Scheduling Techniques in an Integrated Hybrid Node with Electronic Buffers Raimena Veisllari1, Steinar Bjornstad1,2, Norvald Stol1 1
Department of Telematics Norwegian University of Science and Technology, NTNU Trondheim, Norway
[email protected] 2 TransPacket Oslo, Norway Abstract— Integrated hybrid optical packet/circuit switched architectures enable networks with the guaranteed service transport (GST) of circuit switching and the statistical multiplexing known from packet switching. The utilization of the optical lightpaths is increased by inserting low priority statistically multiplexed (SM) traffic in the guaranteed circuit switched traffic gaps. Previous studies of these nodes have used optical packet switches together with reservation techniques to minimize the SM packet losses while giving absolute priority to circuit switched traffic. In this paper we propose a novel scheduling technique that applies electronic buffers and multiple queues to find a suitable SM packet to fit in a GST gap. Additionally, two schemes of managing the output buffer are introduced, the dedicated queue per input and the length-aware buffering. Simulation results demonstrate that their combination significantly increases the utilization of the available capacity and the maximum rate of SM traffic inserted in the network. Keywords-integrated hybrid; electronic buffer; length-aware; time-window;
I.
INTRODUCTION
In recent years, the objective of much of the research work on optical networks has focused on flexible switching paradigms which can accommodate the requirements of next generation networks (NGN). Examples of these paradigms are the different hybrid optical network architectures [1] that combine optical circuit switching (OCS) with optical burst switching (OBS) and/or optical packet switching (OPS). The main purpose is to efficiently utilize the high capacity offered by the optical domain while adapting the network to support different quality of service (QoS) demands. Integrated hybrid architectures, like in [2], [3] and [4], are one type of hybrid networks that has the unique property of using the same wavelength resources for the different switching paradigms. In this work, we consider the optical migration-capable network with service guarantees (OpMiGua) [2] architecture that uses the resources for both circuit and packet switched traffic in a time-interleaved manner. The benefit of OpMiGua is that it increases the bandwidth utilization by inserting statistically multiplexed traffic in the available packet-gaps of circuit switched packet traffic. Furthermore, the processing requirements are low as the intermediate nodes are still bypassed by the transit circuit switched traffic.
The OpMiGua networking concept is illustrated in Fig. 1. At the ingress node, all packets are marked, with either an optical or electronic label, so that the control module of each core node can direct the packets to the appropriate switching module. An OpMiGua node consists of an optical crossconnect (OXC) for the circuit switched traffic and a packet switch for the statistically multiplexed traffic. The circuitswitched packets are categorized as guaranteed service transport (GST) class and have absolute priority over the statistically multiplexed (SM) packet-switched class. Hence, GST traffic is not affected by insertion of SM traffic and has a fixed delay and neither jitter nor losses through the network. The resource utilization is increased by SM packets inserted in vacant time gaps in-between the GST traffic. SM packets are processed hop by hop and forwarded as in traditional packet networks. The GST traffic is switched according to the wavelength and follows a wavelength routed optical network (WRON) path throughout the OpMiGua network. Different GST reservation schemes have been proposed in earlier OpMiGua articles [2], [5]. These studies have analyzed the schemes effect on packet loss performance of the SM traffic and have focused on using optical packet switches in the OpMiGua node. The two different approaches use lengthaware time-window scheduling and no buffering [5], or electronic buffering with simpler scheduling mechanisms [2].
Figure 1. A hybrid network model illustrating the sharing of the physical fiber layer. The optical cross connects and optical packet switches are colocated, either as separate units or as one integrated unit.
In this paper we propose a novel scheduling method and analyze for the first time the performance of an OpMiGua node with electronic buffering and proactive scheduling mechanisms based on the length-aware time-window (LATW) approach [5] with minimum delay [6]. This is different from previous work as we consider the GST traffic not being aggregated into data bursts, but having the same packet length distribution as the statistically multiplexed packet switched traffic. The high wavelength granularity of DWDM technologies has been exploited for contention resolution in previous OpMiGua work. However, many wavelengths to a destination may not be available. This calls for the implementation of efficient reservation schemes which maximize the utilization of single wavelengths. In this paper we consider the case of using a single wavelength where the contention of SM traffic is solved by queue management techniques of the output buffer. We propose two buffer management schemes: the dedicated output buffer per input and the length-aware buffering (LAB) scheme where the LATW scheduler is investigated in relation to these schemes and a novel scheduling technique is proposed. We extend the first scheme to obtain results for different number of inputs. Thus, we evaluate how the node degree influences the system performance. Furthermore, we analyze the SM traffic performance for the two cases of GST traffic arriving as single packets, and in bursts of fixed size.
on a wavelength is smaller than a maximum packet size. The control module reserves the appropriate output of the OCS; even in the case when there is a short SM packet which could be scheduled in the gap and should be able to finish before the GST packet arrives. The RIB effect is shown to decrease when using the length-aware time-window technique (LATW) [5], as illustrated in fig. 2a.
The paper is organized as follows. In section II we give an overview of the reservation techniques employed for integrated hybrid networks, and explain how LATW works in combination with electronic buffering. In section III we present the simulation scenarios. A discussion of our main findings is given for each scenario. In section IV we draw our conclusions. II.
RESERVATION MECHANISMS
At each output of the OXC, the GST packet experiences a fixed delay by passing through a fiber delay line (FDL) of fixed length. This delay corresponds to the service time ∆ of the maximum SM packet size. It is used as a reservation mechanism to allow the transmission of an SM packet without preemption in case that a GST packet arrives at the output when an SM packet has begun transmission. The use of the FDL increases the delay of the GST traffic through the network. However, these proactive time-window reservation techniques are shown to achieve lower packet loss ratios for the SM traffic than reactive reservation schemes [5]. The reason is that the reactive reservation schemes rely on a preemptive priority for GST traffic and that the SM packets then will suffer from preemption induced blocking.
Figure 2. The scheduling mechanisms in the optical and electronic domains.
The simple time-window (STW) reservation mechanism presented in [5] is the simplest to implement in an OPS because it does not require the information on the length of the SM packet. The only information needed by the control module is to know if a GST packet is due to be scheduled within the time of arrival of the SM packet Ta until the end of the time window ∆, Ta+∆. The SM packets are scheduled only when there is no GST traffic sensed during a full time-window because there is no information of the service time of this SM packet. The main drawback is that this causes a Reservation Induced Blocking (RIB) which decreases the channel utilization. It is an effect of not using the channel when the time gap between GST packets
In the LATW technique, the control module uses the time window to obtain the information on all available gaps at each output wavelength during a full ∆ period. In fig.2a we illustrate this scheduling algorithm for only one output wavelength while employing OPS for contention resolution between the SM packets. In case the packet at the output of the OPS has a shorter service time than the available time until the GST packet arrives at the output wavelength, then it will be scheduled and will be able to exit before the GST arrival. The void-filling scheduling techniques [6] are known for OPS with FDL. However, the packet loss probability in OpMiGua is not only influenced by the Contention Induced Blocking (CIB) but
also the RIB. Fig. 2b shows the case when electronic buffers are used for the SM traffic. The packet loss probability of the SM packets does not directly depend on the available gap at the time of arrival as in [5], e.g. packet SM2 will be rescheduled at a later available gap. The control module monitors the time gap between the GST packets and the service time of the SM packets at the head of the queues of the electronic buffer. If there is enough time to schedule the SM packet within the known gaps of a time-window period, the utilization of the channel resources will inherently increase. In this paper we analyze the performance of the LATW technique for electronic buffers. Because electronic buffers are able to store packets for a relatively long time period, the average queuing delay becomes an important performance parameter for the SM traffic. We also perform a PLR evaluation of the different buffering schemes finding and comparing the different system loads for which the buffer overflows. III.
SIMULATED SCENARIOS AND DISCUSSIONS
A discrete event simulation program is used to quantify the performance of different reservation schemes. The program is implemented in SIMULA programming language using the DEMOS library [7]. The main parameters of the simulation scenarios are listed in table 1. The values in brackets show possible values for different scenarios. The SM packets are generated according to a Poisson arrival process from F input sources. The destination output link of these packets is randomly distributed among F single wavelength output links. This traffic model allows the evaluation of an OpMiGua node which inserts and drops SM traffic either in the core or edge part of the network. The electronic buffer of each output link is set to a fixed value, allowing 200 ms of packet buffering. Thus we focus on the relative performance of different schemes and not the buffer size. The packet length of the SM traffic follows an empirical distribution from Internet traffic measurements [8]. The packets are scheduled in a first-come first-served (FCFS) manner from the electronic buffers in all scenarios. The GST traffic is generated by one source per output channel with exponentially distributed interarrival times between packets or bursts. This model is suitable for the connection-oriented nature of the GST traffic with dedicated output wavelengths. In the case of GST bursts, we consider bursts to be of fixed size. The simulation results were obtained running ten independent replications to establish 95% confidence intervals which are also plotted in the graphs. For the system load values where the SM buffer overflows, each simulation run consists of 109 successfully scheduled SM packets which updated delay statistics after exiting the FCFS buffer. TABLE I. Component/Quantity
Bitrate per input
SIMULATION PARAMETERS Parameter
Rate
Fibre inputs
F
Number of Queues per buffer
Q
Value
Unit
10
[Gbps]
[1,4, 10, 16, 32, 64] [1, 4, 10, 16, 32, 64]
Parameter
Value
Unit
Normalized system load
Component/Quantity
A
0.6≤A≤0.9
Erlang
GST share of system load
S
10≤S≤90
[%]
δ
Empirical [40-1500]
[bytes]
SMMean
736
[bytes]
1.2
[µs]
SM packet length SM average packet length Time window
∆
GST burst length
B· SMMean
GST packet length
GSTPacket
[10, 50, 100, 1000]· SMMean Empirical [40-1500]
[bytes] [bytes]
A. Bursts vs packets In this scenario we considered how the GST packet length distribution affects the queuing delay and buffer overflow probability of the SM traffic. Fig.3 shows the results for the average delay experienced by the SM traffic when the GST share of the total load is set to S=50% and the number of output queues is fixed to Q=1. Previous results on all-optical OpMiGua nodes with STW reservation techniques have shown that the SM PLR decreases significantly with the increase of the burst length. In this paper we analyze how this factor influences the average delay, specifically for the LATW reservation scheme.
Figure 3. SM average delay (µs) as a function of the total system load for different sizes of fixed sized GST bursts and GST packets with empirical length distribution. GST burst size varies from 10 to 1000 mean packet size.
Intuitively, the delay induced from the service times of GST is proportional to the size of the burst, i.e. the delay in case of a burst of B=1000 is 10 times the delay for B=100. This is approximately equal with the simulation results when the size of GST bursts is higher than B=10 and is maintained throughout all the normalized system loads. However, the results show that even though the service time of the GST bursts is the dominating effect at low loads, RIB is still affecting the SM queuing delay. E.g. for the point at load A=0.6, the burst size B=10 achieves better results than all other lengths; it is 25% better than GST packets but only 6.3 and 59.2 times better than respectively B=100 and B=1000. Thus we might deduce that the main difference between GST packets and small bursts is the effect of channel fragmentation. Furthermore, we observe that aggregating GST traffic in bursts
As the total load increases it is observed that the SM average delay curve for the case of single GST packets crosses the curves of GST burst sizes of B=50, B=100 and B=1000. At those points the GST traffic load is the same for the intersecting curves while its service times are proportional to B, i.e. = ∙ . Accordingly, to achieve the same load as of GST packets has for GST bursts, the arrival rate increased proportionally to B, so that = ∙ . Let us consider the system as an M/G/1 queue with non-preemptive priorities [9] to obtain approximate results on the influence of the burst service times. The average waiting time in queue of SM packets for the cases of GST packets and bursts can be expressed by their respective residual service times. The total residual service time of the system in case of GST packets is 2 because both traffic classes have the same packet length distribution; while for the case of GST bursts, = + ∙ because the residual service time of bursts is approximately B times higher. Using Little’s formula for the number of packets accumulated in queue during the residual service time, we find the simplified relation between the numbers of packets in queue for the two cases to be ⁄ = 2⁄(1 + ). When comparing bursts and packets at low loads, this can be called the GST traffic fragmentation factor. We observe that at higher loads the RIB is the most prominent influence as the available gaps for SM packets become shorter. Hence, the LATW scheduling technique is especially important for cases of large GST packet traffic in order to increase the channel utilization and make use of gaps shorter than the timewindow. Furthermore, we notice that the buffer overflows at moderate loads when the GST traffic has the same packet length distribution as the SM traffic. The main reason is that the RIB effect increases when the GST traffic load consists of single packets. Thus this motivates the investigation of queue management schemes for this type of GST traffic. B. The influence of the GST load share on PLR and delay In fig.4 and fig. 5 we present the simulation results for different shares of the GST traffic. The output buffer is organized as one common queue for all SM packets destined to this output. The average delay is relatively low for moderate loads and varies between 1µs to 45µs for different GST shares. Table II shows the system load values for which the SM buffer overflows as a function of different GST load shares. TABLE II.
APPROXIMATE OVERFLOW LOAD VALUES
GST Share S
Normalized System load
SM relative load
30%
0.875
0.61
50%
0.775
0.38
70%
0.725
0.21
90%
0.7
0.07
The effect of the GST share [2] [5] is less significant at low loads, i.e. the difference in delays is relatively proportional to the difference in shares. E.g. at load A=0.6, the delay for
S=30% is 1.9 times the delay for S=10%, the delay for S=50% is 1.8 times the delay for S=30%. However, the share significantly influences the points of the system load for which the buffer overflows. For example, for shares S=90% and S=30%, the buffer overflows at SM load of 0.07 and 0.61 respectively. Hence we observe that the most important influence among RIB and CIB for high GST shares is the RIB; even though the SM load is lower, the average length of the gaps decreases and the RIB builds up the number of packets in the buffer. If we consider the unused gaps to be another stream of guaranteed traffic, their share of the total link capacity decreases the available capacity left for SM. At the buffer overflow load values, the arrival rate of SM traffic has exceeded the capacity left over by GST and the gaps, thus the system is overloaded. The distribution of the length of the gaps is dependent on the load of the system and GST share. Therefore, the system overloads at different load values for different GST shares. Q=1, GST packets 1e+07 1e+06
Average SM delay (us)
beyond B=10 is not efficient for the SM traffic as the delay tends to be proportional to the service times of GST.
S=10% S=30% S=50% S=70% S=90%
100000 10000 1000 100 10 1 0.6
0.65
0.7
0.75
0.8
0.85
0.9
Normalized system load
Figure 4. SM average queuing delay (µs) as a function of the normalized system load for streams of single GST packets.
Previous studies in all-optical OpMiGua nodes have shown that for moderate system loads, high GST shares give lower PLR for the SM traffic [2], [5]. This is explained by the fact that the arrival rate of SM packets is lower which reduces the CIB. In addition, RIB is small for moderate system loads. However as shown in fig. 5, this GST share effect becomes less significant when using electronic buffers. The CIB is solved by buffering the packets, but the RIB influence cannot be solved only by buffering since the system is in overload as we previously discussed. The number of SM packets is lower for higher shares but the channel fragmentation is higher as well. Thus, when the buffer overflows, the packet loss ratio at high shares is greater than low shares, and increases consistently with the increase in the system load. However, it is clearly observed that the gain in performance when using electronic buffers is high for relatively low GST shares. In the case when S=90% we can see that after the buffer overflows, the Optical OpMiGua node performs better than the electronic counterpart for loads lower than 0.8. This is explained by the influence of the Head-of-Line (HoL) blocking that the FCFS scheduler adds to the buffer overflow. This indicates that the buffer management mechanisms may be investigated for using either proactive or reactive schemes. In this work we have however
focused on passive queue management, the scalability of the node and the length-aware buffering (LAB) technique shown in the next scenarios. Q=1, GST packets 1 0.9
SM packet loss ratio
0.8 0.7 0.6
HoL blocking probability decreases. This reduction is however not proportional to Q. The largest increase is found when increasing from Q=1 to Q=4, and the increase is less significant for Q values above 4 up to 64. For loads A=0.825, the buffer overflows for all values of Q. The performance differences between the different sizes of Q remain however approximately constant for high loads. For example, at load A=0.9 when moving from Q=1 to Q=4 and Q=64 the PLR drops respectively with 10.7% and 17%. Furthermore, we observe that after Q=16, the gain in system performance is small.
0.5 0.4 S=10% S=30% S=50% S=70% S=90% Optical S=50% Optical S=90%
0.3 0.2 0.1 0 0.6
0.65
0.7
0.75
0.8
0.85
0.9
Normalized system load
Figure 5. SM packet loss ratio as a function of the total system load using electronic buffer is compared with a bufferless all optical approach.
C. Dedicated output queue with RRGF scheduler An important factor that influences the buffer overflow probability, and the delay of SM traffic, is HoL blocking. The packets are served in a FCFS manner. Thus, in our context, the HoL blocking is observed at the output buffer. As the load increases, the probability to fit a long SM packet from the head of the queue in a gap decreases. The shorter packets are penalized and the channel utilization decreases. This blocking may be solved by actively dropping packets e.g. from the head of the queue, preventing overflow of the buffer. In this paper we focus on reducing the RIB effect on the buffer overflow probability, leaving the study of the CIB effect to future work. In this section we propose using a buffer management scheme logically dividing the output buffer into F identical queues, one for each input channel. This way the OpMiGua node can avoid reordering of packets within one SM input stream. The total sum of queues sizes is equal to the output buffer size applied in the reference scenario. A novel scheduling technique Round-Robin Gap Fit (RRGF) is proposed serving the queues in a round-robin manner scheduling the first packet that fits in the gap. The GST share is fixed to S=50% and we observe how the number of queues, meaning the number of inputs of the node, influences the system performance. For a node with F input links we consider that there are Q=F queues at the output buffer as in a dedicated queue per input scheme. Intuitively, increasing the node degree of an OpMiGua node increases Q and thus increases the probability of finding a packet at the head of a queue that fits the gap. At low loads, the gain in performance from Q=1 to Q=64 in terms of average SM queuing delay is in the range of 1µs. Hence, the number of separate queues does not have a noticeable impact on the average queuing delay for moderate system loads. The influence of Q is observed for the load values at which the buffer overflows; the PLR presented in fig. 6 shows that for Q= 64, the overflow load is shifted with 5% compared to Q=1. This is a result of decreasing the PLR RIB effect because the
Figure 6. SM packet loss ratio as a function of the total system load for different number of queues Q. GST traffic share = 50 %.
D. Length-aware buffering In this scenario the SM packets are buffered based on their length and scheduled by the RRGF scheduler. The buffer at the output port is divided into four queues. Each of the queues has a different size that reflects the probability of the empirical distribution of the packets’ lengths that it accommodates. The total buffer size of the different queues is as in the reference scenario. Fig. 7a shows the SM packets’ loss ratio for different GST shares of the system load. As previously discussed, the GST share S influences system load at which the buffer overflows. Comparing the Length-Aware Buffering (LAB) technique with the other buffering techniques, e.g. shown in fig.5, for all GST shares, the load values at which the buffer overflows are approximately the same. At these overflow loads, the PLR is less than 1% of the PLR for the unmanaged buffer scheme. For higher loads, we observe that the PLR for all GST-shares is less than 55% of the PLR of the unmanaged buffer scheme. The most important factor is that the HoL blocking for the short packets is greatly reduced. However, it introduces packet reordering and unfairness to the long packets, which in our empirical distribution are 36% of all SM traffic. We quantify the unfairness issue: In fig. 7b we have plotted the PLR for the different packet lengths when S=90%. In fig. 7b, e.g. the curve for S=90% and load A=0.8 shows that the average SM PLR is 33%. Inherently, these are the long packets of 1500B which at this load are dropped with a probability of 95%. Thus, the gaps between GST packets have with a probability of 95% a shorter length than a maximum packet size. After this load value, the increase in the system load starts influencing the packets of the
second order of length and so on. We observe that when the load is 0.9, only the short packets of less than 576B have no losses and the long packets are dropped by the node. a) Length-aware buffering 0.5
SM packet loss ratio
0.4
S=10% S=30% S=50% S=70% S=90%
Furthermore, we considered the GST traffic stream consisting of single packets with empirical length distribution. For high GST shares, the available gaps with lengths suitable to fit an SM packet are small and the fragmentation of the available bandwidth increases. This reduces the leftover capacity that can be used by SM traffic. Hence, the maximum rate of SM traffic inserted in the OpMiGua network should be limited as a function of GST load share and its packet length distribution.
0.3
0.2
0.1
0 0.6
0.65
0.7
0.75
0.8
0.85
0.9
0.85
0.9
Normalized system load b) S=90%, Length-aware buffering 1
[40-576]Byte [576-1300]Byte [1300-1400]Byte [1400-1500]Byte Average
SM packet loss ratio
0.8
in the system and its packet length distribution. We showed that when GST traffic is aggregated in bursts, the buffer overflow probability at loads lower than 0.9 is non-existent. The results indicate however that aggregating GST traffic in bursts beyond B=10 is a trade off with the SM queuing delay, because it tends to be proportional to the service times of GST.
0.6
0.4
0.2
In addition, we proposed two schemes of managing the output buffer in combination with the scheduling mechanism, the dedicated queue per input and the length-aware buffering. The first scheme gives more than 10% better results than a single output queue for the SM PLR. Thus, the gain in performance increases with the nodal degree of the OpMiGua node. We found that the largest improvement is found for Q=4 and that the optimal number of queues per output buffer is 16. Beyond 16, the improvement in performance is small. Furthermore, the length-aware buffering scheme performs 55% better in terms of SM PLR but it comes with the cost of packet reordering and discriminating long packets, especially at high shares of GST traffic. We showed that the buffer management schemes together with LATW scheduling increase the utilization of the available capacity and pose less strict limit on this maximum rate of SM traffic inserted in the OpMiGua network. REFERENCES
0 0.6
0.65
0.7
0.75
0.8
Normalized system load
Figure 7. Length aware buffering with four queues. a) SM packet loss ratio as a function of the normalized system load for different GST shares, b) different PLR for the different queues when the GST share is fixed to S=90%.
As shown in fig 7a, the LAB scheme is more effective for moderate GST shares and high system loads. It is interesting to note that LAB quantifies the reservation induced blocking of the LATW technique. For example, based on the PLR of different packet lengths, we can recognize the probability distribution of the gaps length. In addition, it is observed that the SM traffic packet length pattern accepted in the network depends on the GST load of the system. IV.
CONCLUSIONS
In this paper we have proposed a novel scheduling technique RRGF that increases the SM performance in an OpMiGua node. The RRGF scheduler monitors the gaps between GST packets and finds in the electronic buffer with multiple FCFS queues an SM packet of suitable size that fits in the gap. Blocking probability and average queuing delay of low priority SM traffic is shown. We found that the main factor influencing the SM performance is the load of the GST traffic
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