Priority-Based Scheduling Algorithm for Downlink Traffics in IEEE Networks

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1 Prorty-Based Schedulng Algorthm for Downlnk Traffcs n IEEE 80.6 Networks Ja-Mng Lang, Jen-Jee Chen, You-Chun Wang, Yu-Chee Tseng, and Bao-Shuh P. Ln Department of Computer Scence Natonal Chao-Tung Unversty, Hsn-Chu 3000, Tawan Department of Informaton and Computer Engneerng Chung-Yuan Chrstan Unversty, Chung-L, Tao-Yuan 303, Tawan Informaton and Communcatons Research Laboratores Industral Technology Research Insttute, Chu-Tung, Hsn-Chu 3040, Tawan Emal: {jmlang, chencz, wangyc, yctseng}@cs.nctu.edu.tw, bpln@tr.org.tw Abstract The IEEE 80.6 standard s proposed to provde a wde-range broadband wreless servce, but t leaves the mplementaton of the wreless resource scheduler as an open ssue. We thus propose a prorty-based schedulng algorthm to arrange resources for downlnk traffcs n an IEEE 80.6 broadband wreless network. The dea s to dfferentate the moble subscrber statons wth good condtons from those wth bad condtons, and to dfferentate the urgent real-tme traffcs from the non-real-tme ones. Thus, the network throughput can be mproved whle the delays of real-tme traffcs can be allevated. In addton, our desgn also keeps farness n mnd, so non-real-tme traffcs wll not be starved. Smulaton results show that our schedulng algorthm can ncrease the network throughput, mantan the long-term farness, and keep packet droppng ratos of real-tme traffcs low, as compared wth exstng results. Index Terms IEEE 80.6, far schedulng, qualty of servce (QoS), resource management, WMAX. I. INTRODUCTION Recently, the IEEE 80.6 standard [], [] has been proposed to support wde-range broadband wreless access. The standard s objectve s to use a more flexble and economcal way to solve the last mle problem n a metropoltan area network, as compared wth tradtonal wred access networks such as fber optcs or T lnks [3], [4]. IEEE 80.6 supports the pont-to-multpont (PMP) mode, where one base staton (BS) can drectly communcate wth several moble subscrber statons (MSSs). The BS wll manage network resources for these MSSs. Based on the standard, the resource unt s defned by physcal layer specfcatons. In ths paper, we use slot as the resource unt whch s defned by the mandatory physcal layer specfcaton, called orthogonal frequency dvson multplexng (OFDM) and orthogonal frequency dvson multplexng access (OFDMA). The IEEE 80.6 standard also defnes fve types of schedulng servces to support QoS (qualty of servce). They are unsolcted grant servce (UGS), real-tme pollng servce (rtps), extended rtps (ertps), non-real-tme pollng servce (nrtps), and best effort (BE). Brefly, these fve types of schedulng servces can be classfed nto real-tme servces (ncludng UGS, rtps, and ertps) and non-real-tme servces (ncludng nrtps and BE). In the IEEE 80.6 MAC layer, a scheduler s defned to manage wreless resources for these servces. However, how to mplement the scheduler leaves an open ssue n the standard. Therefore, n ths paper, we propose a schedulng algorthm to arrange resources for downlnk traffcs n an IEEE 80.6 broadband wreless network. In the lterature, several studes also consder schedulng downlnk traffcs n an IEEE 80.6 broadband wreless network. The work n [5] proposes a modfed proportonal far () method to ncrease the network throughput whle mantanng farness. In [6], a utlty functon s proposed to evaluate the tradeoff between network throughput and farness. In [7], a proportonal farness scheme based on sgnal-to-nose rato s proposed to acheve rate maxmzaton. However, the above studes consder only non-real-tme traffcs. The work n [8] assgns prortes to dfferent traffcs to satsfy ther QoS requrements, but t does not consder the farness ssue. The work n [9] models the schedulng problem as an M/M//K queung system, whose objectve s to mnmze the blockng probablty. However, t may not guarantee the delays of realtme traffcs. In ths paper, we propose a prorty-based schedulng algorthm to manage downlnk traffcs n IEEE 80.6 broadband wreless networks. Our objectves are to mprove the network throughput, to satsfy the delay constrants of real-tme traffcs, and to acheve far resource dstrbuton among MSSs. The basc dea s to assgn prortes to MSSs accordng to ther condtons and buffered traffcs. In partcular, the MSSs wth good condtons wll have a hgher prorty compared wth those wth bad condtons, so the network throughput can be ncreased snce MSSs can use a hgher rate to transmt ther data. In addton, the MSSs wth urgent real-tme traffcs wll be assgned wth a hgh prorty to allevate ther traffc delays. On the other hand, the prortes of those MSSs that have queued a large amount of non-realtme traffcs wll be rased to prevent them from starvng. In ths way, both the delays of real-tme traffcs can be allevated whle the long-term farness can be mantaned. The rest of ths paper s organzed as follows. Secton II formally defnes our resource allocaton problem. Secton III presents our schedulng algorthm. Smulaton results are gven n Secton IV. Secton V concludes ths paper.

2 TABLE I SUMMARY OF NOTATIONS. notaton n M r R, rn b R, bn a R, an c c avg s N δ F f c f T w R, w N defnton the number of admtted MSSs n the system the th MSS n ths system the request average real-tme data rate and the request mnmal non-real-tme data rate of M, respectvely the amounts of real-tme and non-real-tme buffered data of M, respectvely the amounts of resources allocated to M for real-tme and non-real-tme data, respectvely the current rate of M the average rate of M n the recent f T frames the non-real-tme rate satsfacton rato of M n the most recent f T frames the rato of MSSs for pror real-tme data allocaton the number of free slots n the current downlnk subframe the current frame ndex the wndow sze for farness measurement the weghts of real-tme and non-real-tme data, repsectvely II. PROBLEM STATEMENT We consder the downlnk communcaton n an IEEE 80.6 OFDMA system wth one BS supportng multple MSSs under the PMP mode. When an MSS needs to ntate a traffc flow, t has to ask for the BS s permsson. The BS can admt the connecton f t has enough resource to support the QoS requrement of that traffc flow; otherwse, the traffc flow wll be dropped. We are gven n MSSs, where each of them requests an average real-tme data rate of r R (n bts/frame) and a mnmum non-real-tme data rate of r N (n bts/frame), and uses a rate of c,k (n bts/slot) at frame k. The schedulng problem asks how to determne the resource a,k (n bts) allocated to each MSS M, =..n, n every frame k, such that the network throughput s maxmzed, the long-term farness among MSSs s satsfed, and the delays of real-tme traffcs are guaranteed. Here, referrng to [0], [], we defne a farness ndex (FI) to evaluate the long-term farness of a schedulng algorthm as: F I = ( n = SD ) n n = (SD ), () SD = wr f T a R,f c j f T r R a N,f c j + wn f T f T r N where f c s the current frame ndex, f T s the wndow sze (n frames) that we measure farness, a R,f c j and an,f c j (both n bts) are the resources allocated to M for real-tme and nonreal-tme traffcs at frame f c j, respectvely, and w R and w N are weghts that we put on real-tme and non-real-tme traffcs, respectvely, such that w R + w N =. In partcular, 0 < F I and a schedulng algorthm s consdered to be more far f ts FI s larger. Table I summarzes the notatons used n ths paper. III. THE PRIORITY-BASED SCHEDULING ALGORITHM Fg. llustrates the system archtecture of our proposed scheduler. Snce the scheduler wll handle each downlnk subframe, we omt the frame ndex k n the followng. When schedulng each frame, the scheduler wll frst query the MAC/physcal layers for the current rate c of each, () rate c Fg.. () rate requrement r & r scheduler (3) buffered data b & b. determne the prorty of each MSS accordng to ()-(3). run the prorty-based schedulng algorthm to allocate bandwdth ( a, a ) for each MSS R (5) decson a & a MAC/physcal layers packets The system archtecture of our proposed scheduler. MSS and the total free space F (n slots) of the current downlnk subframe. Then, gven the rate requrement (r R, rn ) and the buffered data (b R, bn ) of each MSS, the scheduler wll calculate ts prorty. Based on these prortes, the scheduler can execute our proposed prorty-based schedulng algorthm to determne the bandwdth (a R, an ) allocated to each MSS. Such a decson wll be sent to the MAC/physcal layers to arrange free slots for transmsson. Gven the current rate c, buffered real-tme data b R (n bts), real-tme data rate r R, and non-real-tme traffc satsfacton rato s N of an MSS M, we can calculate ts prorty p as follows: p = c c c avg br r R s N N (4) free space F, () where c avg s the average rate of M and s N s defned

3 as s N = mn {, ft } a N,f c j f T r N. (3) Note that M has a hgher prorty f ts p s larger. In the above Eq. (), the frst term means that we wll assgn a hgher prorty to those MSSs that can use hgher rates. The second term means that we wll assgn a hgher prorty to those MSSs that have a better condton (as compared wth ther hstorcal condtons). These two terms beneft the MSSs wth good qualtes to mprove the network throughput. The thrd term means that we wll assgn a hgher prorty to those MSSs that requre more tme to transmt ther buffered real-tme data. Ths term s to allevate the delays of real-tme traffcs. The last term means that we wll gve a hgher prorty to those MSSs that have queued a large amount of non-real-tme data. Ths term s to prevent non-real-tme traffcs from starvaton. The scheduler then allocates slot resources to MSSs accordng to ther prortes. However, to allevate the delays of realtme traffcs, we should frst allocate resources to those MSSs that have urgent data, whch are real-tme data that wll be dropped f they are not sent n the current frame. Then, we should select a δ rato of hgh-prorty MSSs to serve ther realtme data, where 0 δ <. If we stll have free resource, we can dstrbute t to MSSs accordng to ther prortes. In partcular, our proposed prorty-based schedulng algorthm nvolves n the followng steps:. We sort n MSSs by ther prortes n a descendng order. Below, we examne each MSS usng ths order.. Let d be the sze of M s urgent data, =..n. For each M wth d > 0, we allocate t wth a resource of a = mn {c (F σ ), d }, (4) where F s the number of free slots n the current downlnk subframe and σ s the summaton of allocated slots for all MSSs except M, that s, σ = aj. j,j Here, c (F σ ) means the total remanng bts that the BS can gve M n the current downlnk subframe, usng M s rate c. 3. We then select the frst δn MSSs to serve ther realtme data. For each such MSS M, we allocate t wth a resource of a = mn { c (F σ ), b R }. (5) 4. For each M, =..n, we allocate t wth a resource of a = mn { c (F σ ), b R + b N }. (6) 5. For each M, =..n, we set a R = mn{b R, a } and a N = a a R. Note that n step 3, we serve the real-tme traffcs of the frst δn MSSs f there stll remans free space. Ths s to avod cumulatng too much urgent real-tme data n the followng frames. If there are free slots after step 3, we can dstrbute c j pt pt condton x- condton x condton x+ Fg.. The three-state Markov chan to model the change of condtons of MSSs. them among MSSs, as shown n step 4. Fnally, n step 5, we transform the result of assgnment a nto (a R, an ) to serve the real-tme and non-real-tme traffcs of each MSS M. Snce d b R b R + b N, we have Eq. (4) Eq. (5) Eq. (6). So, M can be allocated wth enough slots by Eq. (5) to support ts urgent data. Smlarly, M can be allocated wth enough slots by Eq. (6) to support all ts real-tme data. IV. SIMULATION RESULTS In ths secton, we present some smulaton results to verfy the effectveness of our algorthm. We develop a smulator by C++ language. Table II lsts the system parameters used n our smulator, whch follows those defned n the IEEE 80.6 standard. For each MSS, ts admtted real-tme data rate r R and nonreal-tme data rate r N are randomly selected from [0, 400] bts per frame. The condton of an MSS wll change durng the smulatons. We use a three-state Markove chan [] to model the change of condtons, as shown n Fg.. In partcular, let MCS = {QPSK/, QPSK3/4, 6QAM/, 6QAM3/4, 64QAM/, 64QAM3/4} be the lst of modulaton and codng schemes and M CS[x] denote the scheme wth ndex x. Suppose that an MSS uses the scheme of MCS[x] under ts condton at the current frame. There s a probablty of that t has to use the scheme of M CS[x ] when the condton becomes worse at the next frame. Also, there s a probablty of that t can use the scheme of M CS[x + ] when the condton becomes better at the next frame. In addton, there s a probablty of that the condton of the MSS remans the same at the next frame. We set the transton probablty = 0.5 and the x value of each MSS s randomly selected from to 5. The (.e., n) s ranged from 0 to 70. Due to the lmted resource, the system can totally serve at most 70 MSSs. We compare our proposed algorthm aganst the Max-Throughput () scheme and the scheme [5]. The scheme always selects the MSS wth the best condton c to serve. The scheme assgns prortes to MSSs accordng to ther c values and data rates. In our smulatons, the values of weghts w R and w N are set to 0.9 and 0., respectvely.

4 TABLE II SYSTEM PARAMETERS USED IN OUR SIMULATOR. parameter value FFT (fast Fourer transform) sze 04 zone category PUSC (partal usage of sub) wth reuse modulaton and codng scheme QPSK/, QPSK3/4, 6QAM/, 6QAM3/4, 64QAM/, and 64QAM3/4 frame duraton 5 ms types of real-tme traffcs UGS and rtps types of non-real-tme traffcs nrtps and BE netowrk throughput (Mbps) Fg. 3. Comparson on network throughput of dfferent schedulng schemes. farness ndex Fg Comparson on farness ndces of dfferent schedulng schemes. A. Network Throughput We frst compare the network throughput of these three schedulng schemes. Fg. 3 llustrates the effect of dfferent numbers of MSSs on the network throughput. We can observe that when the s smaller than 40, the network throughput of all schedulng schemes are smlar. Ths s because qute few MSSs ask for transmsson, so the effect of dfferent schedulng schemes s not sgnfcant. However, when the grows, our schedulng algorthm can mprove the network throughput as compared wth the and schemes. Ths s because our scheduler wll assgn a hgher prorty to two knds of MSSs: ) the MSSs that have a better condton (.e., a larger c ) and ) the MSSs whose current condton s better than ther hstorcal condtons (.e., c > c avg ). In ths way, the MSSs can use a hgher data rate to transmt ther packets, so the network throughput can be ncreased. B. Farness Index Although our schedulng algorthm benefts those MSSs that can transmt data usng hgh data rates, t stll can mantan long-term farness among all MSSs. Ths s verfed n Fg. 4, whch demonstrates the effect of dfferent numbers of MSSs on the farness ndex (.e., Eq. ()). We can observe that the farness ndex of our schedulng algorthm s stll close to, even though there are 70 MSSs n the system. Ths s because our scheduler wll assgn a hgher prorty to those MSSs that have queued a large amount of data. On the other hand, the farness ndces of both the and schemes drop sgnfcantly when the grows, because they only allow the MSSs wth better condtons to transmt ther data frst. C. Packet Droppng Ratos of Real-Tme Traffcs We then compare the packet droppng ratos of real-tme traffcs under dfferent schedulng schemes. Fg. 5 llustrates the effect of dfferent numbers of MSSs on the packet droppng ratos of real-tme traffcs. When the s more than or equal to 50, the network starts saturated. We can observe that the packet droppng ratos of both the and schemes ncrease when the ncreases. Ths s because they do not dfferentate real-tme traffcs from non-real-tme ones, causng a large amount of non-realtme traffcs to contend wth urgent real-tme traffcs. On the other hand, by makng MSSs transmt ther urgent real-tme traffcs frst, our schedulng algorthm can result n zero packet droppng rato, even though there are 70 MSSs n the system. Ths verfes the effectveness of our algorthm. D. Satsfacton Ratos of Non-Real-Tme Traffcs Although our schedulng algorthm benefts real-tme traffcs, t does not starve non-real-tme traffcs. Ths s verfed n Fg. 6, whch demonstrates the effect of dfferent numbers of MSSs on the satsfacton rato of non-real-tme traffcs (.e., Eq. (3)). We can observe that the satsfacton rato of our schedulng algorthm s stll close to, even though there are 70 MSSs n the system. Ths s because our scheduler wll assgn a hgher prorty to those MSSs that have queued a large amount of non-real-tme data. On the other hand, the

5 packet droppng rato Fg. 5. Comparson on packet droppng ratos of real-tme traffcs under dfferent schedulng schemes. satsfacton rato Fg. 6. Comparson on satsfacton rato of non-real-tme traffcs under dfferent schedulng schemes. satsfacton rato of both the and schemes drop sgnfcantly when the grows, especally when the s more than 50. Ths s because they do not consder the queue lengths of MSSs, makng nonreal-tme traffcs starvaton. ACKNOWLEDGEMENT Y.-C. Tseng s research s co-sponsored by MoE ATU Plan, by NSC grants 96-8-E , E , 97- -E MY3, and 98-9-E , by MOEA under grant 98-EC-7-A-0-S-0048, and by ITRI, Tawan. REFERENCES [] IEEE Std , IEEE Standard for Local and Metropoltan Area Networks Part 6: Ar Interface for Fxed Broadband Wreless Access Systems, 004. [] IEEE Std 80.6e-005, IEEE Standard for Local and Metropoltan Area Networks Part 6: Ar Interface for Fxed and Moble Broadband Wreless Access Systems Amendment : Physcal and Medum Access Control Layers for Combned Fxed and Moble Operaton n Lcensed Bands and Corrgendum, 006. [3] A. Ghosh, D. Wolter, J. G. Andrews, and R. Chen, Broadband wreless access wth WMax/80.6: current performance benchmarks and future potental, IEEE Communcatons Magazne, vol. 43, no., pp. 9 36, 005. [4] C. Eklund, R. B. Marks, K. L. Stanwood, and S. Wang, IEEE Standard 80.6: a techncal overvew of the WrelessMAN TM ar nterface for broadband wreless access, IEEE Communcatons Magazne, vol. 40, no. 6, pp , 00. [5] J. Km, E. Km, and K. S. Km, A new effcent BS scheduler and schedulng algorthm n WBro systems, n IEEE Internatonal Conference on Advanced Communcaton Technology, vol. 3, 006, pp [6] J. Sh and A. Hu, Maxmum utlty-based resource allocaton algorthm n the IEEE 80.6 OFDMA System, n IEEE Internatonal Conference on Communcatons, 008, pp [7] Y. Ma, Rate-maxmzaton schedulng for downlnk OFDMA wth long term rate proportonal farness, n IEEE Internatonal Conference on Communcatons, 008, pp [8] X. Zhu, J. Huo, S. Zhao, Z. Zeng, and W. Dng, An adaptve resource allocaton scheme n OFDMA based multservce WMAX systems, n IEEE Internatonal Conference on Advanced Communcaton Technology, 008, pp [9] N. A. Al, M. Hayajneh, and H. Hassanen, Cross layer schedulng algorthm for IEEE 80.6 broadband wreless networks, n IEEE Internatonal Conference on Communcatons, 008, pp [0] D. M. Chu and R. Jan, Analyss of the ncrease and decrease algorthms for congeston avodance n computer networks, Journal of Computer Networks and ISDN, vol. 7, no., pp. 4, 989. [] Y. C. Wang, Y. C. Tseng, and W. Chen, MR-FQ: a far schedulng algorthm for wreless networks wth varable transmsson rates, SCS Smulaton, vol. 8, no. 8, pp , 005. [] P. Y. Wu, J. J. Chen, Y. C. Tseng, and H. W. Lee, Desgn of QoS and admsson control for VoIP Servces over IEEE 80.e WLANs, Journal of Informaton Scence and Engneerng, vol. 4, no. 4, pp , 008. V. CONCLUSIONS In ths paper, we have proposed a prorty-based schedulng algorthm for the downlnk communcaton n an IEEE 80.6 broadband wreless network. Our schedulng algorthm can allocate resources to MSSs based on ther condtons and buffered data. The MSSs wth good condtons and urgent real-tme data wll be served frst. In ths way, we can not only ncrease the network throughput but also allevate the delays of real-tme traffcs. Our schedulng algorthm also addresses the farness ssue, so non-real-tme traffcs are not starved. Smulaton results have shown that our schedulng algorthm can mprove the network throughput, mantan the long-term farness, allevate the packet droppng ratos of realtme traffcs, and ncrease the satsfacton ratos of non-realtme traffcs.