Performance Comparison of VOQ Selection Policies in Scalable Packet Switches

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1 Performance Comparison of VOQ Selection Policies in Scalable Packet Switches Boran Gazi, Student Member, IEEE and Zabih Ghassemlooy, Fellow of IEE, S. Member, IEEE Abstract Virtual output queueing is known to overcome the head of line blocking problem of input queueing. This type of buffering is widely used in ATM networks. In order to deliver desired performance, virtual output queueing requires efficient and effective scheduling algorithm with low operating complexity. For large scale switches this might be difficult to achieve, as algorithm complexity increases together with the size of the switch fabric. It is possible to resolve this problem by using interconnection network architectures with distributed buffers. In this case, each network node is a switching element that employs virtual output queueing and dedicated selection policy that operates locally. Thus, large scale switches can be achieved without the expense of complex scheduling algorithms. In this paper, performance characteristics of the longest queue first, oldest cell first and random selection policies in the Banyan like interconnection network are studied. Results show that the longest queue first selection policy outperforms others in terms of packet loss performance, whereas random selection policy achieves low throughput-delay ratio performance. Index Terms Virtual output queueing, interconnection networks, buffering. (HOL) blocking problem which limits throughput to 58.6% when N [1]. Even if N, the switch throughput is 75%. For this reason this type of buffering is not favorable. A number of techniques, such as windowing, dropping HOL packets, output expansion, switch speed-up, input port expansion, and virtual output queueing, have been proposed to address this problem. Output queueing achieves the optimal throughput-delay ratio, but switch fabric speed must be N times the line speed to avoid backlog of input requests [1]. Shared buffering achieves similar performance as output queueing with a reduced amount of buffering requirements []. In shared buffering memory read/write speed must be N times the line speed. Recirculation buffering is similar to shared buffering where buffer module is shared among all output ports. Packets are stored for one time-slot duration and are fed back to the switch until contentions are resolved. One drawback of this architecture is that a heavily loaded output port could affect other ports []. Furthermore, this architecture does not scale well to large switch sizes. I. INTRODUCTION Typical multistage interconnection network (MIN) architecture interconnects N inputs to N outputs through multiple stages of switching elements (SEs). There are mainly two types of MIN architectures, namely internally nonblocking and internally blocking. In the former there is more than one path that connects inputs to output ports. Hence, packet losses due to contentions in the SEs can be avoided by using diversity feature of the interconnect architecture. In internally blocking case, two or more packets from different inputs cannot always be transferred to the outputs of the MIN simultaneously. Resource sharing is required at some stage of the interconnection network in SEs. Therefore, only one of the packets is allowed to use the SE output port and the rest of the packets are either dropped or stored in SE buffers. Buffer arrangement and management play a major role in the overall performance of MIN. Buffered packet switches can be classified according to the positions of the buffering modules. The most common buffer organizations are input queueing, output queueing, recirculation buffering, and shared buffering [1]-[3]. Input queueing suffers from the head of line Authors are with the Optical Communications Research Group, School of Computing, Engineering, and Information Sciences, The University of Northumbria, Newcastle upon Tyne (borangazi@ieee.org and fary.ghassemlooy@unn.ac.uk). Fig. 1. Virtual output queueing Virtual output queueing (VOQ) was first proposed to overcome the HOL blocking problem of input queueing [4]. In this type of buffering, each input queue is further divided into N sub-queues, e.g. one FIFO queue for every output port (see Fig 1). Therefore, totally N queues are required in an N N non-blocking switch. Arriving packets are sorted into corresponding queues according to their destination port addresses. By this way, HOL blocking is eliminated, as a packet cannot be held up by another packet destined for another output port. Maximum number of fixed length packets (i.e. cells) per time slot is aimed to be delivered, but this can take a long time to compute and may result in starvation of some input flows [4]. For this reason, high-performance low complexity packet selection algorithm, such as islip [5], is

2 required to maximize the number of packets delivered in a large scale switch. The computation time of selection policies grow with the switch dimensions. For instance, the wellknown islip has an upper bound complexity of O(N.logN), where N is the switch size. For this reason the realisation of truly large scale switches is rather impractical. Note that large scale packet switches with this type of buffering can still be achieved by using the Banyan like MIN architecture (see Fig. ). In this case, each Banyan node is a VOQ SE and each VOQ node can be controlled locally by employing maximum weight matching (MWM) type selection policies, such as the longest queue first (LQF) and the oldest cell first (OCF) selection policies [6]. In this approach, scalable switch architectures can be achieved by employing potentially highperformance selection policies in a distributed manner. Even tough LQF and OCF have a complexity of O(N.logN); due to small node dimensions (i.e. ), the complexity of these policies are transparent. Besides, MWM type selection policies are known to achieve a relatively better input-output matching than islip in non-uniform traffic conditions [7]. such as islip, PIM, ilqf, iocf, have no advantage over their non-iterative counterparts. Hence, implementation of LQF and OCF selection policies become much simpler with the promise of delivery of high performance. In LQF, each output port selects a packet from the longest queue (e.g. number of packets). If both of the queues have the same length, one is selected randomly. In OCF, each output port selects an HOL packet with the longest waiting time; otherwise selected randomly. Note that waiting time is calculated by summing up the amount of time delay experienced by each packet that traverse from inputs to outputs of the MIN. When a packet is in a queue the delay counter is incremented based on the number of time-slots this particular packet waits until it is routed out. In every time-slot this information is updated and is then recorded into waiting time field in the packet header (as in TTL field in IPv6). This requires reading and updating packet header every time packets visit a node. As its name implies, random selection policy selects one of HOL packets in a random fashion with a 50% success rate. Nevertheless, this policy does not serve HOL packets in a similar way to Round Robin scheme. Fig. 3. VOQ node Fig Banyan network (butterfly permutation) This paper aims to demonstrate the performance characteristics of a large scale packet switch with VOQ node architectures that employ efficient but simpler control mechanisms, viz LQF, OCF and random selection, in a distributed fashion. Section II describes the VOQ architecture and LQF, OCF, and random selection policies. Simulation model and parameters evaluated are described in Section III. Simulation results and discussions based on our findings are presented in section IV. Drawn conclusions are provided in Section V. II. VOQ SELECTION POLICIES In VOQ, for each output port there is a separate fix-length queue at the input ports (see Fig. 3). Each incoming packet is first sorted into a queue according to its destination address. Meanwhile each output port selects a packet among queues destined for the same output port according to a selection policy. Due to small node dimensions iterative algorithms, Selection policies are local to each MIN node and no centralized control, such as backpressure [8][9], is used. This way of distributed control reduces the control complexity of a large scale switch. Banyan architecture uses a self-routing mechanism. Packets arriving to the input ports of the Banyan network are assigned with a binary array tag. In an N N network with n log N stages, n-bit address tag is attached to each packet header (address tag b 0, b 1..., b n-1 ). When a packet arrives at a SE in stage k, kth address bit is stripped off and the packet is routed accordingly. Routing decision can be formally represented as follows: bk 0, At stage k b k 1, upper lower port port Banyan network with butterfly permutation can be iteratively constructed by using the following function: β (k) (x n- 1x n- x k-1 x k- x 1 x 0 ) (x n-1 x n- x 0 x k- x 1 x k-1 ). By this way each Banyan node knows its preceding neighbors. (1)

3 III. SIMULATION METHOD Simulations are carried out using a C program. Bernoulli random number generator function is used to generate packet traffic. Packets are of fixed length and the network is synchronous (i.e. slotted). At most one packet is generated in a given time-slot. Hence, given the traffic load p, this is formally represented as: F( x) x p.(1 p) 1 x, for x { 0,1} Duration of a single simulation is 50,000 cycles (timeslots). The values computed in first 500 cycles are ignored, as the network needs to reach a steady state to achieve realistic results. Simulations are performed using different network sizes, buffer depths and traffic patterns, namely uniform or non-uniform. In a uniform traffic pattern, it is equally likely for each output port to receive a packet. In non-uniform traffic, also known as the hotspot, one or more output ports may receive more traffic each with a given hotspot probability H p. In our simulations we considered a single hotspot port case. When the network reaches a steady state allocation (t 1 500), packets that reach the output ports of the Banyan network are counted at the end of each cycle η i. These are averaged over the number of output ports and cycles (t 50,000). Therefore, the normalized throughput τ is given as: t 1 N 1 j t1 i 0 t ηi t 1 j N () τ (3) Where N denotes the number of Banyan inputs/outputs, η i denotes whether a packet is received at output port i in cycle j, η i {0,1}. Packet loss rate is measured as the ratio of the number of packets lost to the total number of packets injected into the network between t 1 and t. The average packet delay is calculated by averaging the delay experienced by each packet d throughout the network over the number of packets that reach the output ports. Each packet header stores the number of time slots delay experienced by the packet. This is incremented only when a packet is waiting in a queue in a node. Traversing from one node to another is not considered as delay and therefore the delay counter is not updated. Only those packets that reach the output ports are taken into consideration and dropped ones are not included. Hence the average packet delay δ is simply given by: t 1 N 1 d i k j j t1 i 0 δ (4) t t 1 Where d i is the total delay experienced by a packet received at output port i in cycle j, and k j is number of packets received in cycle j. The throughput-delay ratio criterion determines achieving desired throughput while keeping the average packet delay low. Namely, high throughput-delay ratio is a favorable performance for a buffered system, where low delay and high throughput is an important requirement. IV. RESULTS AND DISCUSSIONS Simulation results for the packet loss rate and packet throughput-delay ratio for varying buffer depths under uniform input traffic are illustrated in Figs. 4 and 5 for the three selection policies. In this setup, it is assumed that network size is and the traffic load is 1.0. Considering there are four FIFO queues per node (i.e. ) and there are N log N nodes in Banyan network, the total buffer size is given as M N m log N, where m is the depth of a single first-in-first-out (FIFO) queue in Fig. 3. Values represented in buffer depth dimension of Figs. 4 and 5 are the sizes of single queue in each VOQ node (i.e. m). As shown in figures, LQF achieves low packet loss, but suffers from high delays for all values of buffer depth. Random selection policy achieves the worst loss performance with a better throughputdelay performance. In the case of random selection some queues are starved, and consequently packets are dropped from the end of congested queues whilst smaller queues are served more frequently. For this reason the overall delay is relatively small. Ideally, random selection policy must provide similar loss performance as LQF as it maintains the balance of the queues (with 50% load balance). However, random selection is somewhat biased, i.e. one queue might be served over a long period of time whilst the other one is starved resulting in losses from the tail of congested queues. OCF policy is somewhat trade-off between LQF and random selection policies. Packet loss rate and throughput-delay ratio for varying Banyan network sizes are shown in Figs. 6 and 7. It is assumed that each VOQ node can accommodate up to 8 packets and traffic load is 1.0. Network size dimension is given as the number network stages. As the network size increases (e.g. large scale switch), LQF selection policy outperforms other schemes in terms of packet loss rate. LQF policy tries to serve lengthy queues to free up buffer space for incoming packets. In this way, lengthy queues are prevented from being congested and buffer space is used effectively. OCF policy only considers the HOL packets. For this reason this policy does not have the overall view of the entire node. Packets arriving from shorter length queues might be served more frequently than those arriving from longer length queues. In case of random selection, each queue is served equally, but some queues may grow faster the other even tough the input traffic is uniformly distributed. For very large network sizes, the differences between the three policies become negligible. This makes the LQF policy more favorable among the schemes discussed in this paper.

4 Fig. 4. Packet loss rate versus the buffer depth Fig. 7. Throughput-delay ratio versus the network size Fig. 5. Throughput-delay ratio versus the buffer depth Fig. 8. Packet loss rate versus hotspot probability Fig. 6. Packet loss rate versus the network size Fig. 9. Throughput-delay ratio versus hotspot probability

5 Finally, the effects of hotspot load on packet loss rate and packet throughput-delay ratio for a single output port are shown in Figs. 8 and 9. In this case, the total buffer size per VOQ node is 8 packets, network size is 64 64, and the traffic load is 1.0. As shown in Fig. 8, packet loss rate displays linear characteristics for all three selection polices, with very small differences at low hotspot probabilities. For hotspot probabilities greater than 0.35, all policies behave in a similar manner due to tree-saturation effect. In such a case, buffers on paths of the hotspot port, i.e. tree-saturation path, are populated whereas the rest of the nodes are not fully used. Buffers on the saturated path cannot accommodate high volume of packets destined to the hotspot port, thus resulting in high loss rates. For high values of hotspot probabilities, most of the traffic is destined to the hotspot port. Thus, the throughput performance is degraded significantly regardless of the type of selection policy employed, see Fig. 9. V. CONCLUDING REMARKS VOQ requires high-performance and low complexity scheduling algorithm to offer quality performance by effectively matching inputs and outputs of a switch. Iterative matching algorithms operate at low complexity levels, e.g. O(N.logN), but require sophisticated algorithms to deliver good performance. These algorithms do not scale well for large switch sizes. In this study, we showed that large switch sizes can be achieved by using the Banyan like interconnection networks and a VOQ switching element that could be employed as buffered node architectures. In VOQ switching element the complexity of selection policies are transparent and therefore sophisticated selection policies are not needed. Besides, each node is controlled locally. In this paper, we carried out a simulation study to demonstrate the performance characteristics of three selection policies, namely LQF, OCF and random selection. It was concluded that LQF offers an improved loss performance than other policies and is more desirable for network environments where loss performance as well as the scalability of the switch are of prime importance. For large network sizes, the packet loss performance of LQF is considerably low when compared with OCF and random selection policies. This is because LQF makes better use of available buffer space by simply serving the longest queue first. It was shown that the random selection policy offering a reasonable throughput-delay ratio performance is more suitable for networks with relatively low delays. However, this policy does not scale well in terms of loss performance. Finally OCF policy is somewhat trade-off between LQF and random selection policies. Further research include: (i) Determining the buffer memory requirements of proposed switch architecture, and (ii) matching the performance of traditional N N VOQ switch architecture as opposed to relatively complex scheduling algorithms. REFERENCES [1] M. Karol, M. Hluchyj, and S. Morgan, Input versus Output Queueing on a Space-Division Packet Switch, IEEE Transactions on Communications, vol. 35, no. 1, pp , December [] M. G. Hluchyj, and M. J. Karol, Queueing in High- Performance Packet Switching, IEEE Journal on Selected Areas of Communications, vol. 6, no. 9, pp , [3] D. K. Hunter, M. C. Chia, and I. Andonovic, Buffering in Optical Packet Switches, Journal of Lightwave Technology, vol. 16, no. 1, pp , December [4] N. McKeown, and T. E. Anderson, A Quantitative Comparison of Iterative Scheduling Algorithms for Input- Queued Switches, Computer Networks, vol. 30, no. 4, pp , [5] N. McKeown, The islip Scheduling Algorithm for Input-queued Switches, IEEE/ACM Transactions on Networking (TON), vol. 7, no., pp , April [6] N. McKeown, V. Anantharam, and J. Walrand, Achieving 100% Throughput in an Input-Queued Switch, IEEE Transactions on Communications, vol. 47, no. 8, pp , [7] R. Schoenen, G. Post, and G. Sander, Weighted Arbitration Algorithms with Priorities for Input-Queued Switches with 100% Throughput, 3rd IEEE International Workshop on Broadband Switching Systems (BSS'99), Kingston, Ontario, Canada, June [8] D. Basak A. K. Choudhury, E. L. Hahne, Sharing Memory in Banyan-Based ATM Switches, IEEE Journal on Selected Areas in Communications, vol. 15, no. 5, pp , [9] B. Zhou and M. Atiquzzaman, A Performance Comparison of Four Buffering Schemes for Multistage Interconnection Networks, International Journal of Parallel and Distributed Systems and Networks, vol. 5, no. 1, pp. 17-5, 00. [10] R. B. Magill, C. E. Rohrs, R. L. Stevenson, Outputqueued switch emulation by fabrics with limited memory, IEEE Journal on Selected Areas in Communications, vol. 1, no. 4, pp , May 003. [11] S. -T. Chuang, S. Iyer, and N. McKeown, Practical Algorithms for Performance Guarantees in Buffered Crossbars, Proceedings of IEEE INFOCOM 005, Miami, Florida, pp , March 005.