Their Share: Diversity and Disparity in IP Traffic

Broido, Andre; Hyun, Young; Gao, Ruomei; Claffy, Kimberly C.

doi:10.1007/978-3-540-24668-8_12

Cited by 37 publications

(44 citation statements)

References 14 publications

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“…It has therefore been proposed that it is best to keep track of only the large flows, and simply ignore all the small ones [215]. Likewise, traffic engineering may benefit from focusing on the few large flows that dominate the bandwidth consumption [92]. Even routing can benefit from offloading the large flows to a fast hardware forwarding device, thereby freeing capacity on the slower software-based router [590].…”

Section: Discussionmentioning

confidence: 99%

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Workload Modeling for Computer Systems Performance Evaluation

Feitelson

2015

197

131

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Reliable performance evaluations require the use of representative workloads. This is no easy task since modern computer systems and their workloads are complex, with many interrelated attributes and complicated structures. Experts often use sophisticated mathematics to analyze and describe workload models, making these models difficult for practitioners to grasp. This book aims to close this gap by emphasizing the intuition and the reasoning behind the definitions and derivations related to the workload models. It provides numerous examples from real production systems, with hundreds of graphs. Using this book, readers will be able to analyze collected workload data and clean it if necessary, derive statistical models that include skewed marginal distributions and correlations, and consider the need for generative models and feedback from the system. The descriptive statistics techniques covered are also useful for other domains.

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Section: Discussionmentioning

confidence: 99%

“…The simplest metric is the joint ratio (also called the crossover [92]). This is a direct generalization of the 90/10 rule and the 80/20 rule.…”

Section: Metrics For Mass-count Disparitymentioning

confidence: 99%

Workload Modeling for Computer Systems Performance Evaluation

Feitelson

2015

197

131

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“…In line with [3,21], we consider 1000 such popular destination prefixes. In fact, each of them may not merely represent an individual prefix but also a group of distinct destination prefixes that have the same set of candidate EPs [22] in order to improve network and TE algorithm scalability. Hence, the number of prefixes we consider could actually represent an even larger value of actual prefixes.…”

Section: Network Topology and Destination Prefixesmentioning

confidence: 99%

“…Previous work has shown that inter-AS traffic is not uniformly distributed [23]. According to [22], the volume of inter-AS traffic demand is top-heavy and it can be approximated by a Weibull distribution with the shape parameter equal to 0.2-0.3. We generate the inter-AS TM following this distribution with the shape parameter equal to 0.3.…”

Section: Inter-as Traffic Matrixmentioning

confidence: 99%

A Closed-Loop Control Traffic Engineering System for the Dynamic Load Balancing of Inter-AS Traffic

Amin

Pavlou

et al. 2009

J Netw Syst Manage

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Inter-AS outbound traffic engineering (TE) is a set of techniques for controlling inter-AS traffic exiting an autonomous system (AS) by assigning the traffic to the best egress points (i.e. routers or links) from which the traffic is forwarded to adjacent ASes towards the destinations. In practice, changing network conditions such as inter-AS traffic demand variation, link failures and inter-AS routing changes occur dynamically. These changes can make fixed outbound TE solutions inadequate and may subsequently cause inter-AS links to become congested. In order to overcome this problem, we propose the deployment of a closed loop control traffic engineering system that makes outbound traffic robust to inter-AS link failures and adaptive to changing network conditions. The objective is to keep the inter-AS link utilization balanced under unexpected events while reducing service disruption and reconfiguration overheads. Our evaluation results show that the proposed system can successfully achieve better load balancing with less service disruption and re-configuration overhead in comparison to alternative approaches.

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“…Previous work has shown that intra-and inter-AS traffic are not uniformly distributed [32,35]. According to [33], AS traffic volumes are top-heavy and can be approximated by Weibull distribution with shape parameter 0.2-0.3. We therefore generate the inter-AS TM with traffic demand using this distribution with the shape parameter 0.2.…”

Section: Traffic Matricesmentioning

confidence: 99%

Joint Optimization of Intra- and Inter-Autonomous System Traffic Engineering

Howarth

Wang

et al. 2006

2006 IEEE/IFIP Network Operations and Management Symposium NOMS 2006

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Abstract-Traffic Engineering (TE) is used to optimize IP operational network performance. The existing literature generally considers intra-and inter-AS (Autonomous System) TE independently. However, the overall network performance may not be truly optimized when these aspects are considered separately. This is due to the interaction between intra-and inter-AS TE, where a solution of intra-AS TE may not be a good input to inter-AS TE and vice versa. To remedy this situation, we propose considering intra-AS aspects during inter-AS TE and vice versa. We propose a joint optimization of intra-and inter-AS TE to further improve the overall network performance by simultaneously finding the best egress points for the inter-AS traffic and the best routing scheme for the intra-AS traffic. Three strategies are presented to attack the problem, namely sequential, nested and integrated optimization. Our simulation study shows that, compared to sequential and nested optimization, integrated optimization can significantly improve the overall network performance by accommodating 30%-60% more traffic demands.

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Their Share: Diversity and Disparity in IP Traffic

Cited by 37 publications

References 14 publications

Workload Modeling for Computer Systems Performance Evaluation

Workload Modeling for Computer Systems Performance Evaluation

A Closed-Loop Control Traffic Engineering System for the Dynamic Load Balancing of Inter-AS Traffic

Joint Optimization of Intra- and Inter-Autonomous System Traffic Engineering

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