Learning the Parameters of Periodic Traffic based on Network Measurements

Transcription

1 3rd IEEE Inernaonal Workshop on Measuremen & Neworkng (M&N), Combra - Ocober 25 Learnng he Parameers of Perodc Traffc based on Nework Measuremens Marna Guérrez Wlfred Sener TTTech Compuerechnk AG {marna.guerrez, wlfred.sener}@ech.com Radu Dobrn Saskumar Punnekka Mälardalen Unversy {radu.dobrn, saskumar.punnekka}@mdh.se Absrac The confguraon of real-me neworks s one of he mos challengng demands of he Real-Tme Inerne-of-Thngs rend, where he nework has o be deermnsc and ye flexble enough o adap o changes hrough s lfe-cycle. To acheve hs we have oulned an approach ha learns he necessary confguraon parameers from nework measuremens, ha way provdng a connuous confguraon servce for he nework. Frs, he nework s monored o oban raffc measuremens. Then raffc parameers are derved from hose measuremens. Fnally, a new me-rggered schedule s produced wh whch he nework wll be reconfgured. In hs paper we propose an analyss based on measuremens o oban he specfc raffc parameers and we evaluae hrough nework smulaons. The resuls show ha he confguraon parameers can be learned from he measuremens wh enough accuracy and ha hose measuremens can be easly obaned hrough nework monorng. I. INTRODUCTION Communcaons s a key elemen of he emergng paradgm of he Real-Tme Inerne of Thngs (RT-IoT), as we face scenaros n whch more and more sysems wll be nerconneced []. A use-case for hs s ndusral auomaon where he rend s owards shor produc developmen cycles and flexble manufacurng sysems [2]. Neworks for hose sysems need o be prepared o sasfy hese new demands whle sll beng able o provde deermnsc guaranees. In hs new scenaro he nework mus be capable o adap o all knds of changes: changes n he opology of he nework, addng or removng equpmen, changes for manenance purposes, or even changes n he funconaly of he nework. One way o acheve hs flexbly s hrough an agle reconfguraon of he nework. Currenly, he reconfguraon process of real-me neworks s cosly, boh n erms of me and engneerng effor [3]. Ths s especally rue for me-rggered neworks, ha provde deermnsc guaranees by schedulng he momens n me n whch messages are sen. The schedule s obaned off-lne and all he nformaon abou he nework opology and he applcaon requremens mus be known beforehand [4] [5]. Thus, hese characerscs lm he confguraon flexbly of a me-rggered nework. An approach ha addresses he lmaons of he merggered neworks confguraon s presened n [6] where he confguraon s seen as a connued servce o he nework. There we nroduce he Confguraon Agen ha learns he characerscs of he nework hrough connuous monorng and analyss of he raffc. Thus, he Confguraon Agen s able o updae he confguraon of he nework preservng he orgnal real-me requremens (e.g. maxmum ransmsson laency and jer) and oponally mprove hem (e.g. less laency and jer). CONFIGURATION AGENT EXTRACTOR Traffc Measuremens MONITOR SCHEDULER Traffc Parameers Schedule RECONFIGURATOR NETWORK Fg.. Confguraon Agen Overvew The Confguraon Agen (Fg. ) s composed of four elemens. The Monor observes he nework s raffc and gahers measuremens o denfy raffc paerns. The Exracor derves raffc parameers based on he raffc paerns observed by he Monor and prevous knowledge of he nework and applcaons. The Scheduler uses he raffc parameers obaned by he Exracor o generae a schedule for he nework, ha manans and mproves he deermnsc guaranees. Fnally, he Reconfguraor s n charge o updae he nework confguraon o follow he new schedule. Ths approach sees he confguraon as a connued process. Therefore, afer he nework has been confgured accordng o he new schedule, he Monor wll sar agan o gaher nformaon from he nework because he raffc parameers mgh have changed or wll change agan. One concree use-case of he Confguraon Agen s o auonomously re-confgure unsynchronzed raffc o synchronzed raffc. For hs, he Confguraon Agen can look for regulares on prevously unsynchronzed raffc and f deecs ha he raffc follows some paern wll be scheduled along wh he res of he synchronzed raffc. Ths ransformaon of unsynchronzed raffc o synchronzed raffc s algned wh he laes developmens of he IEEE 82. Tme-Sensve Neworkng (TSN) [7] ask group n whch a form of me-rggered communcaon s beng sandardzed n IEEE 82.Qbv [8][9]. Furhermore, he TSN s also lookng no new ways of nework reconfguraon

2 and for ha he concep of Sofware Defned Neworkng (SDN) [] s emergng. One of he man feaures of SDN s he cenralzed managemen of he nework whch makes a perfec archecure for he ncluson of he Confguraon Agen. In [] s even shown how exsng Eherne brdges as defned by he IEEE 82.Q sandard are already prepared o suppor SDN. In hs paper, he focus s on one of he seps of he Confguraon Agen approach: he exracon of raffc parameers from he measuremens gahered by he Monor. Through hese parameers we am o be able o recreae he orgnal realme consrans defned by he applcaons. Frs, n Secon III we expand he analyss of he raffc n he nework focusng on he raffc parameers ha we are neresed n obanng. Ths way we can defne more precsely he measuremens ha are needed. Then we use ha knowledge o develop a se of nework smulaons o es hs approach. The resuls of he smulaons can be found n Secon IV. Fnally n Secon V he conclusons and fuure work are presened. II. SYSTEM MODEL The mehod proposed n hs paper o learn raffc parameers from measuremens wll be appled o an exsng nework. Ths nework s composed by a mulude of nodes, N and swches, S. The nodes execue applcaons ha send frames, F hrough he nework. N N 2 N 3 f f2 f3 S f f2 f3 S 2 N 6 f f4 f f2 f3 f4 Fg. 2. Nework composed by sx nodes N - N 6 and hree swches, S - S 3. Nodes N - N 4 are sendng frames f - f 4 respecvely. Formally, he physcal opology of a nework s defned by an undreced graph G(V, E). Nodes and swches are verces v such ha v V and N S V. The physcal communcaon lnks connecng verces v and v j are edges, so ha (v, v j ) E. Fg. 2 depcs an example of a nework opology wh nne verces: sx nodes and hree swches. The raffc ha s sen hrough he nework s composed by frames of dfferen ypes (perodc, sporadc, bursy, ec) and dfferen crcales. For hs paper we are gong o focus jus on perodc frames, bu hs lmaon wll be removed n laer works. A frame, f, can be characerzed by s perod, lengh and prory: S 3 f4 f2 f3 f F : f {perod, lengh, prory } () Beng all frames perodc, he perodc repeons of he same frame can be grouped n flows. We call flow o he N 4 N 5 se of frames f ha are sen hrough he nework durng a me span L, so ha: flow {f ( ), f ( 2 ),..., f ( n )} (2) where,..., n are momens n me ha sasfy, j : j > j > j < L. The raffc n he nework s defned by he flows of frames. Snce he flows follow dfferen pahs hrough he nework, no all he flows / frames are gong o be vsble n every pon n he nework. In a gven swch s j we defne M sj as he se of frames ha he swch serves, hen he raffc paern, T P sj observed n he swch s he se of observed flows: T P sj {flow f M sj } (3) To furher descrbe our sysem model we have made he followng assumpons: ) The nodes execue applcaons, whch defne mandaory requremens and oponal desremens on he nework. 2) The nework s confgured n a way o mee all he mandaory requremens (e.g., maxmum ransmsson laency and jer) and may also sasfy oponal desremens (e.g., less laency and jer). 3) The change n he order of he frames whn he flow s no allowed as n accordance wh IEEE 82.Q [2]. III. NETWORK TRAFFIC ANALYSIS The confguraon agen needs o dsll ceran raffc parameers from he nework o be able o produce a schedule. In hs secon we descrbe hese parameers and see how we can oban hem by performng nework raffc analyss. A. Perod of he frames / flows In order o learn he perod of he frames / flows ha are sen hrough he nework we need o rack he me nsances a whch every frame from he flow s receved n a ceran pon n he nework. To do so, he Monor gahers he arrval mes of frames a every swch. Those measuremens can be expressed as follows: f f 2 f n f 2 f 2 2 f n 2 T sk (n, m). (4)..... f m f 2 m f n m Here f j represens he j-h frame of flow and f j he pon n me n whch frame f j arrves o swch s k. Therefore, m s he number of flows ha go hrough he swch s k and n s he number of frames of each flow. If he measuremens are aken durng a fxed me span L, hen n L/perod,.e., f perods are dfferen hen he marx (4) wll be an ncomplee marx, n whch no all enres are flled. The arrval mes of a gven frame o wo dfferen swches n he nework are no only dfferen because hey are wo evens ha happen one afer he oher, bu also because raffc paerns observed n each swch can be dfferen. We now llusrae hs wh an example.

3 [S,S 2 ] [N,S ] [N 2,S ] [N 3,S ] Fg. 3. Traffc observed n he npu pors of he swch S. [N 4,S 2 ] f f 2 f 3 f f 2 f 3 f 4 Fg. 4. Traffc observed n he npu pors of he swch S 2. In Fg. 2 we can see a smple nework ha consss of sx nodes (N, N 2, N 3, N 4, N 5 and N 6 ) and hree swches (S, S 2, and S 3 ). There are four applcaons usng he nework, sendng four perodc frames, f, f 2, f 3 and f 4 from nodes N, N 2, N 3 and N 4 respecvely. The Monor s placed n one of he swches, le us analyze wo of he hree possbles: The Monor could be gaherng he arrval pons n me of he frames on swch S (Fg. 3). Thus he observed raffc paern wll show hree frames clearly perodc. The Monor could be gaherng he arrval pons n me of he frames on swch S 2 (Fg. 4). Then he perodcy of he frame f s affeced by frames f 2 and f 3. Wh hs example we show ha he perod of he frames can no be obaned by a drec measuremen. We need nsead o measure for a long enough me span o oban T sk (n, ) { f, f 2,... f n } and hen average he nerarrvalmes as shown n (5). avg_perod j ( f j+ n B. Jer of he frames / flows f j ) f n f n In he prevous secon has been shown ha he nerarrval mes of a frames from he same flow o a gven pon n he nework are no always consanly equal o her perod. Somemes a frame from a flow, f, has o compee wh a frame from anoher flow, f k, for he use of he nework. In hese cases he swch has o decde whch frame s gong o be sen frs. Thus, f frame f k s sen frs, frame f s gong o be delayed for a ceran amoun of me. From now on, we refer o hs effec as an nerference, I, caused by frame f k on frame f. These nerferences make he laency of he frames vary, even whn he same flow. We defne hen he jer of a (5) flow as he dfference beween he maxmum and mnmum laency ha frames from ha flow suffer o reach a ceran pon n he nework. The jer s usually one of he man applcaon consrans ha should be aken no consderaon when confgurng he nework. We could agan use he arrval mes of frames o a swch, s k, T sk (n, ), o oban he jer, bu we would need an nal me reference ha could only be possble n a me synchronzed nework. Tha mgh no always be he case so we nsead calculae he ner-arrval mes beween consecuve frames of he same flow: f j j f j+ Thus, he perodc jer wll be hen: (6) J max( ) mn( ) (7) C. General analyss on he nerference Typcally real-me neworks use some mechansm o guaranee he deermnsm of he raffc wh he hghes crcaly. They could defne some me-rggered schedule [9], dfferen raffc classes [3] or a prory based schedulng [4]. Thus, learnng wha knd of mechansm has been used would be of grea help o have a clearer pcure of he nework. In Fg. 5 we can see how an nerference modfes he nerarrval mes. For each nerference we are gong o observe a longer han he orgnal perod of he frame, j 2 n he fgure, and anoher shorer han he perod, j. Ths dependency of he wh he nerferences can be modeled as: j perod + (C j+ C j ) (8) where C j and C j+ are he duraon of he nerferences suffered by frames f j j+ and f respecvely. Jus f boh frame f j and frame f j+ have no suffered nerferences j perod. D j-2 D j- D j D j+ Fg. 5. Inerference suffered by frame f j C no only reflecs he nerferences suffered by he frame n he swch, bu he accumulaed nerferences suffered from he frame on s pah o he swch. To oban more nformaon from he nerferences we make he Monor rack no only he arrval mes of he frames o he swch, bu also he mes n whch he frames are sen. Thus n every swch here wll be a second mes-marx as he one n (4), bu for he sen mes. To dfferenae boh ses of mes we noe a as he me n whch frame f j f j arrves o he swch and s as he me n whch s sen. f j The dfference beween hose mes gve us he amoun of me ha he frame has o wa n he swch unl s sen o s desnaon (9).

4 C j s a f j f j (9) hos 2 The value of C j reflecs he duraon of he nerferences suffered by he frame n ha specfc swch. Of course, hs value ncludes, no only he possble nerferences, bu also he me ha he swch needs o process he frame. Through hese wo vews on he nerferences we can nfer prory relaonshp beween frames, whch help us o re-enac he orgnal nework schedulng paradgm. swch swch8 6 5 swch2 3 swch3 4 swch7 D. Precedence relaons beween frames / flows Precedence relaons beween frames are all requesresponse knds of communcaon as, for example, he communcaon ha s esablshed beween a sensor and an acuaor. To ry o learn hese relaonshps we need he Monor o gaher he desnaon of each frame. Ths s somehng feasble as ha nformaon s usually par of he frame header as occurs wh Eherne frames [5]. Wh he nformaon abou he desnaon we can denfy he "edge swches",.e, he swches ha are drecly conneced o he nodes. Knowng hs and usng agan he arrval and sen mes of he frames from he swches we denfy he precedence relaons followng hese seps: ) A frame from flow a arrves o he node N a me a. 2) If a frame from anoher flow flow b s sen from N a a me b > a hen we calculae he delay d b a. 3) We repea he process for every perodc arrval of he frame from flow a 4) If d s consan for every frame of he flow, hen we conclude ha here s a precedence relaon beween hose flows. IV. RESULTS To evaluae he proposed approach we have creaed an Eherne nework smulaon usng OMNeT++ [6] ogeher wh he INET framework [7]. Fg. 6 depcs he opology of he smulaed nework: s a nework composed of 4 nodes (hoss) and 2 swches. I has a sze ha allow us o reason over whle sll beng generc and complex enough o provde meanngful resuls. As can be seen from he analyss of Secon III he sze of he messages s no drecly used o derve he raffc parameers. Neverheless here are some dfferences beween havng dfferen messages szes n he nework or havng all he messages of he same sze, especally when comes o undersand he nerferences. Thus wo dfferen ses of smulaons has been performed: wh random message szes (5-4 byes) and sngle message sze (329 byes). The daa rae s se o Mbps. The frames ha are sen hrough he nework have perods ms, 2 ms and 3 ms. Smulaons have been performed for dfferen duraons and wh dfferen raffc loads. The smulaon ool gves as an oupu he race of he frames,.e., he he momens n me n whch he frames pass by he dfferen nodes / swches n her pah hrough ulzaon hos34 swch 7 swch5 swch8 swch7 swch9 9 8 frame pah Fg. 6. Nework used for he smulaon ess. u 93 % (4 flows) u 82 % (2 flows) u 72 % ( flows) u 55 % (8 flows) u 3 % (4 flows) u 2 % (2 flows) u 3 % ( flow) lnks ulzaon lnks u 8 % (4 flows) u 78 % (3 flows) u 55 % (2 flows) u 22 % ( flows) u 3 % ( flow) lnks Fg. 7. Ulzaon of he lnks ha frames from flow goes hrough. Lef: same message sze. Rgh: dfferen message sze. he nework. Ths oupu can be confgured o any oher measuremen ha we need o rack and herefore fulflls he role of he Monor n hese evaluaon ess. To analyze he resuls we focus on he frames from he flow ha goes from hos o hos34 hrough lnks o. As can be seen n Fg. 6 he frames of hs flow, flow are he ones ha go hrough he hghes number of and herefore can suffer he mos sgnfcan nerferences. Fg. 7 shows he ulzaon of he lnks o for every dfferen es ha has been performed. As expeced from he opology of he nework, he lnks wh he hghes ulzaon are he cenral swches hrough whch he hghes number of frames are relayed. The ulzaon of he lnks has been calculaed akng no accoun he number of frames ha use he lnk, he ransmsson me of each frame,.e., rans lengh/daarae and he hyperperod of all he frames on he lnk, n: U l rans hyperperod perod hyperperod ()

5 perod / ms u 93 % (4 flows) u 82 % (2 flows) u 72 % ( flows).9998 u 55 % (8 flows) u 3 % (4 flows) u 2 % (2 flows) u 3 % ( flow) perod / ms u 8 % (4 flows) u 78 % (3 flows) u 55 % (2 flows) u 22 % ( flows) u 3 % ( flow) Fg. 8. Average perod of flow obaned n dfferen swches usng equaon (5) (orgnal perod: ms). Lef: same message sze. Rgh: dfferen message sze. A. Perods of he frames / flows In Fg. 8 he average perod of frames from flow as obaned n every swch ha he frame goes hrough s depced. We observe wha mgh be seen as an errac behavor of he values for he perod. Inuvely one would expec ha he values dffer more from he real perod as he ulzaon of he nework ncreases. Bu s mporan o remember ha he perod s calculaed usng equaon (5). There, n he numeraor s he sum of he nerarrval mes of he frames o he swch. We have seen n Secon III-C ha due o he perodc naure of all he frames n he nework, he nerarrval mes can be seen as he real perod of he frame modfed wh he duraon of he nerference ha suffers (see equaon (8)). If we subsue hs n equaon (5), we have: avg_perod j ( f j+ n f j ) j [perod + (C j+ C j )] n perod + perod + Cn C n j (Cj+ C j ) n () Ths means ha he wors case for he perod wll be for n 2 and from here he value ncreases and decreases and he amplude of hese oscllaons decays wh he me lke damped oscllaons, as can be seen n Fg. 9. In he lef sde of ha same fgure we show how he values obaned for he perods ge closer o he real value as he smulaon me ncreases. perod / ms L 3. s L 2.5 s L 2. s L.5 s L.8 s L.6 s u 55 % (8 frames) L.2 s me / ms Fg. 9. Average perod of flow obaned usng equaon (5) (orgnal perod: ms). Lef: n swch8 durng he frs ms of smulaon. Rgh: n dfferen swches wh smulaons of dfferen duraon. perod / ms Taken all hs no consderaon he mporan pon n Fg. 8 s ha for all cases he value obaned n each swch s always close enough o he orgnal perod. Ths means ha n a more realsc scenaro n whch no all he swches can be monored, even f here s jus one monored swch n he flow s pah he value obaned for s perod wll have a relave error of less han.3%. B. Inerference analyss Fg. depcs he laency of frames from flow as hey go hrough he nework. As can be seen wh jus one flow beng sen hrough he nework he delay beween one swch and he followng s always equal o he expeced ransmsson me of a frame rans lengh/daarae. When he raffc n he nework ncreases, he delay beween swches also ncreases as a consequence of he nerferences ha he frame suffers. Concreely, he boleneck of hs nework can be seen as he major delay s produced when he frame raverse he cenral swches. me / ms u 93 % (4 flows) u 82 % (2 flows) u 72 % ( flows) u 55 % (8 flows) u 3 % (4 flows) u 2 % (2 flows) u 3 % ( flow) me / ms u 8 % (4 flows) u 78 % (3 flows) u 55 % (2 flows) u 22 % ( flows) u 3 % ( flow) Fg.. Average laency of frames from flow. Lef: same message sze. Rgh: dfferen message sze. Fg. gves a lo of nformaon abou he nework, bu s mporan o noce ha hs fgure can only be produced f here s nework-wde me synchronzaon. The smulaon ool assumes perfec me synchronzaon so n hs case he mes obaned n dfferen swches are comparable. However n a real nework ha mgh no be he case, hus o oban more nformaon abou hese nerferences we apply he wo mehods descrbed n Secons III-B and III-C. For ha we use he daa from he smulaon wh a sngle message sze, u3% and agan we focus on flow. In Fg. we can see he nerferences suffered by frames from flow n swch7 as obaned wh boh mehods. The lef plo represens he classfcaon of he j calculaed usng equaon (6). We can hen apply equaon (7) o oban he jer of frames from flow n swch7: J ms The columns on he rgh plo represen he dfferen nerferences suffered by frames from flow n swch7. The frs column represens he frames ha have no suffered nerferences and ha ake 26.95µs o arrve from he prevous swch. The second column represens he frames ha have suffered one nerference of 55.52µs 26.95µs 28.56µs. Ths value s congruen wh he expeced value of he duraon of an nerference cause by a sngle frame,.e. he ransmsson me of one frame. Thrd column corresponds hen wh he

6 frames ha have suffered wo nerferences, and so on. As was explaned n Secon III-C hese nerferences are always local o he swch,.e., hey are jus he nerferences suffered by he frame when gong from swch3 o swch7. On he oher hand, he rgh plo of Fg. reflecs he accumulaed nerference ha has suffered he frame unl arrves o swch7. I shows larger nerferences han he ones we see n he lef plo, whch ndcaes ha he frames suffered more nerferences before arrvng o swch / ms Fg.. Inerferences suffered by frames from flow n swch7 (u3%). The analyss of rgh and lef plo ogeher shed some lgh on boh he nerferences and show ha hese mehods can be used o denfy he underlyng schedulng paradgm as menoned n Secon III-C. C. Precedence relaons beween frames / flows We have appled he mehod descrbed n Secon III-D o learn precedence relaonshps for all he smulaon ess. The resuls obaned have been a complee success as we have been able o denfy % of precedence relaonshps n all cases. And jus for he smulaon wh u93% we have had a few false posves. In spe of hese good resuls we have spoed a few shorcomngs ha need o be addressed. Frs, he proposed mehod dslls he precedence relaonshps usng daa jus from he edge swches. Ths assumes ha daa from hose swches s always gong o be avalable, whch mgh no always be he case. So he mehod should be exended o every swch n he nework and s performance should be re-evaluaed. Second, wh hs mehod we only see local precedence relaonshp beween frames, means ha we can only denfy ypes of communcaon ha are N sends reques o N 2 - N 2 sends response o N. However precedence relaons can be much more complex han ha and no necessarly crcular, could be N sends reques o N 2 - N 2 sends response o N 3 - N 3 sends reques o N 5 - ec. Therefore he mehod should be exended o be capable of deecng all knds of precedence relaonshps. C / ms V. CONCLUSIONS AND FUTURE WORK In hs paper we have dscussed he Confguraon Agen as a mehod for flexble confguraon and re-confguraon of real-me neworks. In parcular, we have focused on how o exrac raffc parameers of perodc raffc by means of raffc monorng. We have demonsraed by smulaon ha he presened approach allows us o learn he perods of he frames and he nerferences ha hey suffer. Mehods, lke he one presened, have a poenal o become a key acceleraor for he adopon of sandardzed me-rggered proocols such as he ongong IEEE 82. Tme-Sensve Neworkng projecs as hey allow o auomaze he ranslaon from currenly unsynchronzed o fuure synchronzed neworks. Curren effors are focused on refnng he algorhms used o exrac he raffc parameers and expandng he curren sysem model o nclude more ypes of raffc (aperodc, sporadc, bursy, ec.). The challenge here s boh o be able o dsngush he dfferen raffc ypes, as well as o fnd he relevan raffc parameers for each. We beleve ha he framework presened n hs paper o learn he raffc parameers from he nework s flexble enough for hs purpose. ACKNOWLEDGMENT The research leadng o hese resuls has receved fundng from he People Programme (Mare Cure Acons) of he European Unon s Sevenh Framework Programme FP7/27-23/ under REA gran agreemen REFERENCES [] L. Azor, A. Iera, and G. Morabo, The Inerne of Thngs: A survey, Compuer Neworks, vol. 54, no. 5, pp , Oc. 2. [2] P. Vrba and V. Mark, Capables of dynamc reconfguraon of mulagen-based ndusral conrol sysems, Sysems, Man and Cybernecs, Par A: Sysems and Humans, IEEE Transacons on, vol. 4, no. 2, pp , March 2. [3] L. Dürkop,, J. Jaspernee, and A. Fay, An Analyss of Real-Tme Ehernes Wh Regard o Ther Auomac Confguraon, n h IEEE World Conference on Facory Communcaons Sysems (WFCS), 25. [4] S. Poledna, H. Kopez, and W. Sener, Deermnsc Sysem Desgn wh Tme-Trggered Technology, n Mcroelecroncs Sysem Symposum 24 (MESS4), 24. [5] W. Sener, G. Bauer, B. Hall, and M. 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