Adaptive Network Resource Management in IEEE Wireless Random Access MAC

Transcription

1 Adaptve Network Resource Management n IEEE Wreless Random Access MAC Hao Wang, Changcheng Huang, James Yan Department of Systems and Computer Engneerng Carleton Unversty, Ottawa, ON, Canada Abstract Effectve and effcent management of wreless network resources s attractng more and more research attenton, due to the rapd growng deployment of wreless mesh and ad hoc networks and to the ncreasng demand for Qualty of Servce (QoS) support n these networks. Ths paper proposes an adaptve network resource management scheme n the popular IEEE random access MAC by adaptvely adustng the mnmum contenton wndow szes of traffc flows. Frst, a novel Generalzed Processor Sharng (GPS) model s presented for the IEEE random access MAC revealng the relatonshp between the mnmum contenton wndow sze of a traffc flow and the amount of network resource ths flow can receve. Usng ths GPS MAC model, a feedback control system model for the proposed adaptve network resource management system s developed, by drectly extendng our prevous work n wrelne GPS networks. Based on the feedback control system model, adaptve P and adaptve PI controllers are desgned, and ther performances are studed n smulatons. Smulaton results show that by usng the desgned controllers, the proposed adaptve network resource management approach s able to provde guaranteed dstnct QoS support to traffc flows. I. INTRODUCTION In recent years, Wreless Local Area Networks (WLANs) have become ncreasngly popular, both as an extenson to the exstng wrelne networks and as stand-alone applcatons. WLANs are expected to play an mportant role n future everyday communcaton provdng support for both best effort and qualty of servce (QoS) senstve servces. In order to provde effcent QoS support, network resources must be effectvely managed and allocated to traffc flows. The medum access control (MAC) manages the network resources n wreless networks. There are usually two types of MAC,.e. centralzed and dstrbuted. Although centrally controlled MAC, such as the PCF n IEEE , makes t easer to provde QoS support, they are hardly mplemented n most exstng wreless devces. Ths s mostly because of ther hgher complexty, neffcency for normal data traffc, lack of robustness and assumptons on global synchronzaton. In addton, such centralzed MAC can only work n nfrastructure-based wreless LAN, where a central control access pont s avalable, but t cannot be appled n nonnfrastructure-based ad hoc networks. In contrast, the dstrbuted contenton-based random access MAC, such as the DCF n IEEE , receved great acceptance among end users, because t s smple, robust, plug and play, and t allows fast nstallaton wth mnmum network management and mantenance cost. It s very lkely that the contenton-based random access MAC wll reman the domnant wreless MAC n the future. However, n legacy IEEE random access MAC, only best-effort type of servce s supported. Gven the ncreasng growth of QoS demandng real-tme applcatons, such as voce and vdeo, t s necessary to study how to provde QoS support n such dstrbuted random access MAC. One approach for provdng QoS n dstrbuted random access MAC s measurement-based admsson control, such as VMAC [1] and SWAN [2]. However, because the wreless medum s shared among all nodes n the same contenton neghborhood, the arrval of a new flow at one node wll affect the delay of all other flows on all nodes n ts contenton neghborhood. Therefore, such admsson control based approaches cannot effectvely support delay QoS requrement [3]. Another approach s to mtate the centralzed schedulng algorthms n wrelne networks by exchangng nformaton among nodes n a contenton neghborhood [12] [13]. But such approach causes hgh message overhead. In addton, because the transmsson range of a wreless node s usually much smaller than ts carrer-sensng range, usng ths approach, a node may only receve nformaton from a small porton of nodes n ts contenton neghborhood. Therefore, the effectveness of ths approach s greatly lmted. A thrd approach explores the fact that the contenton wndow sze of a traffc flow n IEEE random access MAC determnes how much network resource ths flow can get n the contenton process. IEEE e [14] mplements class-based QoS dfferentaton, whch s smlar to DffServ n wrelne IP networks. DWTP [15] and DFS [16] acheve proportonal delay dfferentaton and proportonal throughput dfferentaton respectvely. But these methods cannot guarantee satsfyng the flow-level QoS requrements. In [3] and [4], the contenton wndow szes of the flows are adaptvely adusted n order to acheve guaranteed perflow QoS requrement. But, the guaranteed QoS n [4] s the maxmum packet delay, whch s over conservatve, snce most real-tme applcatons can tolerate a porton of ts packets havng delay greater than the requred delay bound [25]. In [3], the guaranteed QoS s the average contenton delay of the packets, whch s only part of the entre average packet delay 1. Although the authors show that the average packet delay s determned by the average contenton delay, t 1. The entre average packet delay was decomposed nto three components, average queueng delay, average transmsson delay and average contenton delay.

2 s not easy to calculate the proper average contenton delay requrement gven the requrement on average packet delay. Therefore, t s dffcult to apply ths method n real networks. In addton, the adaptve contenton wndow adustment algorthms proposed n [3] and [4] are only heurstc. There s no analytcal model proposed for studyng the performance of the adaptve contenton wndow adustment algorthms. Ths paper proposes an adaptve network resource management scheme n the popular IEEE random access MAC by adaptvely adustng the mnmum contenton wndow szes of traffc flows. There are four maor contrbutons of ths paper. Frst, we present a novel GPS model for the popular IEEE random access MAC. Second, we extend our prevous work n wrelne GPS networks and develop a feedback control system model for the proposed adaptve network resource management system, whch s essental for the analytcal study of the adaptve contenton wndow adustment algorthms. Thrd, dfferent controllers are desgned based on the feedback control system model, and ther performances are evaluated n smulatons. Fnally, the QoS requrement used n ths paper s gven n a statstcal form by the packet delay volaton rato (DVR), whch s the rato of packets experencng delay greater than the requred delay bound. By usng such a statstcal QoS requrement, the network resource needed by a traffc flow s much less than when usng a determnstc QoS requrement [26]. Therefore, the effcency of the networks can be greatly mproved. The remander of ths paper s organzed as follows. Secton II brefly descrbes the MAC studed n ths paper. Secton III ntroduces the GPS model developed for the IEEE random access MAC, revealng the relatonshp between the mnmum contenton wndow sze of a traffc flow and the amount of network resource ths flow can receve. Usng ths model and by extendng our prevous work n wrelne GPS networks, a feedback control system model for the proposed adaptve network resource management n IEEE wreless random access MAC s presented n secton IV. In secton V, adaptve P and adaptve PI controllers are desgned, and ther performances are studed n smulatons n secton VI. Due to space constrant, the smulatons n ths paper are lmted to the sngle hope case. Secton VII concludes the paper and dscusses future research drectons. II. IEEE random access MACs, ncludng DCF and EDCA, are the most popular MACs, and they mplement a dstrbuted CSMA/CA contenton-based random access network resource management scheme. The IEEE random access MAC studed n ths paper s not lmted by the specfcatons of any sngle standardzed MAC protocol, but s based on a generalzed verson of all dstrbuted CSMA/CA contenton-based random access MAC, as shown n Fg. 1. In ths MAC, a wreless node can have multple traffc flows, and each flow has ts own queue and ts own contenton wndow assocated wth ts queue. Each flow contends OVERVIEW OF THE MAC ndvdually wth all other flows n ts contenton neghborhood, both nter- and ntra-node, for the network resource usng the contenton wndow mechansm as f each flow s an ndvdual wreless node n IEEE DCF. In ths generalzed MAC, f all traffc traversng a wreless node s aggregated and treated as a sngle flow, ths MAC turns nto DCF. If traffc s separated nto dfferent classes and each class s treated as an ndvdual flow, ths MAC becomes EDCA. Traffc flows can also be defned by the source and destnaton IP and port numbers, as t s done n wrelne networks. MAC PHY ntra-node contenton Fgure 1 nter-node contenton Generalzed MAC. In the next secton, we wll present a novel GPS model developed for ths generalzed random access MAC. III. GPS MODEL OF THE MAC Exstng research on the performance of the IEEE MAC has been prmarly focused on the packet level network throughput [21][22][24]. But such results are of lttle help n provdng QoS for traffc flows n ad hoc wreless networks. [23] studes the performance of DCF usng the G/G/1 queueng model and computes the probablty dstrbuton functon of the flow level packet delay. But t does not support flow level servce dfferentaton. In addton, t s computatonally very complex and s of lmted help for obtanng deeper understandng of the random access MAC. [17] and [18] propose to analyze DCF and EDCA usng the Processor Sharng (PS) model at the flow level. But because such CSMA/CA based random access MAC s very dffcult to analyze, no analytcal result on flow queue dstrbuton and packet delay dstrbuton s produced. Because of the absence of a good analytcal model of the wreless MAC, QoS support n wreless ad hoc networks s dffcult to quantfy and optmze. To help address ths problem, n ths paper, we propose a GPS model for analyzng the queue tal behavor n IEEE random access MAC. The mportance of havng ths model s that t makes extendng some of the wrelne analytcal results nto wreless ad hoc networks possble, and such results would be useful n provdng QoS support n wreless ad hoc networks We frst start from the network saturaton mode. When the wreless network s n the saturaton mode, every flow s ntra-node contenton

3 backlogged, whch means that there are always packets n the queue of every flow watng to be transmtted by the MAC layer. Let s and s represent the expected servce rate for flow and flow n the same contenton neghborhood when the network s n the saturaton mode. It s shown n [20] that s and s have the followng relaton: s E[ L ]/ W =, (1) s E[ L ]/ W n whch, W s the mnmum contenton wndow sze of flow ; and L s the extended packet sze of flow, whch s the channel transmsson rate multpled by the total duraton of a successful transmsson of a flow packet, ncludng DIFS/AIFS, SIFS, and RTS/CTS/DATA/ACK handshake. A new feature, called TXOP, s ntroduced n EDCA, whch allows a traffc queue to transmt multple packets contnuously after wnnng one contenton. These contnuously transmtted packets can be treated as an aggregated sngle packet. Therefore, f TXOP s employed, the extended packet sze of a flow would become adustable by changng ts TXOPlmt value. However, most actual wreless networks are runnng n non-saturaton mode. Let S ( τ, t) and S ( τ, t) be the actual amount of traffc served for flow and flow n tme nterval [ τ, t], durng whch flow s always backlogged. Because accessng the transmsson medum s contenton-based, the maxmum amount of traffc that flow can transmt s acheved only when flow s backlogged all the tme durng ths tme nterval. Whch means that the value of S ( τ, t) / S ( τ, t) s mnmzed only when flow s also always backlogged durng [ τ, t]. When flow and flow are both always backlogged, they can be treated the same as n the saturaton mode, therefore equaton (1) holds n ths tme nterval [ τ, t]. Because the MAC s CSMA/CA based random access, the amount of traffc served for flow and flow n the tme nterval [ τ, t] are random numbers. However, when t >> τ, ths amount can be approxmated by the product of the expected servce rate and the servce tme nterval followng the Law of Large Numbers. Therefore, we have the followng S ( τ, t) s mn{ } S ( τ, t) s ( t τ ) E[ L ]/ W = ( t τ ) E[ L ]/ W, ( t >> τ ). (2) Let φ = E [ L ]/ W and φ = E [ L ]/ W, then (2) can be rewrtten as: S ( τ, t) φ, ( t >> τ ). (3) S ( τ, t) φ From (3), one can show that flow s guaranteed a mnmum backlog clearng rate of rˆ ( e( u( C(Z) + _ g φ = N =1 φ f(u) + r( R, (4) n whch R s the total throughput of the contenton neghborhood of flow when the network s n saturaton mode. Note that (3) s n the exact form as a GPS scheduler n wrelne networks, whch means that when t >> τ, the IEEE random access MAC can be modeled as a GPS scheduler,.e. the queue tal behavor n the IEEE MAC should match the queue tal behavor n wrelne GPS scheduler. Ths GPS model for IEEE has been verfed n smulaton. More detals can be found n [7]. IV. FEEDBACK CONTROL SYSTEM MODEL In [8], a feedback control system model was proposed for adaptve bandwdth provsonng n wrelne GPS networks. In the prevous secton, t has been shown that the contentonbased IEEE random access MAC can be modeled as a GPS scheduler. Thus, ths wrelne feedback control system model can be drectly extended to wreless networks. Fgure 2 ε( r'( EWMA ( 1 β )Z Z β Block dagram of the feedback control system model. r ( n ) Fg. 2 shows the block dagram of the feedback control system model for adaptve network resource management proposed n ths paper, whch s almost the same as the one n [8]. The nput of the system s r ˆ(, whch s the desred DVR of the flow. The output of the system s r ( n ), whch s the result of passng r'( through an Exponental Weghted Movng Average (EWMA) flter wth parameter β. r'( s the actual DVR of ths flow measured durng the tme nterval [(n-1)t, nt], n whch T s the control update nterval. Ths DVR measurement s performed at the recevng end of ths flow by countng the total number of packets receved and the number of packets that are lost or have packet delay greater than the delay bound. The dfference between r ˆ( and r ( n ), denoted as e(, s used as the nput to the controller C(Z). The output of the controller s u( = 1/w(, n whch w( s the mnmum contenton wndow sze of ths flow. The block f(u) s the mappng between the controller output u( and the resultng DVR experenced by ths flow r(. It should be ponted out here that r( s the long term steady state DVR of the flow assumng that u( and the characterstcs of ths flow as well as all other competng flows n the network are kept constant. Therefore the mappng between u( and r( s determnstc,.e. for every specfc value of u(, there s a correspondng determnstc specfc value of r(. However, because of the stochastc nature of the

4 networks, as well as the lmted fnte duraton of the control nterval, the actual measured DVR of the flow at the end of each control nterval, denoted as r'(, s not determnstc but rather a random process, whch can be modeled as the result of combnng the determnstc r( wth a random nose sgnal ε(. The functon of the EWMA block after r'( s to act as a low pass flter flterng out the random nose ε( wthn r'( and to generate r ( n ), whch s an estmaton of r(. The tal queue dstrbuton of wrelne GPS scheduler was studed n [19], and t s shown that for an exponentally bounded burstness (EBB) flow, ts tal delay dstrbuton can be bounded n the followng form 2, * φ d r = f ( d) = Pr{ D d} Λ e γ, (5) n whch D s the packet delay of ths flow, φ s the weght * assgned to ths flow n the GPS scheduler, and Λ and γ can be calculated usng equatons gven n [19]. As shown n secton III, when t >> τ, the IEEE random access MAC can be modeled as a GPS scheduler. Therefore, the queue tal behavor n the IEEE MAC should match the queue tal behavor n wrelne GPS scheduler. Thus, (5) also holds n IEEE MAC. Snce we have φ = E [ L ]/ W and u = 1/ W n IEEE MAC, as shown n secton III, the tal delay dstrbuton n IEEE random access MAC can be rewrtten from (5) to the followng form, * * α d u r = Pr{ D d} = f ( u ) Λ e, (6) n whch r s the DVR experenced by ths flow ; u s the output of the controller, whch s the nverse of the flow s contenton wndow sze; D s the packet delay of ths flow; d s the requred packet delay bound; and * * Λ and α are constant numbers that can be calculated By adoptng the method used n [8], the feedback control system model s lnearzed by approxmatng the mappng functon f(u) wth a lnear functon: f ( u) Ku + B, (7) n whch K > 0, and B s a constant number. The block dagram of the lnearzed system model s shown n Fg. 3. rˆ ( e( u( + _ Fgure 3 C(Z) -K + r( ε( r'( EWMA ( 1 β )Z Z β Lnearzed feedback control system model. r ( n ) The same as n [8], the specfc value of K s stll unknown. For dfferent flows n dfferent network condtons, K can have dfferent values. It has been shown n [8] that for such a system, t s very dffcult to desgn a fxed controller that can 2. The notaton here s slghtly dfferent from what s used n [19]. work well for varous flows and network condtons. Therefore, by followng the approach n [8], adaptve P and adaptve PI controllers are desgned n the next secton. V. CONTROLLER DESIGN A. Adaptve P Controller The block dagram of the system wth adaptve P controller s shown n Fg. 4, n whch K s the estmaton of K usng the method n [8]. K ( = (1 β ) K' ( + β K' ( n 1), (8) r ( r ( n 1) K ( = u ( u. (9) ( n 1) Ths estmaton of K s then used to adaptvely adust the parameter K of the P controller usng the followng rule, K P ( n + 1) = Gol / K(. (10) n whch, G ol s the desred open-loop gan of the system, whch s determned by how much steady state error s acceptable n the system output. rˆ ( e( + _ Fgure 4 Lnearzed system block dagram wth adaptve P controller. B. Adaptve PI Controller Based on the lnearzed feedback control system model, we can tell that ths system s a frst order system. From control theory, t s known that usng P controllers n ths system wll result n steady state error n the system output. Smulaton results n [8] also confrm ths. In order to elmnate the steady state error n the system output, an adaptve PI controller s desgned. The block dagram of the system wth adaptve PI controller s the same as the one wth adaptve P controller shown n Fg. 4. The only dfference s that an ntegral part s added nto the controller so that the transfer functon of the controller s changed from K to K PI [ 1 + γ ( Z + 1) /( Z 1)]. P K PI Smlar to the adaptve P controller case, the value of s adusted usng the followng rule: K ( n + 1) G / K (. (11) PI = In the next secton, the performances of adaptve P and adaptve PI controllers are evaluated n smulatons. The ol ε( u( + EWMA K P K (1 β )Z r( r'( Z β EWMA (1 β )Z u ( n ) Z β K ( n ) K estmaton r ( n )

5 controller parameter values used n ths paper are the same as those used n [8]. VI. SIMULATIONS The smulatons are mplemented n ns2. Due to space lmt, ths paper contans only sngle hop smulatons. In the smulaton, there are eght pars of wreless nodes n a sngle hop IEEE WLAN, whch has a transmsson rate of 2 Mbps. All wreless nodes are wthn each other s one-hop transmsson range. There are eght traffc flows n ths WLAN, fve of whch are QoS senstve flows and the rest three of them are best-effort data traffc. The fve QoS flows are smulated voce traffc usng exponental on-off sources. Durng the on perod, the sources send out packets at the rate of 64 Kbps, and the sze of each packet s 84 Byte. The sources do not send out any packet durng the off perod. The mean on tme and mean off tme of the sources are 350 ms and 650 ms respectvely. The three data flows are generated usng the well-known BellCore trace [27] and ther mean rates are randomly set between 30 Kbps and 100 Kbps. In ths paper, the QoS requrement of traffc flows are gven n the form of the requred packet delay bound and the maxmum acceptable DVR. For the same type of traffc, the end-to-end QoS requrements should be the same. But, snce the traffc flows may travel through dfferent number of hops, ther per-hop QoS requrement could be dfferent. To consder ths effect, n our smulaton, the QoS requrements of the fve voce flows are randomly selected from the followng two, (100 ms, 1.0*10-3 ) and (50 ms, 0.5*10-3 ). To evaluate the performance of the controllers desgned n secton V, three smulaton scenaros are mplemented. In the frst scenaro, the feedback adaptve network resource management s not mplemented. The MAC parameters are set up usng the default e values. Voce traffc belongs to AC[3], and t has CW_MIN[3] = 7 and AIFS[3] = 2. Besteffort data traffc belongs to AC[0], wth CW_MIN[0] = 31 and AIFS[3] = 7. The second scenaro mplements the adaptve P controller desgned n secton V. The adaptve network management scheme s appled only on the fve voce flows. The ntal values of CW_MIN for all fve voce flows are set to 31, and the adustment range of CW_MIN s lmted to between 31 and 7. The data flows have fxed CW_MIN = 31. The values of AIFS for the voce flows and data flows are set to 2 and 9 respectvely, so that when the voce flows demand more network resource by decreasng ther CW_MIN values, the data flows can be blocked from accessng the wreless channel and therefore gve more resources to voce flows. When the voce flows have CW_MIN = 7, the data flows can be completed blocked. Ths s also referred to as QoS protecton. The adaptve PI controller s mplemented n the thrd scenaro. The settng n ths scenaro s the same as n scenaro 2, except that the control algorthms are dfferent. Fg.5 and Fg. 6 show the smulaton results of two voce flows wth the QoS requrements of (100 ms, 1.0*10-3 ) and (50 ms, 0.5*10-3 ) respectvely. Fgure 5 Fgure 6 QoS requrement: (100 ms, 1.0*10-3 ) System output for a voce flow usng dfferent controllers, wth QoS requrement (100 ms, 1.0*10-3). QoS requrement: (50 ms, 0.5*10-3 ) System output for a voce flow usng dfferent controllers, wth QoS requrement (50 ms, 0.5*10-3). The smulaton results for scenaro 1 are shown by the doted lnes n these two graphs, labeled as no feedback. In ths scenaro, the two voce flows both belong to class AC[3] and are confgured usng the default e settng, and there s no feedback adaptve adustment. It can be seen that the actual QoS experenced by the voce flow n Fg 5 s better than ts requrement, whch ndcates that the amount of network resource obtaned by ths flow s more than what t actually needs. However, the QoS experenced by the voce flow n Fg.6 does not meet ts requrement, whch means that not suffcent amount of network resource s acqured by ths voce flow. The reason for ths s because the voce flow n Fg. 6 has a strcter QoS requrement than the voce flow n Fg. 5, thus t requres more network resource. Therefore, ths scenaro shows that by usng statc settng, even wth IEEE e type of QoS support, there s no guarantee on the QoS receved by traffc flows. It s very possble that some flows are not gettng suffcent network resource whle some other flows are over-provsoned. In Fg. 5 and Fg. 6, the smulaton results of scenaro 2 and 3, usng adaptve P controller and adaptve PI controller, are shown by the dashed lne and sold lne respectvely. Compared wth the results of scenaro 1, t can be seen that by

6 usng adaptve network resource management, the voce flow n Fg. 5 s no longer over-provsoned, and the voce flow n Fg. 6 can obtan enough network resource to meet ts QoS requrement. In Fg. 6, t can also be observed that when usng adaptve P controller, there s a steady state error n the system output. Ths steady state error s removed when adaptve PI controller s appled. There s no such observaton n Fg. 5, but the reason for ths s due to the contenton wndow adustment range constrant we mplemented n these two scenaros. The smulaton results of scenaro 2 and 3 match our earler analyss n ths paper, and t s also n accordance wth our studes n wrelne networks [8]. VII. CONCLUSIONS Ths paper studes adaptve network resource management n IEEE wreless random access MAC. We propose to adaptvely adust the mnmum contenton wndow szes of traffc flows n order to meet ther QoS requrements. We present a novel GPS model for the popular IEEE random access MAC, revealng the relatonshp between the mnmum contenton wndow sze of a traffc flow and the amount of network resource ths flow can receve. Usng ths GPS MAC model, a feedback control system model for the proposed adaptve network resource management system s developed, by drectly extendng our prevous work n wrelne GPS networks. Based on the feedback control system model, adaptve P and adaptve PI controllers, whch are smlar to our prevously desgned controllers n wrelne networks, are desgned n ths paper. Smulaton results show that by usng the desgned controllers, the proposed adaptve network resource management approach can effectvely meet the QoS requrements of the traffc flows, and the performances of the controllers also match our analyss. Further research that are currently beng conducted nclude more extensve studes on the proposed approach and the system model n dealng wth more complex traffc and network condtons, such as n mult-hop ad hoc networks. Other mportant related ssues nclude the effect of the proposed approach on network throughput, the effect of mperfect measurement on the system performance, solutons for the admsson control of traffc flows, moblty handlng, flow balancng, etc. REFERENCES [1] M. G. Barry, A. T. Campbell, and A. Veres. Dstrbuted control algorthms for servce dfferentaton n wreless packet networks. IEEE INFOCOM, [2] G.S. Ahn, A. Campbell, A. Veres, and L.H. Sun. SWAN: Servce dfferentaton n stateless wreless ad hoc networks. IEEE INFOCOM, [3] Y. Yang and R. Kravets. Achevng delay guarantees n ad hoc networks through dynamc contenton wndow adaptaton. IEEE INFOCOM, [4] Y. Yang and R. Kravets. Dstrbuted QoS guarantees for realtme traffc n ad hoc networks. 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