WS-Notification case study and experiment

Johnsen, Frank T.; Bloebaum, Trude H.; Calero, José M. Alcaraz; Wang, Qi; Nightingale, James; Manso, Marco; Jansen, Norman

doi:10.1109/icmcis.2017.7956491

“…Their non-intrusive solution identifies the communication technology providing the link but there is limited discussion about how to adapt user data flows to the network conditions. Similarly, the study in [6] discusses experiments with a broker implementing the publish/subscribe message exchange pattern using web services. The experiments were done in a network connecting real radios and emulated links.…”

Section: Tactical Systemsmentioning

confidence: 99%

“…These three problems are partially addressed in the literature reporting simulations and field experiments in tactical networks (e.g. [5], [6], [7], [8], [9], [10]). For example, it is common to find studies using non-stochastic user-generated data flows with few types of messages sent to the network within an uniform time window (problem A).…”

Section: Introductionmentioning

confidence: 99%

Queuing Over Ever-Changing Communication Scenarios in Tactical Networks

Lopes

¹

,

Balaraju

²

,

Rettore

³

et al. 2022

IEEE Trans. on Mobile Comput.

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This paper introduces a hierarchy of queues complementing each other to handle ever-changing communication scenarios in tactical networks. The first queue stores the QoS-constrained messages from command and control systems. These messages are fragmented into IP packets, which are stored in a queue of packets (second) to be sent to the radio buffer (third), which is a queue with limited space therefore, open to overflow. We start with the hypothesis that these three queues can handle ever-changing user(s) data flows (problem A) through ever-changing network conditions (problem B) using cross-layer information exchange, such as buffer occupancy, data rate, queue size and latency (problem A|B). We introduce two stochastic models to create sequences of QoS-constrained messages (A) and to create ever-changing network conditions (B). In sequence, we sketch a control loop to shape A to B to test our hypothesis using model A|B, which defines enforcement points at the incoming/outgoing chains of the system together with a control plane. Then, we discuss experimental results in a network with VHF radios using data flows that overflows the radio buffer over ever-changing data rate patterns. We discuss quantitative results showing the performance and limitations of our solutions for problems A, B and A|B.

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“…Since a precise definition of the scenario is missing, it is difficult to quantify the performance bounds of tactical systems using the network. Moreover, hybrid experiments combining real and simulated/emulated network environments, are also developed to evaluate the system performance [49]. These solutions try to vary the network conditions using non-stochastic means to increase the accuracy quantifying performance metrics and determining when the system fails to perform.…”

Section: B Tactical Systemsmentioning

confidence: 99%

Quantizing Radio Link Data Rates to Create Ever-Changing Network Conditions in Tactical Networks

Lopes

¹

,

Loevenich

²

,

Rettore

³

et al. 2020

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Several sources of randomness can change the radio link data rate at the edge of tactical networks. Simulations and field experiments define these sources of randomness indirectly by choosing the mobility pattern, communication technology, number of nodes, terrain, obstacles and so on. Therefore, the distribution of change in the network conditions is unknown until the experiment is executed. We start with the hypothesis that a model can quantize the network conditions, using a set of states updated within a time window, to define and control the distribution of change in the link data rate before the experiment is executed. The goal is to quantify how much variation in the link data rate a tactical system can handle and how long it takes to resume IP data-flows after link disconnections. Our model includes functions to combine patterns of change together, transforming one pattern into another, jumping between patterns, and creating loops among different patterns of change. We use exemplary patterns to show how the change in the data rate impacts other link metrics, such as latency and jitter. Our hypothesis is verified with experiments using VHF radios over different patterns of change created by our model. We compute the inter-packet latency of three types of IP data-flows (broadcast, unicast and overlay) to highlight the time to resume data-flows after long link disconnections. The experimental results also support the discussion on the advantages and limitations of our model, which was designed to test tactical systems using military radios.

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“…Therefore, these experiments are difficult to reproduce, and are limited to a specific evaluation scenario which requires precise definition to quantify the performance of the tactical systems using the network. Moreover, hybrid experiments combining real and simulated/emulated network environments, are also developed to evaluate the system performance [48]. These solutions try to vary the network conditions using non-stochastic means to increase the accuracy quantifying performance metrics and determining when the system fails to perform.…”

Section: B Tactical Systemsmentioning

confidence: 99%

Quantizing Radio Link Data Rates to Create Ever-changing Network Conditions in Tactical Networks

Lopes¹,

Loevenich²,

Rettore³

et al. 2020

Preprint

0

View full text Add to dashboard Cite

Several sources of randomness can change the radio link data rate at the edge of tactical networks. Simulations and field experiments define these sources of randomness indirectly by choosing the mobility pattern, communication technology, number of nodes, terrain, obstacles and so on. Therefore, the distribution of change in the network conditions is unknown until the experiment is executed. We start with the hypothesis that a model can quantize the network conditions, using a set of states updated within a time window, to define and control the distribution of change in the link data rate before the experiment is executed. The goal is to quantify how much variation in the link data rate a tactical system can handle and how long it takes to resume IP data-flows after link disconnections. Our model includes functions to combine patterns of change together, transforming one pattern into another, jumping between patterns, and creating loops among different patterns of change. We use exemplary patterns to show how the change in the data rate impacts other link metrics, such as latency and jitter. Our hypothesis is verified with experiments using VHF radios over different patterns of change created by our model. We compute the inter-packet latency of three types of IP data-flows (broadcast, unicast and overlay) to highlight the time to resume data-flows after long link disconnections. The experimental results also support the discussion on the advantages and limitations of our model, which was designed to test tactical systems using military radios.

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WS-Notification case study and experiment

Cited by 8 publications

References 12 publications

Queuing Over Ever-Changing Communication Scenarios in Tactical Networks

Queuing Over Ever-Changing Communication Scenarios in Tactical Networks

Quantizing Radio Link Data Rates to Create Ever-Changing Network Conditions in Tactical Networks

Quantizing Radio Link Data Rates to Create Ever-changing Network Conditions in Tactical Networks

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