Fluid-Flow and Packet-Level Models of Data Networks Unified Under a Modular/Hierarchical Framework: Speedups and Simplicity, Combined

Bonaventura, Matías; Castro, Rodrigo

doi:10.1109/wsc.2018.8632499

Cited by 8 publications

(6 citation statements)

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“…The estimated minimum required buffer sizes to avoid packet drops on the QFX switch are shown in Figures 4 and 5 as a function of the event size and event rate respectively. Experiments cover event rates in the range [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20] kHz and event sizes in the range [1][2][3][4][5][6][7][8] MB, but fewer cases are included in the plot for simplicity. Regarding lab measurements, the plots show that the system can not cope with high event rates or event sizes due to lack of buffer.…”

Section: Resultsmentioning

confidence: 99%

“…The PowerDEVS [16] simulation toolkit was chosen as it proved suitable to model data networks either from a microscopic (packet-to-packet) and macroscopic (fluid-flow) approaches [17,18]. PowerDEVS allows defining model structure on a Graphical User Interface (GUI) suitable for non-experts, or using Python scripting to build large complex models [19].…”

Section: Related Workmentioning

confidence: 99%

“…In contrast to the analytical model, the simulation model can capture internal and emergent behaviours for all connected elements in the system. For instance, the model has been used to study load balancing mechanisms [17] and traffic shaping algorithms [9].…”

Section: Simulation Modelmentioning

confidence: 99%

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Benchmarking Data Acquisition event building network performance for the ATLAS HL-LHC upgrade

Pozo Astigarraga,

Bonaventura,

Maple

et al. 2024

EPJ Web of Conf.

Self Cite

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The ATLAS experiment’s data acquisition (DAQ) system will be extensively updated to take full advantage of the High-Luminosity LHC (HL-LHC) upgrade, allowing it to record data at unprecedented rates. The detector will be read out at 1 MHz, generating over 5 TB/s of data. This design poses significant challenges for the Ethernet-based network, which will have to transport 20 times more data than during Run 3. The increased data rate, data sizes and number of servers will exacerbate the TCP Incast effect observed in the past, making it impossible to fully exploit the capabilities of the network and limiting the performance of the processing farm. We present exhaustive and systematic experiments to define buffering requirements in network equipment to minimise the effects of TCP Incast and reduce the impact on processing applications. Both deep and shallow buffer switches were stress-tested using DAQ traffic patterns in a test environment at approximately 10% of the expected HL-LHC DAQ system size. As the desired HL-LHC system hardware is not currently available and the laboratory size is significantly smaller, the tests aim to extrapolate buffer requirements to the expected operating point. A novel analytical formula and new simulation models have been developed to cross-validate the results. The results of these evaluations will contribute to the decision-making process for the acquisitions of network hardware for the HL-LHC DAQ.

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Section: Resultsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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Benchmarking Data Acquisition event building network performance for the ATLAS HL-LHC upgrade

Pozo Astigarraga,

Bonaventura,

Maple

et al. 2024

EPJ Web of Conf.

Self Cite

View full text Add to dashboard Cite

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“…The work presented in [17] also introduced a fluid model for ad hoc networks, but with a focus on energy consumption. In the domain of static networks, other works created fluid models based on differential equations, such as in [20] and even the creation of a framework in [28]. This work is also inspired by the fluid model described in [29].…”

Section: Related Work and Limitationsmentioning

confidence: 99%

A Lightweight Fluid Model for Mobile Ad hoc Distributed Systems

Ferreira

Dufour

Silvestre

2022

2022 IEEE Symposium on Computers and Communications (ISCC)

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Emerging edge applications introduced new computing timevariant topologies with mobile nodes connected via ad hoc networks. Such topologies are fundamentally different from cloud infrastructures due to the lack of hierarchy and clear network function separation. Sometimes, nodes that are sources are also destinations and routers, hence, creating dynamic flow patterns traversing the network. The latter, thus, can change the average performance of a distributed system, such as throughput and latency. This work introduces an analytical model based on fluid quantities to study data flows of distributed systems in mobile ad hoc networks. Using an approach based on a network of queues with evenly distributed bandwidth over concurrent flows, this lightweight model enables fast, coarse-grained analysis of different distributed systems configurations. They enable the analysis of different topologies, mobility and data flow models with a small footprint. The model was implemented, validated and evaluated with stress workloads to confirm its accuracy.

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“…Apart from discrete time simulation, which is very time-consuming in the case of transient-state analysis, fluid flow or diffusion approximation methods can be used. Usually, due to its simplicity, the fluid flow method is the one used to model TCP networks [ 13 , 14 , 15 , 16 ]. The diffusion approximation, on the other hand, offers more accurate results.…”

Section: Introductionmentioning

confidence: 99%

Diffusion Model of a Non-Integer Order PIγ Controller with TCP/UDP Streams

Marek

Domański

Domańska

et al. 2021

Entropy

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In this article, a way to employ the diffusion approximation to model interplay between TCP and UDP flows is presented. In order to control traffic congestion, an environment of IP routers applying AQM (Active Queue Management) algorithms has been introduced. Furthermore, the impact of the fractional controller PIγ and its parameters on the transport protocols is investigated. The controller has been elaborated in accordance with the control theory. The TCP and UDP flows are transmitted simultaneously and are mutually independent. Only the TCP is controlled by the AQM algorithm. Our diffusion model allows a single TCP or UDP flow to start or end at any time, which distinguishes it from those previously described in the literature.

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Fluid-Flow and Packet-Level Models of Data Networks Unified Under a Modular/Hierarchical Framework: Speedups and Simplicity, Combined

Cited by 8 publications

References 0 publications

Benchmarking Data Acquisition event building network performance for the ATLAS HL-LHC upgrade

Benchmarking Data Acquisition event building network performance for the ATLAS HL-LHC upgrade

A Lightweight Fluid Model for Mobile Ad hoc Distributed Systems

Diffusion Model of a Non-Integer Order PIγ Controller with TCP/UDP Streams

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