QoS-aware composite scheduling using fuzzy proactive and reactive controllers

Transcription

1 Khan et al. EURASIP Journal on Wreless Communcatons and Networkng 2014, 2014:138 RESEARCH Open Access QoS-aware composte schedulng usng fuzzy proactve and reactve controllers Nabeel Khan 1*, Mara G Martn 1 and Drk Staehle 2 Abstract We consder n ths paper downlnk schedulng for dfferent traffc classes at the MAC layer of wreless systems based on orthogonal frequency dvson multple access (OFDMA), such as the recent 3rd Generaton Partnershp Project (3GPP) long-term evoluton (LTE)/LTE-A wreless standard. Our goal s to provde va the schedulng decsons qualty of servce (QoS), but also to guarantee farness among the dfferent users and traffc classes (ncludng delay-senstve and best-effort traffc). QoS-aware schedulng strateges, such as modfed largest weghted delay frst (M-LWDF), exponental (EXP), exponental proportonal far (EXP-PF), and the log-based schedulng rules, prortze delay-senstve traffc by consderng rules based on the head-of-lne (HoL) packet delay and the tolerated packet loss rate, whereas they serve best-effort traffc by consderng the classcal proportonal far (PF) rule. These schedulng rules do not prevent resource starvaton for best-effort traffc. On the other sde, f both traffc types are scheduled accordng to the PF rule, then delay-senstve flows suffer from delay bound volatons. In order to farly dstrbute the resources among dfferent servce classes accordng to ther QoS requrements and channel condtons, we employ the concept of fuzzy logc n our schedulng framework. By employng the fuzzy logc concept, we serve all the traffc classes wth one prorty rule. Smulaton results show better ntra-class and nter-class farness than state-of-the-art schedulng rules. The proposed schedulng framework enables to approprately balance delay requrements of traffc, system throughput, and farness. 1 Introducton 3rd Generaton Partnershp Project (3GPP) Release 8, and ts subsequent modfcatons, defne the long-term evoluton (LTE) standard [1] that wll take the cellular technology n the 2020s. In wreless communcaton systems, rado resources are shared by multple users; hence, medum access control (MAC) layer schedulng becomes extremely mportant n determnng the overall performance of an LTE system. The effcency of the lnk level, from the LTE base staton (enodeb) to the moble termnal, largely depends on the desgn of the scheduler, whose task s to determne whch users should be served and to assgn resources. An effcent scheduler must ensure a good trade-off between effcency and farness n the system (accordng to the servce needs of each user) by fully utlzng the avalable rado resources. MAC layer schedulng strateges *Correspondence: n.khan@kngston.ac.uk 1 Faculty of Scence, Engneerng and Computng, Kngston Unversty, Penrhyn Rd, Kngston upon Thames, Surrey KT1 2EE,UK Full lst of author nformaton s avalable at the end of the artcle can be classfed as channel-aware and channel-unaware, where channel aware schedulng algorthms take channel condtons nto account and maxmze the system throughput. Note, however, that the man target of moble operators would be the end-user satsfacton, not merely the maxmzaton of system throughput. Schedulng n the LTE standard s more challengng than n earler standards manly because earler standards were based on sngle carrer systems, where resources were usually dvded n terms of tme slots or codes among the users, whereas LTE s a multcarrer system where system resources are shared among users n terms of tme and frequency sub-bands. Some approaches solve the problem of resource allocaton usng optmal solutons, and n other cases, resource allocaton and resource assgnments are performed n two separate steps; other approaches smply target at adaptng schemes orgnally proposed for tme dvson multple access (TDMA) to orthogonal frequency dvson multple access (OFDMA) systems. Thus, schedulng solutons avalable n the lterature broadly fall nto three classes Khan et al.; lcensee Sprnger. Ths s an Open Access artcle dstrbuted under the terms of the Creatve Commons Attrbuton Lcense ( whch permts unrestrcted use, dstrbuton, and reproducton n any medum, provded the orgnal work s properly credted.

2 Khan et al. EURASIP Journal on Wreless Communcatons and Networkng 2014, 2014:138 Page 2 of In [2-5], resource allocaton s modeled as a convex optmzaton problem. The water-fllng algorthm s used to solve the convex optmzaton problem by consderng a contnuous objectve functon. Lnear nteger programmng s also wdely used n solvng the resource allocaton problem by frst transformng the nonlnear optmzaton problem nto a lnear problem. The man drawback of these strateges s the hgh computaton complexty. Snce the transmsson tme nterval (TTI) n LTE s only 1 ms, these algorthms are not feasble from an mplementaton pont of vew. 2. In the second class of approaches, such as n [6-8], schedulng s performed n two steps. The frst step conssts of resource allocaton, whch determnes the number of resources allocated to each user. The resource allocaton step s followed by the resource assgnment step, whch determnes whch resources are assgned to each user. Ths class of schedulng algorthms are specfcally desgned for delay-senstve applcatons and does not provde a prorty dfferentaton between delay-senstve and best-effort flows. 3. The thrd approach s the adaptaton of TDMA strateges for OFDMA systems. Schedulng rules desgned for sngle carrer systems such as the proportonal far (PF) [9], modfed largest weghted delay frst (M-LWDF) [10], and exponental proportonal far (EXP-PF) [11] are adapted for an OFDMA system by calculatng these rules on each resource. Ths adaptaton s referred to as an OFDMA/TDMA strategy. These schedulng rules are analyzed by [12] for delay-senstve applcatons over an LTE system. Accordng to [12], M-LWDF s the best schedulng rule for delay-senstve applcatons n terms of farness and effcency. A very good survey on these schedulng strateges for LTE s provded n [13]. As each of these schedulng rules are based on the proportonal far rule, the calculaton of these schedulng metrcs on each physcal resource block (PRB) allows the explotaton of mult-user tme and frequency dverstes. The complexty of the OFDMA/TDMA approach grows lnearly wth the number of users and resources. Thus, t can be mplemented n real tme. However, for delay-senstve traffc, these schedulng rules cannot provde farness for users wth relatvely low sgnal-to-nterference nose rato (SINR) [14]. In ths work, we address the followng ssues of the thrd class of strateges: Intra-class farness ssues for delay-senstve traffc: schedulng rules for delay-senstve traffc consder the rato of nstantaneous channel qualty and tme-averaged throughput (proportonal far rule) along wth ether the lnear [10], logarthmc [15], or exponental [11,15] functon of the head-of-lne (HoL) delay [16]. The HoL delay refers to the amount of tme packets that resde n the buffer and s also known as the sojourn tme. It s mportant to note that the vdeo s delay-senstve traffc; hence, packets arrvng late are generally not useful at the recever. Therefore, packets are assocated wth a predefned HoL delay bound and packets volatng the delay bound are dropped from the queue. The utlzaton of HoL delay and the proportonal far rule n the schedulng decsons are not suffcent to avod delay bound volaton of flows havng lower channel qualty. Vdeo traffc exhbts hghly varable bt rate characterstcs,.e., the nstantaneous peak rate s hgher than the average rate. Lower channel qualty vdeo flows exhbtng peak nstantaneous rate have hgh probablty of delay bound volaton manly because of the proportonal far rule n the schedulng decsons. In other words, these schedulng rules acheve hgher HoL delay for the packets of flows havng hgher average rate and lower channel qualty. On the other hand, flows havng good channel qualty and lower average rate are scheduled way before ther delay bound. The probablty of delay bound volaton of the flows exhbtng lower channel qualty and hgher average rate s very hgh whch results n an unfar system. Inter-class farness ssues: n the lterature [13], composte schedulng rules serve the best-effort traffc by usng the classcal proportonal far rule,.e., rato of nstantaneous channel qualty to the tme-averaged throughput [9,17-19]. They prortze delay-senstve traffc by consderng ether the logarthmc, exponental, or lnear functon of the HoL delay. However, such composte schedulng strateges result n a hgher prorty dfference between the delay-senstve and best-effort traffc classes. In other words, the hgher the dfference among the schedulng prortes of traffc classes, the lower wll be the mult-user channel dversty explotaton. In LTE, mult-user channel dversty has more sgnfcance snce t s a mult-carrer system whch allows mult-user dversty explotaton n the tme and frequency doman. By usng the concept of fuzzy logc prorty [20], we couple the flow s delay urgency (rato of packet s HoL delay and delay bound) wth the tme-averaged channel qualty. Instead of explotng the tme-averaged throughput and the lnear, logarthmc, or exponental functon of the HoL delay, we use a fuzzy functon of the HoL

3 Khan et al. EURASIP Journal on Wreless Communcatons and Networkng 2014, 2014:138 Page 3 of 21 delay coupled wth tme-averaged channel qualty as ntroduced n [21]. In [21], the HoL delay along wth the tme-averaged channel qualty s processed by a fuzzy proactve controller. Further, whenever a flow suffers a delay bound volaton, the scheduler reacts to ths event and ncreases the prorty of that flow. The delay bound volaton nput s processed by a fuzzy reactve controller. In ths work, we propose a composte schedulng rule for delay-senstve as well as the best-effort traffc. In the earler work, the schedulng rule consders only the vdeo traffc. In ths work, the schedulng rule and scenaros are extended to handle more than one delay-senstve traffc types. Furthermore, the man goal of the proposed composte schedulng rule s to balance the probabltes of qualty of servce (QoS) volaton of the delay-senstve as well as the best-effort traffc types. A block dagram representng the proposed fuzzy composte schedulng (FCS) s gven n Fgure 1. The schedulng metrc comprses a tme-doman prorty component based on reactve and proactve controllers and a frequency doman prorty based on detaled nformaton on nstantaneous channel qualty ndcator (CQI) feedback per PRB. In order to dynamcally adjust the prorty level between best-effort and delay-senstve flows, we utlze a fuzzy-based dynamc resource controller (DRC) (dscussed n Secton 3.3), as shown n Fgure 1. Intraclass farness (farness n terms of acheved QoS among the flows wthn each of the traffc classes) s provded by the fuzzy proactve and reactve controllers whereas nter-class farness (prorty dfferentaton between the delay-senstve and best-effort flows) s provded by the DRC. In fuzzy logc, the output fuzzy set s defned as the range of all possble output values that can be assgned to a fuzzy controller. The output of the controller les wthn the output fuzzy set. The larger the output fuzzy set, the hgher the prorty of the controller. In the proposed schedulng framework, each traffc class has ts own output fuzzy set. We assgn a fxed output fuzzy set to the delay-senstve traffc class and dynamcally adjust the output fuzzy set of the best-effort traffc. The output fuzzy set of the best-effort traffc class s set by the DRC based on the latency (packet s HoL delay) and QoS volaton of the delay-senstve flows as shown n the fgure. The hgher the latency and QoS volatons of the delay-senstve flows, the lower the output fuzzy set of the Fgure 1 Tme and frequency doman models of the FCS schedulng framework.

4 Khan et al. EURASIP Journal on Wreless Communcatons and Networkng 2014, 2014:138 Page 4 of 21 best-effort traffc. The fnal prorty on each PRB s a functon of the tme and frequency doman prorty metrcs as shown n Fgure 1. The remander of ths paper s organzed as follows. Secton 2 presents the consdered system model and the problem statement. Secton 3 presents the detals of our fuzzy logc-based schedulng strategy. Secton 4 presents the performance evaluaton of the proposed approach. In partcular, the solutons consdered as benchmark for the assessment of our schedulng algorthm are presented n Secton 4.1, whereas the smulaton setup s presented n Secton 4.2; results are presented and dscussed n Secton 4.3. Conclusons are drawn n Secton 5. 2 System model and problem statement We consder a multuser downlnk sngle nput sngle output (SISO) LTE/LTE-A system. The sngle-cell scenaro comprses an enodeb MAC scheduler responsble for allocatng PRBs to dfferent users n the cell. Each user s assgned a buffer at the enodeb, and packets of dfferent traffc classes are streamed nto the buffer of the enodeb. For delay-senstve traffc, we consder vdeo and VoIP traffc (the schedulng framework can accommodate all LTE servce classes), whereas for besteffort traffc, we consder constant bt rate (CBR) traffc. The packets of each traffc class enterng the buffer are tme stamped by the scheduler. Packets of delaysenstve traffc are dropped from the buffer f the HoL packet delay s longer than the target HoL delay bound. ThemanQoSparametersforvdeoandVoIPflowsare the HoL packet delay and the packet loss rate (PLR), whereas throughput s the mportant QoS parameter for the flows of best-effort traffc. We consder the HoL delay for best-effort traffc, and we assgn a target delay for the flows of ths traffc class. However, snce we can assume best-effort traffc s delay tolerant, therefore, packets volatng the target HoL delay are not dropped from the buffer. We use a CQI feedback mechansm, where each user feedbacks nformaton about the channel qualty on each PRB. Due to the adopton of adaptve modulaton and codng (AMC) n LTE, each CQI value corresponds to a specfc value of spectral effcency for each PRB. At schedulng epoch n, we defne the normalzed tmeaveraged wdeband spectral effcency, χ (n),ofuser over the movng average wndow of sze n c as: χ (n) = 1 1 χ max n c n χ (k) k=n n c (1) where χ (n) = 1 M PRB χ (n),ϕ (2) M PRB ϕ=1 s the average PRB spectral effcency of user at schedulng nstant n and χ (n),ϕ s the nstantaneous subband spectral effcency of user at PRB ϕ. χ max s a constant,.e., the spectral effcency ( bts/s/hz) correspondng to the maxmum CQI feedback, and M PRB s the number of PRBs avalable for allocaton at each schedulng epoch. Gven the avalable nformaton about: the HoL packet delay for each flow H (n), the channel qualty of each flow on each PRB, hence the resultng spectral effcency χ (n),ϕ, the tolerated delay bound H max, the QoS performance of the delay-senstve flows n terms of packet loss rato, plr (n) and of the best-effort flows n terms of tme-averaged throughput R (n),ave, the schedulng problem s defned as: How to allocate to the dfferent users the M PRB PRBs n each schedulng nterval n order to fulfll the QoS requrements of each of the flows from dfferent traffc classes so that a good trade-off between farness and effcency s acheved. In order to mathematcally formulate the problem, let us defne the followng parameters: : Throughput acheved by flow at schedulng nstant n. I: Total number of flows n the system. It s the sum of delay-senstve I delay senstve and best-effort I best effort flows. R (n) : The packet loss rato of flow at schedulng nstant n calculated over the movng average transmsson wndow t w : plr (n) nm=n tw plr (n) P (m) drop = ) (3) nm=n tw (P (m) transmt + P (m) drop where P (m) transmt :Numberoftransmttedpacketsofflow over the movng average transmsson wndow t w. P (m) drop : Number of dropped packets of flow over the movng average transmsson wndow t w. The man goal of the scheduler s to maxmze the system throughput R (n) t w, subject to the QoS constrants of the

5 Khan et al. EURASIP Journal on Wreless Communcatons and Networkng 2014, 2014:138 Page 5 of 21 delay-senstve flows, over the movng average transmsson wndow t w : R (n) t w subject to ( I = max 1 I delay senstve =1 n R (m) m=n t w I delay senstve =1 ) (4) ( ) 1I plr (n) plr thr = 1 (5) where ( ) 1I plr (n) plr thr s an ndcator functon equal to 1 f ts argument s true,.e., when the packet loss rate of flow s lower or equal than the threshold value plr thr. If the packet loss rate exceeds the threshold, then the ndcator functon s 0. It s mportant to note that farness for delay-senstve traffc s guaranteed when the PLR over a short movng average wndow [22], for nstance one second, s below the prescrbed threshold for each of the delay-senstve flows n the system. As noted n [23], when the scheduler acheves short-term farness, then the long-term farness s guaranteed. The optmal soluton of the above problem s not possble wthout restrctve assumptons on the arrval process of the traffc and changes n channel qualty. Therefore, we propose a novel schedulng framework relyng on fuzzy logc. Fuzzy logc s deally suted for problems where a defnte mathematcal soluton s unavalable. The nformaton about the changes n the rado channel and the traffc rate of each user s uncertan. Fuzzy logc can deal wth such stuatons because of ts capablty to make approxmate reasonng. In our proposed schedulng strategy, each PRB s assgned to the user maxmzng a defned metrc. Our proposed metrc s composed of a PRB-ndependent part and a PRB-specfc part. The PRB-ndependent part calculated for a user descrbes the urgency of an assgnment as tme-doman prorty, whereas the PRB-specfc part descrbes the nstantaneous channel qualty of the PRB and ts relatve qualty versus other PRBs. 3 Fuzzy composte schedulng framework The FCS framework conssts of fuzzy proactve, reactve, and DRC controllers. It s mportant to note that the desgns of the proactve and reactve controllers are the same. The proactve controller processes the HoL delay whereas the reactve controller processes the QoS volaton. In the followng, we present a detaled desgn of the three fuzzy controllers: 3.1 Proactve controller The goal of the proactve controller s to avod delay bound volatons. In order to consder the delay urgency n a dynamc wreless envronment, we propose a novel concept of utlzng tme-averaged channel qualty over a small movng wndow by usng the average wdeband spectral effcency χ (n) assocated to the CQI feedback, defned n Equaton 1. The proactve controller processes twonputs.oneofthesestheholpacketdelayh (n) normalzed to the maxmum tolerated HoL delay H max of each traffc class. The normalzed HoL delay nput, H,for the proactve controller s: H = H (n),voip H,max, H (n),vdeo H,max, H (n),best effort H,max for VoIP traffc class for Vdeo traffc class,forbest effort traffc class The goal of the controller s to be proactve for any possble delay volatons; hence, the second nput s desgned as the weghted sum of the normalzed delay and the normalzed average channel qualty. It s mathematcally defned as: χ H = 0.5(1 H (n) ) + 0.5(χ ) (7) The ratonale behnd the weghted sum Equaton 7 s dscussed n Secton In fuzzy logc, the nput membershp functon represents the magntude of the nputs whch are mapped to the output membershp functon through a set of rules [20]. The membershp functons can be lnear, exponental, bell shaped, or any other shape accordng to the system requrements. Accordng to [12], the M-LWDF schedulng rule, lnear functon of the HoL packet delay, outperforms the EXP-PF schedulng rule whch s an exponental functon of HoL packet delay. Therefore, we select lnear membershp functons for the proactve and reactve controllers. The graphcal representaton of the nput and output membershp functons s shown n Fgure 2a and 2b, respectvely. The same nput membershp functons are used for both the nputs (H and χ H ). It s mportant to note that users wth better channel qualty result n a hgher frequency doman prorty on each PRB ϕ, as there wll be a hgher number of PRBs wth better channel qualty. Therefore, the tme doman prorty should be hgher for users wth hgher normalzed HoL packet delay and lower normalzed channel qualty. Now, we wll utlze the flexblty of fuzzy logc by mappng the nput membershp functons to the output membershps functons through a set of rules. Let μ p be the output of the proactve controller (defuzzfed proactve prorty value), the fuzzy rules for the proactve controller are as follows: (6)

6 Khan et al. EURASIP Journal on Wreless Communcatons and Networkng 2014, 2014:138 Page 6 of 21 a b Fgure 2 Input and output membershp functons. (a) Input membershp functons low and hgh of the proactve and reactve controllers. (b) Output membershp functons low, medum, and hgh of the proactve and reactve controllers. 1. If H s low AND χ H s low THEN μ p s medum 2. If H s low AND χ H s hgh THEN μ p s low 3. If H s hgh AND χ H s low THEN μ p s hgh 4. If H s hgh AND χ H s hgh THEN μ p s medum where low, medum, andhgh are the output membershp functons as shown n Fgure 2 and μ p s the crsp output whch along wth the reactve controller output quantfes the tme doman prorty of each user. The man motvaton of usng the low, medum, and hgh output membershp functons s to prortze flows sufferng from lower channel qualty and hgher HoL delay. The prorty of the users wth relatvely good channel qualty ncreases from low to medum as the HoL delay ncreases. On the other hand, the prorty of users wth lower channel qualty ncreases from medum to hgh. Therefore, farness s ncorporated n the schedulng decsons through the output membershp functons and rules of the fuzzy controllers. The man goal of the frequency doman prorty s to mprove the system effcency whereas the tme doman prorty provdes farness through fuzzy proactve and reactve controllers. The output fuzzy set of the membershp functons, shown n Fgure 2, determnes the traffc prorty of each traffc class. It s mportant to note that μ p les wthn the output fuzzy set. The proactve prorty, μ p, as a functon of the nputs H and χ H s shown n Fgure 3. The steps nvolved n producng a crsp output n the fuzzy logc system are descrbed below. 1. Fuzzfcaton. Ths s the process of convertng fuzzy nput values nto a degree of membershp for each lngustc term. Each lngustc term, hgh, medum, and low, characterzes a membershp functon. For nstance, the proactve controller nputs, H and χ H, as shown n Fgure 4, are fuzzfed by the nput membershp functons low and hgh. In the fgure, the four rows are the four rules of the proactve controller. Rule one comprses only low membershp functon, therefore nput H and χ H are fuzzfed by the low membershp functon as shown n the fgure. 2. Fuzzy nference. Ths s the core process of the fuzzy logc system, comprsng a mappng from a gven nput to an output usng the membershp functons and logcal operators n the f-then-else rules. Accordng to Fgure 4, the AND logcal operaton s performed, accordng to whch the mnmum of the two fuzzfed nputs s mapped to the output membershp functon. The result of the fuzzy nference process s the degree of the output membershp functons fulflled by the logcal operatons between the fuzzfed nputs. The result s the truncated output membershp functons as shown n the thrd column of Fgure 4.

7 Khan et al. EURASIP Journal on Wreless Communcatons and Networkng 2014, 2014:138 Page 7 of 21 Fgure 3 Proactve controller output, μ p, w.r.t the nputs. 3. Defuzzfcaton and producton of the fnal crsp output. The crsp proactve prorty output μ p produced s shown n Fgure 4. The output of each rule s combned to gve the fnal fuzzy set, as shown n the ffth row and thrd column n Fgure 4. The defuzzfcaton process s smply the centrod calculaton on the fnal fuzzy set as shown n Fgure Ratonale The nputs H and χ H are a functon of the HoL delay; hence, the system s made more proactve for any possble delay volatons. The second nput, weghted sum of the normalzed HoL delay and tme-averaged channel qualty, enhances the system farness. For nstance, consder two users - user 1 and 2 - havng normalzed average channel qualty of 0.8 and 0.6, respectvely, and normalzed HoL packet delay of 0.4 and 0.8, respectvely. If we select the second nput as a smple functon of the average channel qualty,.e., χ H = χ (n), then the output of the proactve controller, μ p (Fgure 3), for user 1 and 2 s (H = 0.4, χ H = 0.8) and 1.05 (H = 0.8, χ H = 0.6), respectvely. The dfference n the proactve prorty of the two users s = On the other hand, f the weghted sum Eq. 7 s used, then the output for user 1 and 2 s (H = 0.4, χ H = 0.6) and 1.14 (H = 0.8, χ H = 0.7), wth a dfference n proactve prorty of A hgher prorty wth weghted sum equaton quantfes the urgency n the servce needs of user 2 havng relatvely hgher packet delay and lower channel qualty. Therefore, the system s more senstve to the HoL delay. If the nstantaneous channel qualty of the user mproves, the system explots t. For nstance, consder Fgure 5 where the channel qualty ncreases at the current schedulng nstant; the result s a hgher tme doman prorty, quantfyng lower tmeaveraged channel qualty over a wndow of sze n c epochs and hgher HoL packet delay. Because of the ncrease n the channel qualty at the current schedulng nstant, the frequency doman prorty (functon of current nstantaneous channel qualty) also ncreases wth PRBs havng

8 Khan et al. EURASIP Journal on Wreless Communcatons and Networkng 2014, 2014:138 Page 8 of 21 Fgure 4 Fuzzy rules of the proactve controller. better channel qualty. Therefore, the weghted sum of the normalzed HoL delay and the tme-averaged channel qualty wth weghts equal to 0.5 makes the system opportunstc (explotng nstantaneous channel mprovements) and delay aware. 3.2 Reactve controller Delay-senstve applcatons can tolerate packet losses f they are below a gven threshold. To provde farness n multmeda traffc, packet losses should be kept below a gven threshold for all users. The goal of the reactve controller s to dstrbute the packet losses proportonally equal across all the users. In order to defne the nputs of the reactve controller, we utlze the packet loss rato, plr (n),ofuser gvennequaton3.thepacket loss rato can easly be calculated by usng the number of dropped and transmtted packets over a small transmsson wndow. The desgn of the reactve controller s Fgure 5 Ratonale of the proactve controller desgn.

9 Khan et al. EURASIP Journal on Wreless Communcatons and Networkng 2014, 2014:138 Page 9 of 21 smlar to the proactve controller except that the fuzzy nputs are based on the packet loss rate over a movng average transmsson wndow. We consder a wndowszeof1snthesmulatonstudy.theamountof QoS volaton n terms of packet loss rato and tolerated packet loss threshold, plr thr,ofuser at schedulng nstant n s: V (n),delay senstve = plr(n) plr,thr. (8) The QoS parameter for the delay-senstve traffc s the packet loss rato, whereas for the best-effort flows, the QoS performance parameter s the rato of mnmum rate requred to the acheved tme-averaged throughput. V (n),best effort = R mn R (n),ave where R (n),ave s the tme-averaged throughput and R mn s the mnmum rate requrement. The QoS volaton nput, V, for the reactve controller s: V = V (n),voip V (n) j,max V (n),vdeo V (n) j,max V (n),best effort V (n) j,max, for VoIP traffc class, for Vdeo traffc class,forbest effort traffc class (9) (10) It s a requrement of the fuzzy logc system that the nputs of the fuzzy controller should le wthn the nput fuzzy set,.e., n between 0 and 1. Therefore, we normalze the nput wth respect to the flow havng the maxmum QoS volaton, V (n) j,max. The second nput, χ V, of the reactve controller s desgned as the weghted sum of the normalzed QoS volatons and the normalzed average channel qualty. Mathematcally, t s defned as: χ V = 0.5(1 V (n) ) + 0.5(χ ). (11) Ratonale The ratonale behnd the desgn of the reactve controller sthesameasthatoftheproactvecontrollerdscussed n Secton The weghted sum of the normalzed QoS volatons and the tme-averaged channel qualty wth weghts equal to 0.5 makes the system opportunstc (explotng nstantaneous channel mprovements) and QoS aware as dscussed n Secton The nput and output membershp functons and the output fuzzy set s the same as that of the proactve controller. It s mportant to note that we could have used all the nputs,.e., the HoL packet delay, the QoS volatons, and the tme-averaged channel qualty, and desgn a fuzzy prorty scheme by defnng a set of rules for these three nputs. However, ths ncreases the complexty of the system because, wth three nputs, eght rules and more than three output membershp functons are requred. A fuzzy logc system wth two nputs s smpler n terms of mplementaton and processng. Therefore, by usng the same rules and membershp functons, the same fuzzy module s called for proactve (H and χ H ) and reactve (V and χ V ) nputs. 3.3 Dynamc resource controller The best-effort traffc class s consdered as the lowest prorty class. Schedulng rules desgned for delaysenstve traffc, such as n [24,25] (see the tme utlty functons of dfferent traffc classes), gve low schedulng prorty to the best-effort flows. Hgh prorty dfferentaton between the delay-senstve and best-effort flows causes resource starvaton for the best-effort flows [26,27]. In FCS schedulng framework, nter-class traffc prorty dfferentaton s provded by output fuzzy set. The output fuzzy set represents the range of all possble output values that can be assgned to the proactve and reactve controllers. The larger the output fuzzy set, the hgher the prorty of the controller. In order to dynamcally prortze flows belongng to best-effort traffc class, we adapt the output fuzzy set of the besteffort flows accordng to the QoS performance of the delay-senstve flows. The output fuzzy set of the delaysenstve traffc class s fxed; thus, the amount of resource allocatons between the delay-senstve and best-effort flows s adaptable and controlled by the maxmum lmt of the output fuzzy set, μ max, as gven n Equaton 12. μ rbest effort = μ pbest effort {0, μ max } (12) where μ rbest effort and μ pbest effort are the defuzzfed outputs of the reactve and proactve controllers, respectvely. As dscussed n Sectons 3.1 and 3.2, the desgn of both the controllers and the correspondng output fuzzy sets are the same. Flows from each traffc class utlze the same tme doman prorty by usng the reactve and proactve controllers. The average delay and packet loss rate performance of the delay-senstve flows are used to determne the maxmum lmt of the output fuzzy set for the best-effort traffc. Mathematcally, the average

10 Khan et al. EURASIP Journal on Wreless Communcatons and Networkng 2014, 2014:138 Page 10 of 21 QoS parameters of the delay-senstve flows are as follows: I delay senstve H = 1 H,delay senstve (13) I delay senstve =1 I delay senstve V = 1 V,delay senstve (14) I delay senstve =1 where I delay senstve s the number of delay-senstve users, H s the average normalzed delay, and V s the average QoS volatons of all the delay-senstve users. The nput and output membershp functons of the DRC are shown n Fgure 6a and 6b, respectvely. The maxmum lmt, μ max s set accordng to the followng fuzzy rules: 1. If H s low AND V s low THEN μ max s hgh 2. If H s hgh AND V s low THEN μ max s low 3. If H s hgh AND V s hgh THEN μ max s low 4. If H s low AND V s hgh THEN μ max s medum The nput degree of membershp s determned by the trapezodal nput membershp functons. A lower average packet delay and loss rate causes rule 1 to have a hgher degree of membershp. Therefore, μ max s maxmum as gven by the centrod of the hghest area trangle membershp functon as shown n Fgure 6. On the other hand, μ max s set to mnmum when a hgher average HoL delay and packet loss rate causes the smallest area trangle to be defuzzfed through rule 2 and rule 3. If the normalzed average delay s lower and average PLR s hgher than the medum area, trangle s defuzzfed as gven n rule Ratonale The man ratonale of utlzng DRC s to serve the followng three goals: Utlzaton of delay tolerant nature of the best-effort traffc: accordng to the polcy gudelnes of the QoS archtecture n the 3GPP standard, the resource allocaton probablty of the best-effort traffc class should be mnmum n stuatons where the network becomes congested wth delay-senstve traffc. When the traffc load reaches the network capacty, the ncrease n average packet s latency of the delay-senstve traffc decreases the maxmum lmt of the output fuzzy set for the best-effort flows as shown n Fgure 7. Snce best-effort traffc s delay tolerant, the decreased maxmum lmt of the output fuzzy set ensures delay-senstve traffc gets prorty over best-effort traffc. Channel dversty explotaton: the man goal of the scheduler s to maxmze the system throughput a b Fgure 6 Input and output membershp functons of the DRC. (a) Input membershp functons low and hgh of the DRC. (b) Output membershp functons low, medum, and hgh of the DRC.

11 Khan et al. EURASIP Journal on Wreless Communcatons and Networkng 2014, 2014:138 Page 11 of 21 Fgure 7 DRC s response to the normalzed average delay and average PLR. subject to mantanng the deadlne volatons below the prescrbed threshold (Equaton 5). At lower normalzed average packet latency, the prorty dfference between the delay-senstve and best-effort flows s mnmal. Hence, flows from dfferent traffc classes are scheduled based on ther QoS performance and channel qualty. Utlzaton of same output fuzzy set for the DRC, proactve, and reactve controllers: the prortzaton of the delay-senstve flows w.r.t the best-effort traffc can be acheved by usng the same output fuzzy set for the proactve, reactve, and DRC controllers. When the output fuzzy set of these controllers are same, then the ncrease n latency of the delay-senstve flows causes a reducton n the output fuzzy set of the best-effort traffc as shown n Fgure 7. When the network becomes heavly congested, then delay bound volatons occur for the delay-senstve flows. The delay bound volaton further reduces the output fuzzy set of the best-effort traffc as shown n Fgure 7. Thus, decreasng the resource allocaton probablty of the best-effort traffc. 3.4 Tme doman prorty The proactve controller output, μ p,andthereactvecontroller output, μ r, defne the tme doman prorty of the schedulng rule. Let μ (n) be the fnal tme doman prorty whch s the product of the output of both the controllers gven as: μ (n) = (μ p μ r ) α t (15) where α t s the tme doman farness parameter whch enables the operator of the system to tune the farness level. The hgher the value of α t, the hgher wll be the tme doman prorty of users sufferng from relatvely poor channel qualty, hgher HoL delay, and hgher QoS volatons. 3.5 Frequency doman prorty The tme doman prorty, by utlzng past and current CQI feedbacks, consders the channel qualty over a small wndow. The goal of the tme doman prorty s to control the farness among the users. On the other hand, the goal of the frequency doman prorty s to mprove the system

12 Khan et al. EURASIP Journal on Wreless Communcatons and Networkng 2014, 2014:138 Page 12 of 21 effcency by consderng only the current CQI feedback. Due to multpath propagaton and nterference from the neghborng users, there s a varable amount of fadng on the PRBs of each user. Effcency as well as farness can be enhanced f ths nformaton s utlzed. By employng the CQI feedbacks on each of the PRBs, nformaton on the nterference and multpath propagaton can be obtaned [28,29]. Hence, we adopt a parameter called relatve strength of user on PRB ϕ whch s gven as: θ (n),ϕ = χ (n),ϕ χ (n) (16) where θ (n),ϕ gves nformaton on the varable amount of fadng on the PRBs of each user. If a user s experencng a hgh nterference on some of the PRBs, ths factor assgns a lower weght to such PRBs. On the other hand, the PRBs wth the best channel qualty for a user wll be assgned a hgher weght thus explotng the ndependent mult-user frequency selectve fadng. The frequency doman prorty, Ɣ (n),ϕ,ofuser on PRB ϕ s the product of channel qualty and relatve strength: Ɣ (n),ϕ = χ (n),ϕ θ (n),ϕ. (17) Replacng n (17), the expresson of θ (n),ϕ gven n (16), we get Ɣ (n),ϕ ]2,ϕ = [ χ (n) χ (n). (18) 3.6 Fnal schedulng prorty metrc It has been shown n [15] that a good trade-off between farness and effcency can be acheved by defnng a prorty functon whch s the product of the logarthmc functon of the tme-doman prorty and a lnear functon of the nstantaneous rate on each PRB. The tme-doman prorty used n the LOG rule n [15] s a functon of the HoL packet delay. We use a tme-doman prorty whch s derved from fuzzy logc and s a functon of the user s HoL packet delay, tme-averaged channel qualty and packet loss rate. The fnal prorty, PRF (n),ϕ,ofuser on PRB ϕ s a functon of the logarthm of the tme doman prorty and t vares lnearly wth the frequency doman prorty as gven below: PRF (n),ϕ = log(1 + μ(n) )Ɣ (n),ϕ. (19) User s allocated a PRB ϕ satsfyng the followng rule: ( = argmax PRF (n),ϕ ). (20) It s mportant to note that state-of-the-art schedulng rules serve best-effort flows wth the classcal delaynsenstve PF rule and prortze the delay-senstve traffc by consderng the HoL packet delay. We use the same prorty equaton gven n 19 for all the traffc classes; dynamc prortzaton between the delay-senstve and best-effort classes s acheved by usng the DRC. More detals on the prortzaton of dfferent traffc classes s gvennthefollowngsectons. 4 Performance evaluaton 4.1 Benchmark schedulng rules In order to assess the performance of the proposed FCS schedulng rule, we compare t the wth the state-of-theart strateges shown n Table 1. Accordng to the table, γ s a constant whose value s adjusted to account for dfferent delay requrements for dfferent flows. N (n) Q sthenumberofpacketsresdng n the queue of user s flow at the enodeb. In order to provde farness, the delay-based rules (M-LWDF, EXP-PF, LOG-RULE, and EXP-RULE) use the HoL delay, whereas the queue-based rules (M-LWDFQ, EXP-PFQ) utlze the queue sze of each flow. In the log (LOG- RULE) and exponental (EXP-RULE) rules, b = 1 R (n),ave and a s a tunable parameter. The hgher the value of a, the hgher wll be the prorty of the delay-senstve flows. All the prorty rules shown n Table 1 are for delay-senstve traffc. These rules calculate the prorty for the best-effort traffc accordng to the PF rule gven as: PRF (n),ϕ = χ (n),ϕ R (n),ave Table 1 Benchmark schedulng rules for delay-senstve traffc Strategy Prorty functon χ (n),ϕ M-LWDF [12] γ H (n) R (n),ave χ (n),ϕ M-LWDFQ [39] γ R (n),ave χ (n),ϕ EXP-PF [12] γ R (n),ave χ (n),ϕ EXP-PFQ [39] γ R (n),ave LOG-RULE [15] EXP-RULE [15] N (n) Q ( ) γ exp H (n) γ H 1+ γ H exp γn(n) Q γ N (n) Q 1+ γ N (n) Q [ ( )] H b log a (n) H (n),max ( ) H (n) a H b exp (n),max χ (n),ϕ 1+ H (21) χ (n),ϕ

13 Khan et al. EURASIP Journal on Wreless Communcatons and Networkng 2014, 2014:138 Page 13 of 21 where tme-averaged throughput R (n),ave s defned as: R (n),ave = R(n 1),ave (1 1 ) + 1 R (n 1). (22) n w n w where R (n 1),ave s the average throughput at schedulng nstant n 1. R (n 1) sthenumberofbtstransmttedat schedulng nstant n 1. n w s the sze of the tme-average wndow also known as the exponental averagng constant. The hgher the sze of the tme-average wndow, the hgher the mpact of the nstantaneous channel qualty. 4.2 Smulaton scenaro In order to nvestgate the performance of the proposed schedulng algorthm, a lnk-level smulator [30,31] bult on MATLAB s object-orented features s selected as the smulaton platform wth all the basc features of an LTE lnk layer. The fuzzy controllers are desgned by utlzng the Matlab s Fuzzy Logc Toolbox. The fuzzy logc prorty schedulng, under varyng load, has been studed n [21], where only vdeo traffc was consdered. In ths work, delay-senstve traffc s characterzed by vdeo and VoIP flows. In order to smulate best-effort traffc, CBR flows wth the data rate of 400 Kbps are selected. On the other hand, 64 Kbps traffc wth the threshold packet loss rate of 1% and maxmum delay budget of 100 ms s selected for VoIP users. These QoS parameters are selected accordng to LTE QCI (QoS class ndcators) [32]. Vdeo traffc s generated from a trace fle [33] wth the average and peak traffc rates of 530 and 1,500 kbps, respectvely. The maxmum delay budget for vdeo packets s 200 ms whereas the threshold packet loss rate s 5%. For non-crtcal vdeo applcatons, 5% packet loss rate corresponds to a peak sgnal-to-nose rato (PSNR) of approxmately 29 to 30 db [34]. Therefore, 5% s consdered as the threshold packet loss rate for an acceptable vdeo qualty. Table 2 reports the smulaton parameters adopted for the LTE system and the wreless channel. We smulate 18 vdeo flows (9.54 Mbps), 27 VoIP flows (1.728 Mbps), and 9 best-effort flows (3.6 Mbps) correspondng to a total average nput traffc rate of Mbps. The man motvatons for such traffc dstrbuton are the followng: It has been reported n [35] that by 2015, approxmately 66% of moble s traffc (n terms of petabytes per month) wll be vdeo and the proporton of VoIP traffc wll be a mnorty. Therefore, the proporton of traffc n our smulaton scenaro s domnated by vdeo followed by the best-effort and VoIP traffc. Specfcally, we selected a loaded network wth 64% vdeo, 11% VoIP, and 25% best-effort traffc (n terms of average nput traffc at the enodeb). The proposed scenaro corresponds to an average nput traffc rate of Mbps. In order to evaluate the channel utlzaton n terms of average spectral Table 2 Smulaton parameters - downlnk LTE schedulng for delay-senstve and best-effort traffc Parameters Value Smulator LTE lnk level smulator [30,31] Bandwdth, carrer frequency 5 MHz, 2.1 GHz UE dstrbuton, cell radus Unform, 1 km Channel 3GPP-TU (typcal urban) Pathloss model Hata-Cost-231 model Shadowng model Log-normal shadow fadng HARQ Up to 3 synchronous retransmssons Channel fadng Block fadng (1 ms) UE speed 15 to 100 km/h (users movng ndependently at varable speed) CQI averagng method Mutual nformaton effectve SNR mappng (MIESM) [37] H max, PLR thr (vdeo) 200 ms, 5% [34,38] H max, PLR thr (VoIP) 100 ms, 1% [32] H max, R mn (best-effort) 300 ms, 200 Kbps [32] Number of vdeo, VoIP and 18, 27, and 9 best-effort users Average rate requrements 530, 64, and 400 Kbps for vdeo, VoIP and best-effort users n c (Tme-averaged channel 100 ms qualty wndow) and n w (Tme-averaged throughput wndow) effcency, we smulate an optmum sum rate maxmzaton strategy. The optmum strategy maxmzes the system throughput wthout consderng the delay constrants. The average channel qualty (n terms of SINR) of the users s set such that the total system throughput, sum throughput of all the flows, produced by the throughput maxmzaton strategy [36] s 13.6 Mbps (2.72 bts/s/hz). Ths corresponds to a heavly loaded system where the nput traffc s approxmately 110%, n terms of bts/s/hz, of the maxmum system capacty. Our man goal s to study the farness and effcency performance of the proposed and benchmark schedulng rules when the delay bound and packet loss threshold constrants are consdered. We consder the tme-averaged channel qualty over the perod, n c = 100 ms. All the benchmark schedulng rules utlze the tme-averaged throughput. In order to have a far comparson, the exponental averagng constant n w s set to 100 ms. In the lterature, the optmum sze of the

14 Khan et al. EURASIP Journal on Wreless Communcatons and Networkng 2014, 2014:138 Page 14 of 21 Table 3 Tunable parameters for FCS strategy Strategy Output fuzzy Output fuzzy α t set for vdeo set for VoIP FCS1 {0, 2} {0, 2} 2 FCS2 {0, 2} {0, 2.2} 2 FCS3 {0, 2} {0, 2.2} 3 exponental averagng constant s from 100 to 1,000, wth the 100 beng utlzed n scenaros yeldng hgh farness n terms of throughput. The FCS schedulng strategy has the followng tunable parameters: The tme doman farness parameter α t manly used to adjust the farness level. The output fuzzy set for the DRC, proactve, and reactve controllers. As dscussed n Secton 3.3.1, the same output fuzzy set s utlzed for all the controllers. The membershp functons and fuzzy rules of the DRC are set such that by utlzng the same output fuzzy set for all the controllers, dynamc prortzaton s acheved between the delay-senstve and best-effort traffc. Table 3 reports the settngs consdered n the smulatons for the tunable parameters. The table reports three examples. In the frst one (FCS1), the output fuzzy set s the same for both VoIP and vdeo classes,.e., vdeo and VoIP traffc flows have the same prortzaton. It s mportant to note that the LTE QoS archtecture specfes QCI for each of the consdered traffc classes [32]. Accordng to the QCI, the VoIP traffc class has the hghest prorty followed by the vdeo and best-effort traffc class. Therefore, n the second case (FCS2), VoIP s prortzed by ncreasng the maxmum lmt of the output fuzzy set from 2 to 2.2. The tme doman parameter α t s set to 2. Fnally, n the thrd case, we retan the VoIP prorty and ncrease the tme-doman parameter from 2 to 3. The FCS2 and FCS3 modes follow the QCI servce archtecture where VoIP s prortzed over vdeo traffc, whle the FCS1 mode gves same prorty to both the delay-senstve traffc classes. 4.3 Results and dscusson Frst, we analyze the farness and effcency performance of the FCS strategy accordng to the settngs reported n Table 3. Next, we compare the FCS strategy wth the benchmark schedulng strateges reported n Secton 4.1. Fgure 8 Performance of each traffc class wth dfferent output fuzzy set.

15 Khan et al. EURASIP Journal on Wreless Communcatons and Networkng 2014, 2014:138 Page 15 of 21 Results n terms of packet loss rate (delay-senstve flows) and throughput (best-effort flows) for the proposed rule wth dfferent parameters are shown n Fgures 8 and 9, where users are arranged n a decreasng order of the channel qualty. The user wth lowest ndex has the best channel qualty whch then decreases wth the ncrease n user ndex. Usng the same prortzaton (output fuzzy set) for vdeo and VoIP (FCS1, Fgure 8) results n a hgher QoS volatons for the VoIP flows,.e., seven VoIP flows are volatng the 1% PLR threshold. On the other hand, three vdeo flows are volatng the 5% PLR threshold. In the second set of smulatons (FCS2, Fgure 8), there s a sgnfcant reducton n the PLR of the VoIP flows,.e., only one VoIP flow has a delay bound volaton (PLR) of more than 1%. In FCS2 mode, the mpact of the tmedoman prorty s hgher for the VoIP flows. The ncrease n the HoL delay and PLR prortzes VoIP flows more than the vdeo flows. The result s an ncrease n the PLR of the vdeo flows as shown n Fgure 8. There s also a slght reducton n the throughput of the best-effort flows. Hgher lmt of the fuzzy set, 2.2, for VoIP traffc serves well accordng to the QoS archtecture of LTE as t s the hghest traffc prorty class. Any further ncrease n the maxmum lmt of the fuzzy set for VoIP traffc wll penalze the vdeo and best-effort flows by servng VoIP traffc. Next, we analyze the mpact of the tme-doman parameter α t. An ncrease n the tme-doman prorty parameter (FCS3, Fgure 9) allocates relatvely more resources to the worst channel flows snce tme-doman prorty s a fuzzy functon of the HoL packet delay, PLR, and tmeaveraged channel qualty. It s mportant to note that the proporton of vdeo traffc (18 flows wth average rate requrements of 540 kbps) s hgh wth respect to the VoIP traffc (27 flows wth rate requrements of 64 kbps). Therefore, an ncrease n α t results n a sgnfcant mprovement for vdeo flows as shown n Fgure 9. In other words, more resources are allocated to the lower channel qualty vdeo flows and as a result, ther PLR s reduced at the expense of a slght ncrease n the PLR of VoIP flows. There s also a margnal ncrease n the PLR of good channel vdeo flows. Accordng to the fgure (FCS3, Fgure 9), the worst served vdeo flow has a PLR of approxmately Fgure 9 Performance of each traffc class wth dfferent tme-doman prorty.

16 Khan et al. EURASIP Journal on Wreless Communcatons and Networkng 2014, 2014:138 Page 16 of % and the worst served VoIP flow suffers from a PLR of 1.6%. Thus, the FCS3 mode results n an mproved farness performance for the delay-senstve flows. Under hgh load, the tme-doman prorty of the delay-senstve flows wll always be hgher than best-effort flows. Therefore, ncrease n α t wll further enhance the prorty dfference and results n a reducton n the throughput of best-effort flows. After analyzng the performance of the FCS rule for dfferent desgn parameters, we compare the FCS rule wth the well-known schedulng rules. Frst, we dscuss the performance of state-of-the-art schedulng rules for delay-senstve traffc. Fgures 10 and 11 analyze the performance of state-of-the-art schedulng rules for delay-senstve traffc and compare t wth the FCS rule. Although M-LWDF s generally consdered as the best schedulng rule for delay-senstve traffc [12], the PLR performance of the M-LWDF schedulng rule for low channel qualty vdeo flows s very poor. Accordng to Fgure 10, the PLR of the worst served user s as hgh as 20% and approxmately seven flows suffer from the QoS volaton,.e., havng PLR above the 5% threshold. Hgher QoS volatons for vdeo flows stem from the fact that the M-LWDF rule explots tme dversty by consderng the tme-averaged throughput. The vdeo flows exhbt varable bt rate (hgher peak-to-average rate rato) characterstcs. Therefore, the hgher tme-averaged throughput n the schedulng decson of the M-LWDF rule ncreases the probablty of delay bound volatons for the vdeo flows havng lower channel qualty. Hence, hgh rate delaysenstve flows wth lower channel qualty suffer from HoL delay bound volatons. On the other hand, none of the VoIP flows suffer from delay bound volatons (Fgure 11) manly because lower tme-averaged throughput prortzes the VoIP flows rrespectve of ther channel qualty. M-LWDFQ reduces the QoS volatons of the vdeo flows by consderng the queue sze based on vrtual token mechansm [39]. The PLR of the worst served user s approxmately 12%, and there are only three flows volatng the PLR threshold of 5%. The mproved performance for vdeo flows s manly due the fact that the M-LWDFQ rule prortzes hgh rate flows by consderng the number of packets n the queue based on vrtual token mechansm, as compared to the M-LWDF rule whch reles on the HoL delay. As a result, flows havng fewer packets n the queue are penalzed f ther channel qualty s low. Fgure 11 shows that seven VoIP flows have PLR of more than 1%. Therefore, the M-LWDFQ rule ncreases the QoS volaton for the VoIP flows as compared to the M-LWDF schedulng rule. Fgure 10 Performance of the FCS, M-LWDF, M-LWDFQ, EXP-PF, and EXP-PFQ schedulng rules for the vdeo flows.