Improved Online Algorithms for Buffer Management in QoS Switches

Chrobák, Marek; Jawor, Wojciech; Sgall, Jiřį́; Tichý, Tomáš

doi:10.1007/978-3-540-30140-0_20

Cited by 26 publications

(25 citation statements)

References 4 publications

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“…Observe that the probability of bin i ∈ [1, n 3 ] to be equal to the probability of bin n − i + 1. Thus, the expected number of empty bins in [1, n 3 ] is equal to the expected number of empty bins in [ 2n 3 , n]:…”

Section: A Bad Examplementioning

confidence: 99%

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Prompt Mechanisms for Online Auctions

Cole

Dobzinski

Fleischer

2008

Algorithmic Game Theory

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We study the following online problem: at each time unit, one of m identical items is offered for sale. Bidders arrive and depart dynamically, and each bidder is interested in winning one item between his arrival and departure. Our goal is to design truthful mechanisms that maximize the welfare, the sum of the utilities of winning bidders.We first consider this problem under the assumption that the private information for each bidder is his value for getting an item. In this model constant-competitive mechanisms are known, but we observe that these mechanisms suffer from the following disadvantage: a bidder might learn his payment only when he departs. We argue that these mechanism are essentially unusable, because they impose several seemingly undesirable requirements on any implementation of the mechanisms.To crystalize these issues, we define the notions of prompt and tardy mechanisms. We present two prompt truthful mechanisms -one deterministic and the other randomized, that guarantee a constant competitive ratio. We also prove that our deterministic mechanism is optimal for this setting.We then study a model in which both the value and the departure time are private information. While in the deterministic setting only a trivial competitive ratio can be guaranteed, we use randomization to obtain a prompt truthful Θ( 1 log m )-competitive mechanism. Finally, we show that no truthful randomized mechanism can achieve a ratio better than 1 2 in this model.

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Section: A Bad Examplementioning

confidence: 99%

“…In the non-strategic setting, this problem and its variants have been widely studied (e.g., [1,8,3]). The best deterministic algorithm to date guarantees a competitive ratio of ≈ 0.547 [4,11], while it is known that no deterministic algorithm can obtain a ratio better than 2 √ 5+1 ≈ 0.618 [2].…”

Section: Introductionmentioning

confidence: 99%

Prompt Mechanisms for Online Auctions

Cole

Dobzinski

Fleischer

2008

Algorithmic Game Theory

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“…Their lower bound construction relies on forcing some settings that are scaled versions of a previous choice made by the policy, and therefore with predictable outcomes. Without the extra requirements, Chrobak et al prove that the deterministic competitiveness is 1.377, demonstrating a lower bound and matching (memory-based) policy [CJST04,CJST07].…”

Section: Lower Boundsmentioning

confidence: 99%

“…The first deterministic policy to break the 2-competitive barrier for the general case is GenFlag, by Chrobak et al [CJST04,CJST07]. This always sends either the earliest-deadline e or heaviestvalued h, without restricting consideration to the optimal provisional schedule.…”

Section: General Modelmentioning

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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“…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.…”

Section: Introductionmentioning

confidence: 99%

Scheduling Packets with Values and Deadlines in Size-Bounded Buffers

2010

Combinatorial Optimization and Applications

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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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Improved Online Algorithms for Buffer Management in QoS Switches

Cited by 26 publications

References 4 publications

Prompt Mechanisms for Online Auctions

Prompt Mechanisms for Online Auctions

A survey of buffer management policies for packet switches

Scheduling Packets with Values and Deadlines in Size-Bounded Buffers

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