Open-source software tools for measuring resources consumption and DASH metrics

Montagud, Mario; Rus, Juan Antonio De; Fayos-Jordan, Rafael; García-Pineda, Miguel; Segura-García, Jaume

doi:10.1145/3339825.3394931

Cited by 9 publications

(9 citation statements)

References 6 publications

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“…As mentioned earlier, we design filters based on the attack behavior instead of using the number of connections limitation. Initially, we use Tshark [45] to convert the captured traffic; alternatively, TCPDUMP [46] offers a similar capacity to acquire traffic information [47]. After gathering enough information from the collected traffic, the next step is to analyze the Slowloris attack's detection.…”

Section: Analyzing Slowloris Behaviormentioning

confidence: 99%

SDToW: A Slowloris Detecting Tool for WMNs

et al. 2020

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Denial of service (DoS) attacks play a significant role in contemporary cyberspace scenarios. A variety of different DoS attacks pollute networks by exploring various vulnerabilities. A group of DoS called application DoS attacks explore application vulnerabilities. This work presents a tool that detects and blocks an application DoS called Slowloris on wireless mesh networks (WMNs). Our tool, called SDToW, is designed to effectively use the structure of the WMNs to block the Slowloris attack. SDToW uses three different modules to detect and block the attack. Each module has its specific tasks and thus optimizes the overall detection and block efficiency. Our solution blocks the attacker on its first WMN hop, reducing the malicious traffic on the network and avoiding further attacks from the blocked user. The comparison results show that SDToW performs with 66.7% less processing consumption and 89.1% less memory consumption than Snort. Our solution does not limit the number of parallel connections per user. Hence, by avoiding this limitation, SDToW has a lower incidence of false positive errors than Snort.

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Section: Analyzing Slowloris Behaviormentioning

confidence: 99%

SDToW: A Slowloris Detecting Tool for WMNs

et al. 2020

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“…The tests consisted of launching 10 iterations with the PC-MCU plus 10 iterations without the PC-MCU, collecting samples of the following set of metrics, by using a tool from Montagud et al [10]:…”

Section: Evaluation Methodology and Metricsmentioning

confidence: 99%

Pc-Mcu

Cernigliaro

Martos

Montagud

et al. 2020

Proceedings of the 30th ACM Workshop on Network and Operating Systems Support for Digital Audio and Video

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This paper introduces the Point Cloud Multipoint Control Unit (PC-MCU): a key component for multiuser holoconferencing systems, where remote participants are represented as Point Clouds. The presented solution redefines the idea of MCU, broadly used to optimize connections and communications between users in traditional videoconferencing, and introduces a set of key features for the optimization of holoconferencing services where multiple users can be remotely connected. The PC-MCU is a virtualized cloud-based component, that aims at reducing the end-user client computational resources and bandwidth usage, providing the following key features: fusion of volumetric videos, Level of Detail (LoD) adjustment and non visible data removal. The results obtained for a scenario with two remote users, show how the introduction of the PC-MCU provides significant benefits in terms of computational resources and bandwidth savings, thus alleviating the requirements at the client side in holoconferencing services when compared to a baseline condition without using it. These improvements open the door to further research on this area to enable scalable and adaptive holoconferencing services using lightweight devices. CCS CONCEPTS • Computer systems organization → Real-time operating systems; Real-time systems; Real-time operating systems; • Networks → Cloud computing; Network services; Cloud computing.

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“…During each experiment session the system resource consumption was measured using the Resources Consumption Metrics (RCM) tool [34]. The conversation audio was recorded with audio only capture using the Open Broadcaster Software tool.…”

Section: Experiments Setupmentioning

confidence: 99%

Evaluating the Impact of Tiled User-Adaptive Real-Time Point Cloud Streaming on VR Remote Communication

Subramanyam¹,

Viola²,

Jansen³

et al. 2022

Preprint

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Remote communication has rapidly become a part of everyday life in both professional and personal contexts. However, popular video conferencing applications present limitations in terms of quality of communication, immersion and social meaning. VR remote communication applications offer a greater sense of co-presence and mutual sensing of emotions between remote users. Previous research on these applications has shown that realistic point cloud user reconstructions offer better immersion and communication as compared to synthetic user avatars. However, photorealistic point clouds require a large volume of data per frame and are challenging to transmit over bandwidth-limited networks. Recent research has demonstrated significant improvements to perceived quality by optimizing the usage of bandwidth based on the position and orientation of the user's viewport with user-adaptive streaming. In this work, we developed a real-time VR communication application with an adaptation engine that features tiled user-adaptive streaming based on user behaviour. The application also supports traditional network adaptive streaming. The contribution of this work is to evaluate the impact of tiled user-adaptive streaming on quality of communication, visual quality, system performance and task completion in a functional live VR remote communication system. We perform a subjective evaluation with 33 users to compare the different streaming conditions with a neck exercise training task. As a baseline, we use uncompressed streaming requiring ca. 300Mbps and our solution achieves similar visual quality with tiled adaptive streaming at 14Mbps. We also demonstrate statistically significant gains to the quality of interaction and improvements to system performance and CPU consumption with tiled adaptive streaming as compared to the more traditional network adaptive streaming.

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Open-source software tools for measuring resources consumption and DASH metrics

Cited by 9 publications

References 6 publications

SDToW: A Slowloris Detecting Tool for WMNs

SDToW: A Slowloris Detecting Tool for WMNs

Pc-Mcu

Evaluating the Impact of Tiled User-Adaptive Real-Time Point Cloud Streaming on VR Remote Communication

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