Multiplexing Packets with Arbitrary Deadlines in Bounded Buffers

Azar, Yossi; Levy, Nir

doi:10.1007/11785293_4

Cited by 9 publications

(11 citation statements)

References 9 publications

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“…Azar and Levy consider a variant of the multi-input switch described in Section 4.2, in which there are individual packet deadlines and buffer capacities [AL06a]. The greedy policy is not constant competitive for this model.…”

Section: Bounded-delay Model With Maximum Buffer Requirementmentioning

confidence: 99%

A survey of buffer management policies for packet switches

Goldwasser

2010

SIGACT News

109

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Over the past decade, there has been great interest in the study of buffer management policies in the context of packet transmission for network switches. In a typical model, a switch receives packets on one or more input ports, with each packet having a designated output port through which it should be transmitted. An online policy must consider bandwidth limits on the rate of transmission, memory constraints impacting the buffering of packets within a switch, and variations in packet properties used to differentiate quality of service. With so many constraints, a switch may not be able to deliver all packets, in which case some will be dropped.In the online algorithms community, researchers have used competitive analysis to evaluate the quality of an online policy in maximizing the value of those packets it is able to transmit. In this article, we provide a detailed survey of the field, describing various models of the problem that have been studied, and summarizing the known results.

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Section: Bounded-delay Model With Maximum Buffer Requirementmentioning

confidence: 99%

A survey of buffer management policies for packet switches

Goldwasser

2010

SIGACT News

109

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“…Fung [10] provides a 2-competitive deterministic algorithm and Li presents an alternative proof [15]. When the number of size-bounded buffers is more than 1, Azar and Levy [3] provide a 9.82-competitive deterministic algorithm and Li [14] improves the competitive ratio to 3 + √ 3 ≈ 4.723.…”

Section: A Related Workmentioning

confidence: 99%

Online learning approaches in maximizing weighted throughput

Zhang

Chen

2010

International Performance Computing and Communications Conference

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Abstract-Motivated by providing quality-of-service for next generation IP-based networks, we design algorithms to schedule packets with values and deadlines. Packets arrive over time; each packet has a non-negative value and an integer deadline. In each time step, at most one packet can be sent. Packets can be dropped at any time before they are sent. The objective is to maximize the total value gained by delivering packets no later than their respective deadlines. This model is the wellstudied bounded-delay model (Hajek. CISS 2001. Kesselman et al. SICOMP 2004 which extensive competitive online algorithms have been developed for. In a generalization of this model, the success of delivering a packets in each time step depends on the reliability of the communication channel.In this paper, we apply online learning approaches on this model as well as a few of its variants. We design online learning algorithms and analyze their performance theoretically in terms of external regret. We also measure these algorithms' performance experimentally. We conclude that no online learning algorithms have a constant regret. Our online learning algorithms outperform the competitive algorithms for algorithmic simplicity and running complexity. However, in general, this online learning algorithms work no worse than the best known competitive online algorithm for maximizing weighted throughput in practice.

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“…(A packet's slack time is defined as the difference between its deadline and release time.) The bounded-buffer model is one variant of the multi-buffer model proposed by Azar and Levy [2]. A 9.82-competitive online algorithm has been provided for this model [2], but no offline algorithms have been known yet.…”

Section: Introductionmentioning

confidence: 99%

“…The bounded-buffer model is one variant of the multi-buffer model proposed by Azar and Levy [2]. A 9.82-competitive online algorithm has been provided for this model [2], but no offline algorithms have been known yet. In this paper, we study the offline setting of the multi-buffer model.…”

Section: Introductionmentioning

confidence: 99%

Scheduling Packets with Values and Deadlines in Size-Bounded Buffers

2011

J Comb Optim

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Motivated by providing quality-of-service differentiated services in the Internet, we consider buffer management algorithms for network switches. We study a multi-buffer model. A network switch consists of multiple size-bounded buffers such that at any time, the number of packets residing in each individual buffer cannot exceed its capacity. Packets arrive at the network switch over time; they have values, deadlines, and designated buffers. In each time step, at most one pending packet is allowed to be sent and this packet can be from any buffer. The objective is to maximize the total value of the packets sent by their respective deadlines. A 9.82-competitive online algorithm has been provided for this model (Azar and Levy. SWAT 2006), but no offline algorithms have been known yet. In this paper, We study the offline setting of the multi-buffer model. Our contributions include a few optimal offline algorithms for some variants of the model. Each variant has its unique and interesting algorithmic feature. These offline algorithms help us understand the model better in designing online algorithms. * Research is partially supported by NSF grant CCF-0915681. † Department of Computer Science, George Mason University, Fairfax, VA 22030, USA. lifei@cs.gmu.edu IntroductionMotivated by providing quality-of-service differentiated services in the Internet, we consider buffer management algorithms for network switches. We study a multi-buffer model. A network switch consists of m size-bounded buffers Q 1 , Q 2 , . . ., Q m ; their sizes are denoted as B 1 , B 2 , . . . , B m respectively. At any time, the number of packets residing in each individual buffer Q i cannot exceed its capacity B i . Time is discretized into time steps. Packets arrive at the network switch over time and each packet p has an integer arriving time (release time) r p ∈ R + , a non-negative value v p ∈ R + , an integer deadline d p ∈ Z + , and a designated buffer b p ∈ {Q 1 , . . . , Q m } that it can reside in. The deadline d p specifies the time by which p should be sent. This model is preemptive such that the packets already existing in the buffers can be dropped at any time before they are transmitted. A dropped packet cannot be delivered any more. In each time step, at most one pending packet is allowed to be sent and this packet may be from any buffer. The objective is to maximize weighted throughput, defined as the total value of the packets transmitted by their respective deadlines.The first QoS buffer management model is introduced in [1]. Since then, quite a few researchers have studied this model as well as other variants, mostly in the online settings [7,6,3,8,5]. A well-studied model is called the bounded-delay model. In this model, there is only one buffer. Packets have integer release time, integer deadlines, and non-negative values. The objective is to maximize the total value of the packets sent by their deadlines. An implicit assumption on this model is the buffer's sufficiently large size. All released packets can be stored ...

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Multiplexing Packets with Arbitrary Deadlines in Bounded Buffers

Cited by 9 publications

References 9 publications

A survey of buffer management policies for packet switches

A survey of buffer management policies for packet switches

Online learning approaches in maximizing weighted throughput

Scheduling Packets with Values and Deadlines in Size-Bounded Buffers

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