Large-scale network simulation: how big? how fast?

Fujimoto, Richard M.; Perumalla, Kalyan S.; Park, A.; Wu, Hao; Ammar, Mostafa; Riley, George F.

doi:10.1109/mascot.2003.1240649

Cited by 122 publications

(84 citation statements)

References 31 publications

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“…Around the same time Riley et al [18] implemented a parallel, distributed version of the ns simulator (PDNS) [21], which although not focused on simulating worms reported a significant increase in simulation speed. Since then some notable instances of parallel worm simulators include GTNetS [22], SSFNet [23] and further development of PDNS [19], which demonstrate a significant performance improvement in comparison to their sequential counterparts. A focus on reducing the execution time of worm simulators is two-fold, either focusing on parallelisation as a method of increasing fidelity or improving worm simulators to better facilitate research [19].…”

Section: Parallel Worm Simulatorsmentioning

confidence: 99%

“…Since then some notable instances of parallel worm simulators include GTNetS [22], SSFNet [23] and further development of PDNS [19], which demonstrate a significant performance improvement in comparison to their sequential counterparts. A focus on reducing the execution time of worm simulators is two-fold, either focusing on parallelisation as a method of increasing fidelity or improving worm simulators to better facilitate research [19].…”

Section: Parallel Worm Simulatorsmentioning

confidence: 99%

“…One such method that is commonly used to increase the performance of worm simulators is the use of parallelisation [17]- [19] however; these have only been commonly applied or compared when using a cluster with many discrete nodes. With the growing availability of multiple-core processors their performance when applying parallelisation techniques can be assessed.…”

Section: Introductionmentioning

confidence: 99%

“…IWS is used to compare the two different implementation methods, and their effect on the number of packets transmitted per second (PTS) [19]. The effect of altering various simulation parameters on the two different implementations is presented, and the difference in application of a parallel simulation in comparison to a sequential simulation is discussed.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Comparative Performance of a Parallel Implementation of an Internet-Scale Zero-Day Worm Epidemiology Simulator

Tidy¹,

Woodhead²,

Wetherall³

2016

IJCCE

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Abstract:The threat posed by fast-spreading malware is significant, particularly given the fact that network operator/administrator intervention is not likely to take effect within the typical epidemiological timescale of such infections. The cost of zero-day network worm outbreaks has been estimated to be up to US$2.6 billion for a single worm outbreak. Zero-day network worm outbreaks have been observed that spread at a significant pace across the global Internet, with an observed rate of reaching more than 90 percent of vulnerable hosts within 10 minutes. An accepted technology that is used in addressing the security threat presented by zero-day worms is the use of simulation systems, and a common factor determining their efficacy is their performance. An empirical comparison of a sequential and parallel implementation of a novel simulator, the Internet Worm Simulator (IWS), is presented detailing the impact of a selection of parameters on its performance. Experimentation demonstrates that IWS has the capability to simulate up to 91.8 million packets transmitted per second (PTS) for an IPv4 address space simulation on a single workstation computer, comparing favourably to previously reported metrics. It is concluded that in addition to comparing PTS performance, simulation requirements should be taken into consideration when assessing the performance of such simulators.

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Section: Parallel Worm Simulatorsmentioning

confidence: 99%

Section: Parallel Worm Simulatorsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Comparative Performance of a Parallel Implementation of an Internet-Scale Zero-Day Worm Epidemiology Simulator

Tidy¹,

Woodhead²,

Wetherall³

2016

IJCCE

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“…Large-scale simulations of communications networks and systems at this complexity require large amounts of memory and computing power that is usually available on supercomputers and high-performance computing clusters. Consequently, concurrent, parallel, and distributed simulation techniques must be employed [5].…”

Section: Modelling Requirementsmentioning

confidence: 99%

Performance evaluation of integrated smart energy solutions through large-scale simulations

Song

Kaleshi

Zhou

et al. 2011

2011 IEEE International Conference on Smart Grid Communications (SmartGridComm)

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Abstract-The information and communication technology (ICT)infrastructure that will empower the Smart Grid with real-time management of power flows, bidirectional metering, and more effective integration of renewable energy sources is both large and complex, involving many different sensing, measurement, and control devices. The analysis of such large-scale distributed systems together with the possible communications network technologies can be extremely difficult, not least because many modeling techniques tend to analyze individual components, rather than the relationships and interactions between components, and their impact on the system. In this paper, we describe the CLEVER simulator-a discrete-event simulator developed specifically for evaluating the performance of the ICT infrastructure of smart energy solutions at very large scales. This paper also presents results from simulation experiments comparing the impact of various access technologies on the performance of a smart metering infrastructure. Finally, an outline of ongoing work is provided, and directions for future work are identified.

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Attacking the kad network—real world evaluation and high fidelity simulation using DVN

Wang

Tyra

Chan-Tin

et al. 2013

Security Comm Networks

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SummaryThe Kad network, an implementation of the Kademlia DHT protocol, supports the popular eDonkey peer-to-peer file sharing network and has over 1 million concurrent nodes. We describe several attacks that exploit critical design weaknesses in Kad to allow an attacker with modest resources to cause a significant fraction of all searches to fail. We measure the cost and effectiveness of these attacks against a set of 16 000 nodes connected to the operational Kad network. Using our large-scale simulator, DVN, we successfully scaled up to a 200 000 node experiment. We also measure the cost of previously proposed, generic DHT attacks against the Kad network and find that our attacks are much more cost effective. Finally, we introduce and evaluate simple mechanisms to significantly increase the cost of these attacks.

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Large-scale network simulation: how big? how fast?

Abstract: ABSTRACT

Cited by 122 publications

References 31 publications

Comparative Performance of a Parallel Implementation of an Internet-Scale Zero-Day Worm Epidemiology Simulator

Comparative Performance of a Parallel Implementation of an Internet-Scale Zero-Day Worm Epidemiology Simulator

Performance evaluation of integrated smart energy solutions through large-scale simulations

Attacking the kad network—real world evaluation and high fidelity simulation using DVN

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