Competitive buffer management for multi-queue switches in QoS networks using packet buffering algorithms

Kobayashi, Katsuhei; Miyazaki, Shuichi; Okabe, Yasuo

doi:10.1016/j.tcs.2017.02.014

Cited by 7 publications

(5 citation statements)

References 42 publications

(57 reference statements)

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“…As this process continues, cascading failures occur. Recovery is possible during an overload failure, but it is often slowed by such complex congestion relief mechanisms, such as buffer management and queue scheduling [35,36]. Thus, we do not consider device recovery in our model.…”

Section: Cascading Failure Modelmentioning

confidence: 99%

Dynamic behavior analysis of an internet flow interaction model under cascading failures

et al. 2019

Phys. Rev. E

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Cascading failures in the internet have attracted recent attention due to their unpredictability and destructive consequences. Exploring the failure behavior patterns is necessary because they can provide effective intervention approaches to prevent huge network disasters. To analyze internet flow behaviors during cascading failures (chain reactions in router and link failures), we characterize the internet as two coupled networks, the router network and the flow network. Here the flow network is an abstract representation of data correlations obtained from the router network. We use this coupled network to build a cascading failure model for studying flow transmission and competition, which is reflected in bandwidth competition given by limited link capacity. We first study the dependency between routers and flows to explore the flow transmission efficiency when a failure event occurs. Moreover, we find that rerouting enables flow competition area (the number of flows with which one flow has a competitive relationship) to initially remain stable during a failure episode, but that it then quickly drops due to poor physical network connectivity. Additionally, in the early stage after the failure event, the degree of flow competition sharply increases because of the growing number of the flows and congestion. Subsequently, the flow competition decreases due to the failure of flow transmission.

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Section: Cascading Failure Modelmentioning

confidence: 99%

Dynamic behavior analysis of an internet flow interaction model under cascading failures

et al. 2019

Phys. Rev. E

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“…This is a rare case where the problem has been closed completely: the work [4] presents a deterministic policy with competitive ratio converging to e e−1 ≈ 1.582 for arbitrary B, and a matching lower bound has been proven in [5]. The case of multiple separated queues for packets with variable values has been considered in [5,16,26]; for packets with variable required processing, in [27], where a 2-competitive policy was introduced for that setting, and in [13], where each queue is constrained to contain packets with the same processing requirement.…”

Section: Related Workmentioning

confidence: 99%

New Competitiveness Bounds for the Shared Memory Switch

Bochkov,

Davydow,

Gaevoy

et al. 2019

Preprint

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We consider one of the simplest and best known buffer management architectures: the shared memory switch with multiple output queues and uniform packets. It was one of the first models studied by competitive analysis, with the Longest Queue Drop (LQD) buffer management policy shown to be at least √ 2-and at most 2-competitive; a general lower bound of 4/3 has been proven for all deterministic online algorithms. Closing the gap between √ 2 and 2 has remained an open problem in competitive analysis for more than a decade, with only marginal success in reducing the upper bound of 2. In this work, we first present a simplified proof for the √ 2 lower bound for LQD and then, using a reduction to the continuous case, improve the general lower bound for all deterministic online algorithms from 4 3 to √ 2. Then, we proceed to improve the lower bound of √ 2 specifically for LQD, showing that LQD is at least 1.44546086-competitive. We are able to prove the bound by presenting an explicit construction of the optimal clairvoyant algorithm which then allows for two different ways to prove lower bounds: by direct computer simulations and by proving lower bounds via linear programming. The linear programming approach yields a lower bound for LQD of 1.4427902 (still larger than √ 2).

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“…The multi-queue switch model [9,11,35] consists of m FIFO queues. In this model, the task of an algorithm is to manage its buffers and to schedule packets.…”

Section: Introductionmentioning

confidence: 99%