Maximizing throughput in multi-queue switches

Azar, Yossi; Litichevskey, Arik

doi:10.1007/s00453-005-1190-x

Cited by 57 publications

(25 citation statements)

References 16 publications

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“…Albers and Schmidt [3] proposed a deterministic 1.89-competitive algorithm for the case of unit-value packets. Azar and Litichevskey [5] derived a 1.58-competitive algorithm for this special case with large buffers. Albers and Jacobs [2] gave an experimental study of new and known online packet buffering algorithms.…”

Section: Our Resultsmentioning

confidence: 99%

Improved Competitive Performance Bounds for CIOQ Switches

2011

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Combined Input and Output Queued (CIOQ) architectures with a moderate fabric speedup S > 1 have come to play a major role in the design of high performance switches. In this paper we study CIOQ switches with First-In-First-Out (FIFO) buffers providing Quality of Service (QoS) guarantees. The goal of the switch policy is to maximize the total value of packets sent out of the switch. We analyze the performance of a switch policy by means of competitive analysis, where a uniform worst-case performance guarantee is provided for all traffic patterns. Azar and Richter (ACM Trans. Algorithms 2(2): [282][283][284][285][286][287][288][289][290][291][292][293][294][295] 2006) proposed the β-P G algorithm (Preemptive Greedy with a preemption factor of β) that is 8-competitive for an arbitrary speedup value when β = 3. We improve upon their result by showing that this algorithm achieves a competitive ratio of 7.5 and 7.47 for β = 3 and β = 2.8, respectively. Basically, we demonstrate that β-P G is at most β 2 +2β β−1 and at least β 2 β−1 -competitive.

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Section: Our Resultsmentioning

confidence: 99%

Improved Competitive Performance Bounds for CIOQ Switches

2011

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“…The proposed Semi Greedy algorithm deviates from standard Greedy when the buffer occupancy is low and has a competitive performance of 17/9 ≈ 1.89. The deterministic strategy with the smallest competitive ratio known is the Waterlevel algorithm [6] with a competitiveness of e e−1 (1…”

Section: Known Algorithmsmentioning

confidence: 99%

“…In the original paper [6] the description was more general. Waterlevel is quite involved and consists of a cascade of four algorithms that simulate each other.…”

Section: Deterministic Online Algorithmsmentioning

confidence: 99%

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An Experimental Study of New and Known Online Packet Buffering Algorithms

Albers

Jacobs

2008

Algorithmica

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We present the first experimental study of online packet buffering algorithms for network switches. We consider a basic scenario in which m queues of size B have to be maintained so as to maximize the packet throughput. For this model various online algorithms with competitive factors ranging between 2 and 1.5 were developed in the literature. We first develop a new 2-competitive online algorithm, called HSFOD, which is especially designed to perform well under real-world conditions. In our experimental study we have implemented all the proposed algorithms, including HSFOD, and tested them on packet traces from benchmark libraries. We have evaluated the experimentally observed competitiveness, the running times, memory requirements and actual packet throughput of the strategies. The tests were executed for varying values of m and B as well as varying switch speeds. It shows that greedylike strategies and HSFOD perform best in practice.

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“…The unit-value case, that characterized IP networks, is interesting by itself. Following our paper, deterministic algorithms with competitive ratios strictly lower than 2 (for buffer size B ≥ 2) have been suggested by Albers and Schmidt [2] and by Azar and Litichevskey [4]. Improved lower bounds were also given in [2].…”

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confidence: 90%

Management of Multi-Queue Switches in QoS Networks

Azar

Richter

2005

Algorithmica

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The concept of Quality of Service (QoS) networks has gained growing attention recently, as the traffic volume in the Internet constantly increases, and QoS guarantees are essential to ensure proper operation of most communication-based applications. A QoS switch serves m incoming queues by transmitting packets arriving to these queues through one output port, one packet per time step. Each packet is marked with a value indicating its priority in the network. Since the queues have bounded capacities and the rate of arriving packets can be much higher than the transmission rate, packets can be lost due to insufficient queue space. The goal is to maximize the total value of transmitted packets. This problem encapsulates two dependent questions: buffer management, namely which packets to admit into the queues, and scheduling, i.e. which queue to use for transmission in each time step. We use competitive analysis to study online switch performance in QoSbased networks. Specifically, we provide a novel generic technique that decouples the buffer management and scheduling problems. Our technique transforms any single-queue buffer management policy (preemptive or non-preemptive) to a scheduling and buffer management algorithm for our general m queues model, whose competitive ratio is at most twice the competitive ratio of the given buffer management policy. We use our technique to derive concrete algorithms for the general preemptive and non-preemptive cases, as well as for the interesting special cases of the 2-value model and the unit-value model. We also provide a 1.58-competitive randomized algorithm for the unit-value case. This case is interesting by itself since most current networks (e.g. IP networks) do not yet incorporate full QoS capabilities, and treat all packets equally.

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Maximizing throughput in multi-queue switches

Cited by 57 publications

References 16 publications

Improved Competitive Performance Bounds for CIOQ Switches

Improved Competitive Performance Bounds for CIOQ Switches

An Experimental Study of New and Known Online Packet Buffering Algorithms

Management of Multi-Queue Switches in QoS Networks

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