Nework Slicing framework v1.0.8.pdf [7] P. Gaj, J. Jasperneie, and M. Felser, Compuer communicaion wihin indusrial disribued environmen a survey, IEEE Transacions on Indusrial Informaics, vol. 9, no. 1, pp , [8] M. Wollschlaeger, T. Sauer, and J. Jasperneie, The fuure of indusrial communicaion: Auomaion neworks in he era of he inerne of hings and indusry 4.0, IEEE Indusrial Elecronics Magazine, vol. 11, no. 1, pp , [9] L. Kleinrock, Queueing sysems, volume 2: Compuer applicaions. wiley New York, 1976, vol. 66. [10] M. Fidler and A. Rizk, A guide o he sochasic nework calculus, IEEE Communicaions Surveys & Tuorials, vol. 17, no. 1, pp , [11] J.-Y. Le Boudec and P. Thiran, Nework calculus: a heory of deerminisic queuing sysems for he inerne. Springer Science & Business Media, 2001, vol

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