Comparing IO Visor and Pcap for Security Inspection of Traced Packets from SmartX Box

Rathore, Muhammad Ahmad; Risdianto, Aris Cahyadi; Nam, Taekho; Kim, Jong-Won

doi:10.1007/978-981-10-7605-3_201

Cited by 2 publications

(3 citation statements)

References 4 publications

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“…We use extended Berkeley Packet Filtering (eBPF)-enabled packet tracing tools such as IO Visor for measuring statistical summary of network traffic data. IO Visor-based packet tracing employs the eBPF core functionalities [12] which enables inkernel virtual machines (VMs) with byte-code tracing program execution. IO Visor has the main advantage to monitor and trace user and kernel events (through kprobe and uprobe), providing statistics in maps fetched on the points of interest [13].…”

Section: Network Traffic Data Set Collectionmentioning

confidence: 99%

Edge Intelligence in Softwarized 6G: Deep Learning-enabled Network Traffic Predictions

Zeb¹,

Rathore²,

Mahmood³

et al. 2021

Preprint

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The 6G vision is envisaged to enable agile network expansion and rapid deployment of new on-demand microservices (i.e., visibility services for data traffic management, mobile edge computing services) closer to the network's edge IoT devices. However, providing one of the critical features of network visibility services, i.e., data flow prediction in the network, is challenging at the edge devices within a dynamic cloud-native environment as the traffic flow characteristics are random and sporadic. To provide the AI-native services for the 6G vision, we propose a novel edge-native framework to provide an intelligent prognosis technique for data traffic management in this paper. The prognosis model uses long short-term memory (LSTM)based encoder-decoder deep learning, which we train on real time-series multivariate data records collected from the edge µ-boxes of a selected testbed network. Our result accurately predicts the statistical characteristics of data traffic and verify against the ground truth observations. Moreover, we validate our novel framework model with two performance metrics for each feature of the multivariate data.

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Section: Network Traffic Data Set Collectionmentioning

confidence: 99%

Edge Intelligence in Softwarized 6G: Deep Learning-enabled Network Traffic Predictions

Zeb¹,

Rathore²,

Mahmood³

et al. 2021

Preprint

Self Cite

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“…8 IO Visor employs the extended Berkeley Packet Filtering (a.k.a. eBPF) 11,29 which provides offers in-kernel VMs with byte-code tracing program execution. 30 user events (through kprobe and uprobe), subsequently to keep statistics in maps fetched on the points of interest.…”

Section: Flow Summarization and Optimal Polling For Maintaining Network And Storage Loadmentioning

confidence: 99%

“…3 Visibility data collected, parsed, and validated from distributed resources is reliably transferred at a centralized location named "SmartX Visibility Center" and stored in "Visibility DataLake" waiting for visibility data integration for analysis and subsequent interactive visualizations. 11 Finally, to effectively verify the operational status of physical, virtual, and container types of resources, we have leveraged multi-belt onion-ring style visualization for playground. 12 In summary, the key contributions of this article are:…”

mentioning

confidence: 99%

Maintaining SmartX multi‐view visibility for OF@TEIN+ distributed cloud‐native edge boxes

Rathore

Kim

2020

Trans Emerging Tel Tech

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Maintaining knowledge of infrastructure has always been a key element to ensure the performance of complex distributed systems, being the first step to control quality of service, identify key performance bottlenecks, or make decisions about resource allocation and job scheduling, to name a few. In cloud-native edge boxes, edge computing leverages cloud-native applications to process/analyze some of their computing/storage own close to the location where the data is generated by a large number of heterogeneous devices. Some of the differences between this approach and more conventional architecture, like cloud are that in a distributed environment, these resources/applications have varying running conditions depending on resource availability, quality of network connection, and being geo-distributed. These aforementioned challenges in operations, however, contribute to a requirement toward identifying efficient methods for monitoring operational environment with continuity in order to maintain and sustain services. In this article, we present setup and configuration of distributed resources with cloud-native edge capabilities and having centralized connectivity in a specialized multi-site cloud testbed. Second, we propose a solution to persistently maintain multi-view (ie, resource-layer and flow layer) visibility data (monitoring) collection under varying situations and environments. Third, we present verification results on a prototype implementation and interactive visualization support. This solution allows maintaining monitoring visibility data that conform to the specific demands of cloud-native edge computing resources. We evaluate the research by maintaining visibility data of multi-site cloud at 14 research institutes for several months and we demonstrate that it fulfills the requirements we previously enumerated. INTRODUCTIONMaintaining a stable monitoring state has been a key element in ensuring the sustainable performance of complex distributed systems, as the first step in managing quality of service, detect failures, or making decisions about resource allocation, to name a few. Edge cloud 1 is a broad distribution of storage/-compute resources around geographic locations with cloud-like capabilities located at the infrastructure edge. Cloud-native application is a methodology of building

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Comparing IO Visor and Pcap for Security Inspection of Traced Packets from SmartX Box

Cited by 2 publications

References 4 publications

Edge Intelligence in Softwarized 6G: Deep Learning-enabled Network Traffic Predictions

Edge Intelligence in Softwarized 6G: Deep Learning-enabled Network Traffic Predictions

Maintaining SmartX multi‐view visibility for OF@TEIN+ distributed cloud‐native edge boxes

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