Virtual output queueing

Virtual output queueing (VOQ) is the technique used in input-queued switches where rather than keeping all traffic in a single queue, separate queues are maintained for each possible output location. It addresses a common problem known as head-of-line blocking.^[1]

Description

In VOQ each input port maintains a separate queue for each output port. It has been shown that VOQ can achieve 100% throughput performance with an effective scheduling algorithm. This scheduling algorithm should be able to provide a high speed mapping of packets from inputs to outputs on a cycle-to-cycle basis. The VOQ mechanism provides throughput at a much higher rate than the crossbar switches without it.

There are many algorithms for design and implementation of fast VOQ. Nick McKeown and a group at Stanford University for example published a design in 1997.^[2]

Quality of service and priority are extensions found in literature of the same time.^[3]

VOQ scheduling is often referred to as "arbitration" (resolving the concurrent access wishes), whereas the ordering of packets ("packet scheduling") is an additional task^[4] following the VOQ arbitration.

Example

For example, consider a 2×2 crossbar switch.

     --------
a--->|-\--/-|--->--0
     |  \/  |
     |  /\  |
b--->| /--\ |---->-1
     --------

Let's say that data "0" arriving at port A or B will go to output port 0. Similarly data "1" arriving at port A or B will go to output port 1. So the number of combinations that can happen at the input port A, B are: 00, 01, 10, and 11.

If data at the input is "00", this means both the input data at time t are contending for the same output port 0 and the output port 0 can't route both the data at the same time as it can handle only one unit data per time slot. So in this case the efficiency of the 2×2 switch (without VOQ) is 0.5.

Same is the case for data "11" in which the efficiency is 0.5. Similarly for data "01" and "10" the efficiency is 1 as there is no contention as both the data go to both output ports 0 and 1.

Since it is a 2×2 switch, the probability that any one of out of these four combinations of data will occur = 0.25. So the efficiency of this 2×2 switch is:

  (0.25 * 0.5) --> for data 00
+ (0.25 * 0.5) --> for data 11
+ (0.25 * 1.0) --> for data 01
+ (0.25 * 1.0) --> for data 10
---------------
 = 0.75 (75%)

So we see that the 2×2 crossbar switch is working at 75% efficiency to give out data from input to output. As n increases, for n×n switches this causes further degradation in efficiency. VOQ overcomes this problem by introducing extra buffers (queues) per port.

Let's revisit the same scenario with 2×2 switch with VOQ support.

     --------
a--->|-\--/-|-OO-->--0
     |  \/  |
     |  /\  |
b--->| /--\ |-OO--->-1
     --------

Here each output port has n buffers per port to accommodate a given maximum number of simultaneous data packets that each port can receive at a time. This buffering mechanism removes the bottleneck per port on peak time and distributes it over a period of time increasing the switch performance.

References

↑ Goudreau, Mark W.; Kolliopoulos, Stavros G.; Rao, Satish B. (2000). "Scheduling algorithms for input-queued switches: Randomized techniques and experimental evaluation". Proceedings of IEEE INFOCOM. CiteSeerX 10.1.1.42.5126. doi:10.1109/INFCOM.2000.832562. ISBN 0-7803-5880-5.
↑ McKeown, Nick; Izzard, Martin; Mekkittikul, Adisak; Ellersick, Bill; Horowitz, Mark (1997). "Tiny Tera: a packet switch core" (PDF). IEEE Micro. 17: 26–33. doi:10.1109/40.566194.
↑ Schoenen, Rainer; Post, Guido; Sander, Gerald (1999). "Prioritized arbitration for input-queued switches with 100% throughput". Proceedings of ATM Workshop. doi:10.1109/ATM.1999.786865. ISBN 4-88552-164-5.
↑ Schoenen, Rainer; Hying, Roman (1999). "Distributed cell scheduling algorithms for virtual-output-queued switches". Proceedings of IEEE Globacom. doi:10.1109/GLOCOM.1999.829963. ISBN 0-7803-5796-5.

Queueing theory

Single queueing nodes	D/M/1 queue M/D/1 queue M/D/c queue M/M/1 queue Burke's theorem M/M/c queue M/M/∞ queue M/G/1 queue Pollaczek–Khinchine formula Matrix analytic method M/G/k queue G/M/1 queue G/G/1 queue Kingman's formula Lindley equation Fork–join queue Bulk queue

Arrival processes	Poisson process Markovian arrival process Rational arrival process

Queueing networks	Jackson network Traffic equations Gordon–Newell theorem Mean value analysis Buzen's algorithm Kelly network G-network BCMP network

Service policies	FIFO LIFO Processor sharing Round-robin Shortest job first Shortest remaining time

Key concepts	Continuous-time Markov chain Kendall's notation Little's law Product-form solution Balance equation Quasireversibility Flow-equivalent server method Arrival theorem Decomposition method Beneš method

Limit theorems	Fluid limit Mean field theory Heavy traffic approximation Reflected Brownian motion

Extensions	Fluid queue Layered queueing network Polling system Adversarial queueing network Loss network Retrial queue

Information systems	Data buffer Erlang (unit) Erlang distribution Flow control (data) Message queue Network congestion Network scheduler Pipeline (software) Quality of service Scheduling (computing) Teletraffic engineering

Category

This article is issued from Wikipedia - version of the 11/13/2016. The text is available under the Creative Commons Attribution/Share Alike but additional terms may apply for the media files.