FASA – eFficient and Accurate SchedulingAlgorithm for IEEE 802.15.3 based ad hoc networks

Attila Torok, Lorant VajdaAmI Project Group,

Bay Zoltan Foundation for Applied ResearchEmail: {torok, vajda}@ikti.hu

Abstract— A scheduling algorithm for IEEE 802.15.3based ad hoc networks is proposed. Building on the per-formance analysis of previous proposals, a new algorithmcalled FASA was designed to exploit the features of the802.15.3 architecture so as to achieve higher performance,with better channel utilization, and lower induced over-head. During the analysis, several performance metricsare investigated. We show that the proposed algorithm,with its special extensions, outperforms the previously usedalgorithms, while providing low complexity and overhead.

I. I NTRODUCTION

Nowadays, the wide range of emerging wireless appli-cations triggered the appearance of specialized wirelessprotocols. Bluetooth represents such a specialized solu-tion, with the primary goal of building cheap and simpleWireless Personal Area Networks (WPAN). Targetingapplications with different demands, such as broad-band and Quality of Service (QoS) requirements, IEEE802.15.3 opts to use a different channel access technol-ogy. Similarly to Bluetooth, 802.15.3 is also based on acentralized and connection-oriented ad-hoc networkingtopology, with a master-slave hierarchy. Nevertheless,unlike Bluetooth, the master (called PNC) node handlesonly the admission control, scheduling and managementtasks, without being involved in packet forwarding.

The MAC layer of the protocol employs a time-slottedsuperframe structure. The superframe can be separatedto a channel request part and a data transmission part.Channel request is contention-based, while for datatransmissions time division multiple access (TDMA) isused. The superframe construction has three phases:channel request, scheduling, anddata transmission. Thechannel request phase is used by the nodes to sendtheir requirements to the master. Using the gatheredinformation, the master can schedule the time-slots forthe next superframe. Finally, in the data transmissionpart the nodes transmit their data in the time-slots. As

several technical papers point it out [4][5][6], a dynamicslot reservation MAC protocol with a proper schedulingalgorithm is important in such an environment.

In this paper we investigate implementation and per-formance issues related to scheduling algorithms used in802.15.3 networks. The analyzed scheduling protocolsare based on the Earliest Deadline First (EDF) andon the Shortest Remaining Processing Time (SRPT)algorithms [2][3]. As far as we know, the only work thatdeals with scheduling in 802.15.3 networks is [8]. Theauthors present a performance analysis, and show thatSRPT outperforms EDF in terms of higher throughputand lower job response time; therefore, it is capable tosupport real-time VBR (rt-VBR) traffic.

During the performance analysis of these schedulerprotocols we found some drawbacks that lead to net-work resource wastage. The suboptimal scheduling canbe attributed to some special features of the 802.15.3system; thus, special design issues must be consideredfor proper scheduler implementations. Based on ourfindings, we designed a new scheduler protocol, whichis a combination of the EDF and SRPT approaches. Aswe show in the paper, by extending a scheduler withspecial state-information signaling and burst eligibilitydecision, better channel utilization is achieved, avoidingthe underutilization of the superframes.

In the paper we demonstrate through simulations thatour scheduler architecture outperforms the others interms of channel utilization, end-to-end delay variation,and signaling efficiency.

This paper is organized as follows: Section II providesa brief overview of the scheduling algorithms previ-ously used in 802.15.3 networks. Section III describesthe proposed algorithm, while Section IV provides theperformance analysis and comparison of the varioussolutions. Finally, Section V concludes the paper.

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II. SCHEDULING ALGORITHMS USED IN 802.15.3NETWORKS

The Shortest Remaining Processing Time (SRPT)scheduling policy has been recommended in the liter-ature [2] for both data transmission scheduling and webserving. SRPT selects for service the pending job in thesystem with the least remaining service time. By usingthis policy the SRPT discipline minimizes the numberof transactions in the system at any given time. This inturn implies that the average number of transactions, andthus the mean response time of the jobs, is minimized.To do so, the length of the jobs has to be known by thescheduler. The policy is preemptive, so that if a new jobarrives with a smaller service time than that remainingfor the job currently in service, the scheduler switchesimmediately to service the newly arriving job. The SRPTpolicy is provably optimal: it guarantees the lowest meanresponse time for the overall system. Under overloadedconditions SRPT also minimizes the number of jobs thatare starved. In 802.15.3 systems the instantaneous queuelength of the flows can be associated with a job [8].

Earliest Deadline First (EDF)is a dynamic priorityscheduler, where the prioritization is based on packetarrival times [3]. The deadline assigned to a packet isits arrival time plus the delay guarantee associated withthe flow of the packet. The scheduler always selectsthe packet with the smallest deadline for transmission.Thus, the priority of a packet increases with the timeit spends in the system. One of the main advantagesof the EDF scheduler is that it allows the separationof throughput and delay guarantees; thus, the inefficientuse of resources is avoided. In terms of implementa-tion complexity EDF scheduling can be quite hard torealize. This complexity arises because the schedulerselects the packet with the smallest deadline, whichinvolves keeping a priority list of deadlines. Therefore,the insertion and deletion from the prioritized list hasO(log(# of awaiting packets))complexity. However, thiscomplexity is avoided in the 802.15.3 implementation,since at a certain time, for a certain flow, only a singleburst is managed by the scheduler in the priority list.Thus, the complexity of the list management is reducedto O(log(# of active flows)).

However, the bandwidth can be more efficiently allo-cated if the schedulers knows the length of the queue. Inthe next section we show that more precise informationcan be used to improve the performance of the sched-ulers. This information is related to the bursts, whichconstitutes a flow.

III. T HE FASA SCHEDULER

In this section we propose a new scheduling protocolfor 802.15.3 networks, called eFficient and AccurateScheduling Algorithm (FASA). The system is enhancedwith flow state signaling, burst based eligibility decision,and time-slot allocation reordering. The flow types con-sidered here are real-time VBR (rt-VBR), non-real-timeVBR (nrt-VBR) and CBR.

A. Information provided for the scheduler

For bandwidth allocations in a superframe, 802.15.3uses Guaranteed Time Slots (GTS) with static ordynamic position. Due to its characteristics, optimalscheduling of rt-VBR traffic requires allocation of vari-able length GTS. Time-slots are wasted if the sourcedoes not have packets to transmit but the scheduler stillallocates GTS for its respective flow. Even though otherflows may have waiting packets, they do not get served inthe actual superframe. Thus, to achieve better resourceutilization, the scheduler must be provided with infor-mation regarding the internal state of the mobile nodes.This information can be the instantaneous queue length,the deadline of the packets, the packet arrival rate, etc.Unfortunately, the scheduler does not have direct accessto these parameters. Therefore, its performance highlydepends on how fast and accurately this information canbe transmitted from the mobile node to the schedulerlocated in the PNC.

Nodes in wireless ATM networks use piggybackingtechniques to transfer the internal state information [4].The base station forwards all the data packets of the com-municating nodes; thus, it can analyze the piggybackedstate information. These solutions involve an excessiveoverhead to transmit the information in a timely manner,especially in case of traffic with highly dynamic anddelay sensitive nature (rt-VBR flows).

In 802.15.3 networks, since the PNC is in the ra-dio range of the communicating nodes, it can receiveand analyze their packets. Investigation of schedulingalgorithms in 802.15.3 networks is presented in [8].Here the piggybacked internal state is the number ofpackets in each queue. However, receiving every packetoverwhelms the PNC in terms of processing power andenergy consumption. Therefore, this kind of piggyback-ing solution is not suitable for systems where the PNCparticipates only in scheduling and piconet management,but not in packet forwarding.

In [1], in order to minimize the signaling overheadand to maximize the statistical multiplexing gain of ATMservice classes, the authors use theresidual lifetimeof

the rt-VBR flows as a parameter to determine the trans-mission time of reservation requests. The wireless nodeswith rt-VBR traffic determine how much bandwidth mustbe provided for the respective flow by the end of itscell transfer deadline. This information is transmittedalong with each uplink burst, and includes only thenumber of requested slots and the number of framesafter which the deadline expires. Each burst containspiggybacked information about the reservation request ofthe subsequent burst in the queue. The authors supposethat there are always cells to transmit in the buffers;thus, the case when the subsequent reservation cannot bepropagated to the base station, due to buffer depletion,does not occur.

As we can see, none of the above presented solutionsis suitable for implementing state-information signalingfor 802.15.3 network nodes. Some of the solutionsinduce excessive overhead, energy, and processing de-mand, while the others use an over-simplified modelingof traffic characteristics.

1) Flow representation: In the superframes, CBRflows use fixed time-slot allocation; thus, their schedul-ing task does not require any special information. ForVBR traffic classes both delay-sensitive and delay-tolerant traffic types have been considered. Based ontheir characteristics, we model a VBR flow with batcharrival processes (Figure 1). The flow consists of packetbursts (frames) following each other in a timely manner.One burst can be fragmented at MAC level in one ormore packets, considered as being generated at the sametime. The time elapsed between two bursts has a highertimescale; it can be variable or periodic, depending onthe VBR traffic type. Thus, each burst can be representedby the packets’ generation time and the number ofpackets that constitute the burst. Theresidual lifetime(deadline) of the burst is calculated at the sender node,and is based on the generation time and the lifetime ofthe burst. It is relative to the superframe when the burstwas generated, and will represent theresidual lifetimeof the burst in number of superframes. Based on thecharacteristics of VBR flows, we can determine whenand how the nodes should notify the PNC.

2) CTRL packets:Because of the above mentionedon-off characteristics of the flows, even when the state ispiggybacked in data packets, during an off period controlinformation must be sent somehow, in order to signalan upcoming burst. This implies that a contention basedrandom access or a polling-based access scheme must beused besides the piggybacking mechanism. We considerthat a contention based solution would introduce sig-

nificant delay in the case of a heavily loaded network;therefore, it is not suitable for us. Furthermore, in orderto avoid the excessive overhead caused by reservingMAC header fields for piggybacking, special control(CTRL) packets are used; they are sent by the nodesonly when it is necessary to update the internal stateinformation of the PNC. In our solution we reserve atime-slot for CTRL packets in the case when there is anoff-period of the respective flow. These CTRL packetsare used by the nodes to notify the scheduler about theparameters of an upcoming burst.

As an example, for the flow from Figure 1 twoCTRL packet time-slots are allocated, at timest1 andt3respectively. The flow has two bursts that arrive att1 andt2 respectively. The deadline of the bursts is convertedto a relative value; in this case it has the length of fivesuperframes. Thus, when the node att3 sends the CTRLpacket related to the second burst, this has the deadlineof only four superframes.

Fig. 1. Flow representation for the scheduler

3) Time-slot allocation for CTRL packets:Let usconsider the possible cases that can arise during thelifetime of a flow (Figure 2).

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Fig. 2. Possible burst behavior scenarios

In the first scenario (Figure 2a) the burst can bescheduled in one superframe (in the 4th superframe onthe figure). In this case, it is advisable to allocate a time-

slot for a CTRL packet after the burst is scheduled (t3),in order to give the chance to the node to signal itsinternal state if modified. If a new burst arrives untilt3,based on the state information received in the new CTRLpacket the PNC is able to schedule the new burst in theupcoming (5th) superframe. In the second case, the burstis so long that it cannot be scheduled in one superframe(Figure 2b). Therefore, until the last superframe (6th)there is no need to allocate time-slots for control packets.

From the first two scenarios it seems that sending theresidual lifetime (deadline) and the number of packets inthe burst conveys enough information for the scheduler.The advantage of passing these parameters comparedto [8] relies on cases with high network load. In oursolution the scheduler will be able to make a moreoptimized scheduling, by allocating only the necessaryamount of time-slots (one burst) for each flow. Sendingjust an arbitrary number of packets from each flow [8]leads to situations when half of the burst is scheduledand the other half is left in the queue; this can result inmassive packet dropping.

The third scenario (Figure 2c) occurs when there ismore than one burst in the node’s queue. If the super-frame is not overloaded, it is advisable to schedule alsothe second burst right after the first one. To accomplishthis, just the parameters of the first burst do not presentenough information for the scheduler. Therefore, in ourmethod, besides the parameters of the first burst, theoverall queue size is also transmitted. By using thisinformation, the PNC will be able to schedule in theactual superframe as many packets from the second burstas possible. After the packets of the second burst a CTRLpacket time-slot is allocated; thus, the node will be ableto send more precise information about the remainingpackets of the second burst.

To summarize, the information sent in the CTRLpackets in the case of rt-VBR flows are:• lifetime: the residual lifetime of the first burst ex-

pressed in number of superframes;• nr. of packets: the number of packets in the first

burst;• queue size: length of the overall queue size for the

respective flow.Non-real-time VBR flows do not have stringent deadlinesas real-time flows. Therefore, it is not necessary totransfer information related to the residual lifetime ofthe first burst. These flows will be scheduled based onlyon theirqueue size information, which is sent in CTRLpackets. By sending only the queue size, the signalingoverhead will also be reduced.

4) Burst aggregation: The residual lifetime of thebursts is calculated relative to the ongoing superframe,when the burst is generated. Until now, only the case ofone burst per superframe was taken into consideration.In certain cases it can happen that during the lifetime ofa superframe more bursts are generated. In such cases,sending information only about the first burst can leadto the other bursts timing out, as without the additionalinformation the scheduler will presume that there isenough time to serve the next bursts. To prevent theabove mentioned problem, FASA usesburst aggregation.In case of burst aggregation, each burst generated duringthe current superframe gets the same relative deadline.The number of packets signaled is the sum of the packetsof the aggregated bursts. By using aggregation, signalingoverhead is reduced; in the meantime, the deadline limitof bursts is kept under control.

B. Phases of flows

At any given time, the flows in the scheduler can bein one of the following phases:

Initial phase: after Call Admission Control (CAC),when there is no information about the actual internalstate of the flow at the scheduler. In this phase a time-slot for a CTRL packet must be allocated in order togive the opportunity for the node to signal an arrivingburst.

Starting phase: after the PNC received a CTRL packetabout the flow’s state it will enter in starting phase. Thescheduler will be able to allocate time-slots for the firstburst of the flow before the packets’ deadline expires.When the flow is in starting phase, it will not get anytime-slot for CTRL packets, as the PNC has alreadyall the information necessary for scheduling. Until thedeadline expiration of this burst, there is no need forinformation about the next burst.

Middle phase: the flow enters in middle phase afterthe PNC scheduled the flow, and the flow’s packets arestarted to be transmitted. At the end of this phase (lastpacket of the burst), a time-slot will be allocated for aCTRL packet. The possible scenarios and the reactionof the scheduler in this phase are illustrated in Figure2. If there is a new burst in the queue, at the end ofthis phase, the state of the flow will switch back to thestarting phase.

End phase: follows the transfer of the burst. In thisphase the PNC does not have valid information aboutthe flow. This can happen for example when there areno more packets in the queue. During this phase, in eachsuperframe the PNC allocates a time-slot for a CTRL

packet, in order to get information about an upcomingburst. The scheduler remains in this phase until a newburst is signaled; then it enters in the starting phase.

C. Determination of burst transfer eligibility (BTED)

In the previous section a burst was characterized by itsresidual lifetime and the number of its packets. If the life-time of the burst expires before all its packets reach thedestination, the received packets will be dropped by thereceiver. This means that the transmitted packets weresent unnecessarily; thus, bandwidth wastage appears dueto a wrong scheduling decision.

Analyzing the schedulers presented in Section II, itturned out that SRPT based scheduling suffers less frombandwidth wastage caused by expired residual lifetimethan deadline based scheduling. SRPT always selects theburst with the smallest length not taking into consid-eration the residual lifetime. If, during the serving ofa burst, a new burst with smaller length appears, thescheduler will switch and start to serve the newly arrivedburst. Thus, the shorter bursts have higher probabilityfor successful transmission. In the case of deadlinebased scheduling the scheduler selects the eligible burstbased on its residual lifetime. If there is congestion, thescheduler makes bursts with high residual lifetime to waituntil their residual lifetime becomes the smallest one.Because of this kind of prioritization, in many cases theresidual lifetime of longer bursts will expire before thelast packet can be sent.

1) Eligibility decision for real-time VBR flows:Forrt-VBR flows, based on the first selection criterion,the FASA scheduler selects the eligible burst with thesmallest residual lifetime. To prevent the above presentedproblem, the scheduler first determines if all the packetsof the selected burst can be transmitted before the expi-ration of their residual lifetime; the burst is scheduled fortransmission only afterwards. For better decision making,we use a two level decision process. Figure 3 presents themoments when these decisions are applied in a burst’slifetime.

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Fig. 3. Burst transfer eligibility determination

In Figure 3 there is a burst with a residual lifetime of7 superframes and a length of 2.4 superframes. Thus, it

should be scheduled before the 6th superframe.The first level of eligibility decision (Decision 1) is

applied at the beginning of each superframe construction,for every flow that has a burst for transmission. Thepurpose of this process is to eliminate the flows whichalready have no chance of sending all the packets fromtheir bursts before their lifetime expires. The sooner thescheduler realizes that a burst cannot be scheduled fortransmission, the more helpful this information can be.

The second level of eligibility decision (Decision 2) isused after the scheduler has found an eligible burst fortransmission. Before actually selecting and schedulingthis burst, it is checked again whether its lifetime limitis violated or not. This second decision level can be moreaccurate than the first one, as the PNC will have fresherinformation about the state of the flows in the network.

The flows whose bursts cannot be scheduled due tolifetime expiry are announced, and in the upcomingsuperframe a CTRL packet is allocated for them. Basedon the announcement, the sender node is able to dropthe packets of the ineligible burst and send its new stateinformation in the CTRL packet. By using this techniquethe PNC gets fresh information about the state of theflow; thus, more efficient scheduling is possible.

The eligibility decision making cannot be applied forSRPT scheduling, due to its preemptive nature. Becausethe scheduler switches from the serving of a flow to anew flow any time a shorter burst arrives, it is impossibleto calculate when the respective burst will be servedcompletely. Thus, the eligibility decision is applied onlyin the case of EDF and FASA schedulers.

2) Eligibility decision for non-real-time VBR flows:The nrt-VBR flows can spend considerable time in thequeues until they are served; thus, it can happen thattheir packets’ lifetime expires. Unfortunately, during thescheduling this fact will not be taken into account, be-cause the scheduler has no information about the lifetimeof the nrt-VBR packets; it only has information aboutthe queue length of these flows. To avoid the situationwhen nrt-VBR packets are scheduled, but dropped laterby the sender due to lifetime expiry, asender basedburst eligibility decisionis used. The sender can make asimilar decision as the one adopted at the first decisionlevel, at the PNC; thus, it can foresee whether theheadline packets of its queue can be transmitted beforetheir lifetime expires or not. If the burst is not eligible, itwill be dropped from the beginning of the queue. In orderto avoid scheduling based on inaccurate information,when the queue size at the sender changes the PNC hasto be notified.

D. The operation of the scheduler

The FASA scheduler is based on the combination ofthe EDF and SRPT schemes. The scheduler has twoserving cycles.

During the first cycle, as the first selection criterionthe scheduler selects a flow for transmission based on theresidual lifetime of its first burst. The reason behind thisis that in case of an overloaded network, the resourceusage is optimized by first serving the flows with themost stringent deadlines, and delaying the serving offlows with enough residual lifetimes. The second selec-tion criterion is the length of the burst.

If there is more than one flow with the same residuallifetime, then the flow with the shortest burst is selectedfirst. If there is no more flow whose first burst canbe scheduled, and there are additional free time-slotsin the superframe, then the scheduler enters in thesecond serving cycle (called extended mode). During thiscycle the flows are selected based on their queue sizeinformation. The reason behind this selection is that thescheduler has information only about the deadline of thefirst burst. Anyway, all the flows with stringent deadlinesalready have been scheduled; thus, the deadlines of theother queued bursts do not have to be taken into account.

During the scheduling, rt-VBR flows get higher pri-ority to satisfy their deadline limitations. After all thert-VBR demands are satisfied, and if there is additionalfree time in the superframe, the remaining time-slots areallocated for the nrt-VBR flows.

E. Superframe construction

After the scheduling is done, the PNC constructsthe beacon. Based on [6][7] it appears that informationinconsistency can occur due to the data packets beinggenerated at the node after the internal state informationwere sent. This can happen when the time-slot forthe CTRL packet is allocated in the first part of thesuperframe. In this case, the CTRL packet will conveyinconsistent information; thus, the real queue size andthe queue size seen by the scheduler will be different.Therefore, the scheduling will be misguided.

In order to avoid this information inconsistency, time-slot allocations in the superframe are rearranged beforethe beacon is constructed and broadcasted. The time-slots for flows with only CTRL packet allocations arepositioned at the end of the superframe; thus, informationinconsistency is reduced. The order of time-slot alloca-tions for data flows is also scrambled. The flows withlonger allocations will be located in the front of thesuperframe, while the flows with short allocations will

be situated at the end of the superframe. Therefore, if aCTRL packet follows a burst, then there is more chanceto avoid information inconsistency for the respectiveflow.

IV. PERFORMANCE ANALYSIS

This section presents the performance analysis of theFASA algorithm. We apply FASA, SRPT, and EDFalgorithms in the same simulation scenarios, and evaluatethe resulting system performance.

A. Simulation scenario

Simulation results were obtained using a discrete-event simulator, the VINT project Network Simulator(ns2) [11], with the 802.15.3 module presented in [8].We implemented the proposed FASA algorithm, andenhanced the simulator in order to support our analysisdemands.

The simulation topology consists of several nodeslocated in the same coverage area, so that they cancommunicate directly with each other. Therefore, all thenodes are organized in one piconet; there is one PNC,while all the other nodes are slaves. The senders and re-ceivers are selected randomly. The PNC node takes onlycare of scheduling and superframe management tasks;it does not participate in traffic forwarding. Nodes areassumed to be static. Data for each scheduling algorithmwas collected from 20 simulation runs, with differentrandom seeds. All simulations ran for 200 seconds.

We applied several types of traffic with differentparameters under ideal channel conditions. The 802.15.3networks were mainly designed to support multimediatraffic; thus, the most interesting traffic type is the rt-VBR (real-time Variable Bit Rate) traffic.

During the first set of simulations (Simulation I) theTransform Expand Sample (TES) method [10] is usedto generate MPEG4 video traffic traces. The TES modelcaptures both marginal distributions and the autocorre-lation of empirical MPEG4 video traces. The marginaldistributions of the model match the histograms of theempirical traffic, and the auto-correlation functions ap-proximate their empirical counterparts up to a reasonablelag. To simulate multimedia servers or aggregated videotraffic, in Simulation II we aggregated the streams toreach a packet generation interval of approximately3 − 10ms. Simulation III presents mixed multimediatraffic, with both MPEG4 and H.263 video sources.In Simulations II and III real video traces are used,presented in [9]. Both kinds of VBR traffic are generatedwith a frame rate of 30fps.

TABLE I

PARAMETERS FOR SIMULATION

Simulation Parameters Value

Channel bit rate 100MbpsMinimum GTS for rt-VBR 28µsMean offered load by MPEG-4 8MbpsPacket generation intervals 30fpsMean offered load by H263 256kbpsPacket deadline for rt-VBR 33msUsed superframe size 4msTotal number of devices 10 (1 PNC, 9 slaves)Simulation random seeds 20Simulation time 200sec

TABLE II

APPLIED TRAFFIC ON SIMULATIONS

Traffic Type Offered Load/ Stream

Simulation I TES-MPEG4 30fps 8MbpsSimulation II Aggregated Real

MPEG4 30fps8Mbps

Simulation III Real MPEG4 30fpsReal H.263 30fps

8Mbps256kbps

The main parameters used in the simulations arelisted in Table I. The traffic patterns applied in differentsimulations are shown in Table II.

B. Performance metrics

The first and most obvious aspect in network charac-terization is the channel utilization efficiency. Thus, weanalyzed the performance of the respective schedulingalgorithms considering this parameter as the most impor-tant one. The main goal was to analyze the service qual-ity provided by the algorithms in the case of an increasein network load. In order to analyze the performanceof the algorithms, we used the following performancemetrics:

TheJob Failure Ratio (JFR)is a drop ratio metric; allthe packets dropped because of an expired deadline arecharacterized by this metric.

Response Time (RT)is the time between passing apacket from the upper layer to the MAC layer, sending it,and receiving back a MAC layer acknowledgement. Thismetric is measured only for the successfully transferredbursts. Therefore, there can be cases when the systempresents a good RT metric, while JFR is very high.

End-to-end Delay (E2EDelay) Variationis the vari-

ance of the end-to-end packet delay. In case of interactivevideo and audio applications the E2E Delay varianceis a critical parameter that should be bounded to avoiddistracting human users. For video applications, we canconsider either the delay between multimedia frames, orthe delay between the packets that constitute the frames.To guarantee a variance bound for inter-packet delay ismore important than bounding the delay between theframes, because the inter-packet delay variance bounddetermines the buffer length used to store and reconstructthe frames at the receivers.

C. Simulation I – Burst transfer eligibility decisionanalysis

Figure 4, Figure 5 and Figure 6 show the impacton different performance metrics of theburst transfereligibility decision (BTED)mechanism. In order to betterunderstand BTED, the EDF algorithm was also extendedwith it. For all the algorithms the same control informa-tion signaling (presented in Section III) was used.

We point out that as the network load is increasing,the Job Failure Ratio shows in all cases an increasingtendency (see Figure 4). In terms of JFR, the simpleEDF algorithm is outperformed by the other solutions.Relevant performance difference between SRPT (6.8%JFR) and FASA (2.1% JFR) can be seen under mediumnetwork load. The difference between these two valuesshows a 70% performance increase in favor of FASA.

The above explained performance increase can beattributed to the BTED mechanism used by the FASAscheduling algorithm. In case of the SRPT and the simpleEDF algorithms this mechanism is missing, which im-plies the scheduling of those bursts that cannot be fullytransferred before their residual lifetime expires. In thiscase, a part of the burst is sent, while the other part isdropped; this leads to the loss of the entire burst.

The behavior of the simple EDF solution can be ex-plained by the fact that the chosen bursts for schedulinghave their residual deadlines almost expired. Thus, in thecase of EDF needless bandwidth consumption appearsdue to wrong scheduling decisions. On the other hand,by serving bursts with smaller lengths, SRPT avoids ata certain level the burst dropping.

EDF performance can be increased if the BTEDscheme is applied (EDF-BTED). Based on this observa-tion we can divide the results shown in Figure 4 in twoparts. If network load is low (under 60%), the JFR pre-sented by EDF-BTED follows the performance of FASA.If network load is higher than 60%, the achieved JFR forEDF presents a drastic increase. Therefore, under low

and medium network load the deadline based schedulingwith BTED is considered to be determinative. As thenetwork load increases, the SRPT mechanism becomesmore important at scheduling. Thus, at high networkloads both SRPT and FASA have higher performancethan EDF-BTED.

Analyzing these results, it can be seen that BTED is animportant performance increasing mechanism in terms ofchannel utilization. Meanwhile, it is also advisable to usea combination of deadline and SRPT based schedulingrule in case of high network loads, just as FASA does.

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Fig. 4. Effect of BTED on JFR

Figure 5 presents the Response Time (RT) measure-ments for the FASA, SRPT, EDF, and EDF-BTEDscheduling algorithms. As we can see in this figure, theSRPT algorithm presents an almost constant RT value,in spite of the network load increase. Meanwhile, if theFASA or the EDF algorithms are applied, the RT presentsan increasing tendency, with higher values than thoseobtained by SRPT. Even if the FASA algorithm presentshigher RT values than the SRPT, it does not violate themaximum allowed packet lifetime. Therefore, FASA issuitable for real-time traffic scheduling.

The RT difference between EDF and EDF-BTEDcan be attributed to the fact that the EDF schedulercan also switch servicing between the flows. Thus, byswitching between flows that have bursts with the samedeadlines, the mean response time of these flows isreduced. FASA and EDF-BTED do not use this feature.When a flow is scheduled, all its packets are transferredwithout switching to a new flow.

On the other hand, we also analyzed the inter-packetEnd-to-End Delay Variation (E2EDelay Variance – Fig-ure 6). It can be seen that FASA performs better than theother algorithms related to this metric. It achieves lowerinter-packet E2EDelay variation as it does not switchfrom one flow to another during the serving. On the

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Fig. 5. Effect of BTED on RT

contrary, SRPT and EDF always switch the serving offlows, which leads to a considerably higher inter-packetE2EDelay variation. This feature of FASA can reducethe size of the receiver buffer needed to reconstruct thefragmented bursts at the receiver node.

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FASA

Fig. 6. Effect of BTED on E2E Delay Variance

The slight delay variation increase in case of FASAcan be explained considering the superframe construc-tion mechanism presented in Section III-E. After thescheduling, the allocations are rearranged; thus, it canhappen that the last part of a served burst gets time-slots from the end part of the superframe. Therefore, acertain delay is introduced between its packets that arescheduled in the previous and the current superframes.However, this delay will not be higher than the length ofthe superframe. As the network load increases, the prob-ability of some flows’ allocations making the analyzedflow to occupy the end part becomes higher. BecauseEDF-BTED selects longer bursts than FASA (with thesecond, SRPT based selection criterion) the probabilitythat a burst will be rearranged is higher. Therefore, thereis a slight increase between the results of EDF-BTEDand FASA.

D. Simulation II – Burst extension analysis

In this part we analyzed the role of control informationsignaling and the impact of the information sent andprocessed on scheduling performance. To investigatecases when there are many bursts in the queues anaggregated media stream (Simulation II, Table II) wasapplied. The EDF and SRPT were used with the originalinternal state signaling presented in [8], which uses thequeue size as the state information.

For the FASA algorithm we used the proposed state-information signaling (Section III-A.3). In addition to thebenefits of state signaling, the burst aggregation mode(Section III-A.4) and the scheduler’s extended mode(Section III-D) were studied. The investigated versionsof FASA are represented as:

• Y Y: burst aggregation and extended modes areenabled;

• Y N: only burst aggregation mode is enabled;• N Y: only extended mode is enabled;• N N: none of these modes is enabled.

0

5

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40 45 50 55 60 65 70 75 80 85 90 95 100

Network Load [%]

JFR

[%]

FASA(Y_Y)FASA(Y_N)FASA(N_Y)FASA(N_N)SRPTEDF

Fig. 7. JFR of aggregated media streams – 5ms interval

From the JFR graph presented in Figure 7 we canobserve that FASA(YY) exhibits the best performance.FASA(Y N) and FASA(NY) have similar performancecharacteristics like SRPT, while FASA(NN) and EDFhave the worst behavior. From this simulation we candraw the conclusion that both burst aggregation andextended mode are useful for scheduling performanceincrease.

Burst aggregation is important when there are manylittle bursts in a queue, arrived during the same super-frame interval. Extended mode is useful when all thefirst bursts are scheduled, and there are additional freetime-slots in the superframes. In this case FASA(YY)will start to fill up the superframe based on the queuesize information received from the senders.

0

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40 45 50 55 60 65 70 75 80 85 90 95 100

Network Load [%]

JFR

[%]

FASA(Y_Y)FASA(Y_N)FASA(N_Y)FASA(N_N)SRPTEDF

Fig. 8. JFR of aggregated media streams – 3ms interval

When the bursts are arriving with a higher rate, thesituation slightly changes (Figure 8). FASA(YY) stillleads the scheduling performance, but the FASA(NN)version changes its behavior. Without burst aggregationand extended mode, the FASA algorithm basically failsto achieve consistent performance. This can be attributedto the fact that in this case only the first bursts aresignaled for the scheduler, which leads to suboptimalchannel utilization. The queue size information is alsounused, because the extended mode is switched off.

E. Simulation III – General performance analysis onmixed traffic

In a realistic 802.15.3 system, designed mainly to sup-port multimedia traffic, the scheduling algorithm shouldbe able to deal with different types of traffic. In thefollowing we present an analysis applied to differentlysized and coded real-time multimedia traffic (see TableII). All applied traffic types are based on real video tracespresented in [9]. The traffic pattern consists of a fixednumber (35) of H.263 coded video streams (about 10%of the network capacity) and an increasing amount ofMPEG-4 coded video flows. Figure 9 shows the averageJFR for the MPEG4 flows; as H.263 does not sufferany packet loss, all these flows have 0% JFR. The lowJFR for H.263 traffic is attributed to the behavior of thescheduling algorithms. As far as SRPT is concerned, theflows with a small queue size have a higher priority thanthe flows with higher queue sizes. Because the H.263coded video streams have lower offered bandwidth thanthe MPEG4 streams, the queue size of these flows ismuch lower; thus, SRPT will favor these flows. FASAhas a similar behavior. Even if FASA chooses in the firstphase the flows with the same deadline, in the secondphase it works as the SRPT does. Therefore, the flowswith a smaller queue size are served.

When the JFR of the MPEG4 flows is considered,high values of this metric can be observed even underlow network load, in case of EDF or SRPT. This can beattributed to the above mentioned nature of the SRPTalgorithm. In case of EDF, the lack of burst transfereligibility decisions leads to network resource wastage.

0

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Network Load [%]

JFR

[%]

EDF

SRPT

FASA

Fig. 9. JFR in case of mixed traffic

F. Signaling Overhead analysis

We investigated the signaling overhead induced byusing the FASA and SRPT scheduling algorithms. (EDFuses the same state signaling like SRPT.) If SRPT isconsidered, the piggybacked status information (QueueSize information [1 byte]) is located in each packet. Inthe case of FASA, the status information is coded andsent only in the CTRL packets, the Data packet headersremained unmodified. In this case the status informationconsists of the Queue Size Information [1 byte], theResidual Lifetime [6 bits], and the Burst Length [6 bits].During the investigation we applied the same trafficconditions as in Section IV-C. It turned out that bothalgorithms present an increasing overhead as the networkload is increased. This tendency is mainly due to theamount of the generated status information. However, thesignaling overhead does not exceeded 0.4% of the overalltraffic, for neither of the algorithms. The signaling over-head of FASA was lower than of SRPT, due to the factthat FASA uses CTRL packets instead of piggybackingin each data packet. The SRPT implementation also doesnot use state for flow phases, therefore sends the statusinformation in every superframe, even if there is nochange in the flow’s internal status.

V. CONCLUSION

This paper investigates scheduling issues in 802.15.3networks, IEEE’s emerging wireless standard. The per-formance of such a system is highly dependent on

the used scheduling algorithm; therefore, we analyzedthe scheduling algorithms and the additional techniquesused in previous proposals. Based on our findings, weproposed a new scheduler called FASA that is basedon a combination of the EDF and SRPT algorithms.Our solution uses special features, like state-informationsignaling, burst eligibility decision, and time-slot re-ordering.

Using a discrete event simulator (ns2), we analyzedand compared the performance of the proposed schedulerto the other solutions. During the analysis, differentperformance metrics were investigated, showing that theFASA algorithm outperforms the previous solutions.

Future work includes the extension of the scheduler, tocope with multi-hop ad hoc scenarios. A Call AdmissionControl algorithm is also to be developed.

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[11] http://www.isi.edu/nsnam/ns/ 760