DOI: 10.5281/zenodo.7311294
title: Edge infrastructure traces

description: These Edge infrastructure traces consist of bandwidth and latency between devices along with the execution time of the microservices of a video processing application on the different devices. These traces were collected between 2022-10-24 and 2022-11-09.

The execution times are related to a set of microservices, including video encoding and framing, along with the training and inference model for road sign classification.The microservices' descriptions are provided in the following research paper:N. Mehran, Z. N. Samani, D. Kimovski, and R. Prodan, "Matching-based Scheduling of Asynchronous Data Processing Workflows on the Computing Continuum," 2022 IEEE International Conference on Cluster Computing (CLUSTER), 2022, pp. 58-70, DOI: 10.1109/CLUSTER51413.2022.00021.

Devices are as follows:- D01 machine has a twelve-core AMD Ryzen Threadripper 2920X processor with 32GB memory;- D02 machine has an eight-core Intel Core(TM) i7-7700 processor with 16GB memory;- Nvidia Jetson Nano machine has a four-core ARM Cortex-A72 processor with 4GB memory;- Raspberry Pi 4 has a four-core ARM Cortex A57 processor with 4GB of memory.

Moreover, the dataset provides the network traces regarding the bandwidth and latency between the Edge devices. We provided the size of the transmitted messages between the devices for the throughput measurements. For the network latency, the information related to the round trip time is calculated by ICMP message request and reply.

The traces include five parts of data as follows:

BW-MessageSize.csv- Timestamp: Date and time in CET- Source device: the throughput from which we are checking; D02 machine- Destination device: the throughput to which we are checking; D01 or D02 machine- Message size (MB): the message size transmitted between the devices- BW (Mbps): the maximum achievable bandwidth*** "-1" means "iperf3: error - unable to connect to server: Connection refused"

 

JetsonNano-ExecutionTime.csv- Timestamp: Date and time in CET- Device: the Nvidia Jetson Nano single-board computer- Microservice: includes encoding, framing, training, or inference of Dockerized microservices- Execution Time (seconds)

Large-ExecutionTime.csv- Timestamp: Date and time in CET- Device: the highest performance machine (D01 machine) in the C3 testbed- Microservice: includes encoding, framing, training, or inference (containers)- Execution time (seconds)

 

Latency.csv- Timestamp: Date and time in CET- Source device: the latency from which we are checking; D02 machine- Destination device: the latency to which we were checking; D01 or D02 machine- Minimum latency (ms) of the round-trip times among four transmitted messages- Average latency (ms) of the round-trip times among four transmitted messages- Maximum latency (ms) of the round-trip times among four transmitted messages- Mean standard deviation of the round-trip times among four transmitted messages

 

RPi4-ExecutionTime.csv- Timestamp: Date and time in CET- Device: Raspberry Pi v4 single-board computer- Microservice: includes encoding, framing, training, or inference (containers)- Execution time (seconds)

 

For more information regarding the testbed, please refer to the C3 website at https://c3.itec.aau.at/.

Authors:Zahra Najafabadi Samani, Narges Mehran, Dragi Kimovski, Josef Hammer, Radu ProdanInstitute of Information Technology, Alpen-Adria-Universitaet Klagenfurt, Austria