RUMP: Resource Usage Multi-Step Prediciton

Ruslan Kain, Sara A. Elsayed, Yuanzhu Chen, Hossam Hassanein

[Multi-step Prediction of Worker Resource Usage at the Extreme Edge]

Data may also be accessed on Borealis

Citation: Kain, Ruslan; Elsayed, Sara A.; Chen, Yuanzhu; Hassanein, Hossam S., 2022, "Resource Usage of Applications Running on Raspberry Pi Devices", https://doi.org/10.5683/SP3/GOZAJE, Borealis, V1, UNF:6:FjVtgSYUu2Iy08LQ2ra6fQ== [fileUNF]

Description

The collection and construction of this dataset were organized by the Queen's Telecommunications Research Lab (TRL) and led by Ruslan Kain, a Ph.D. student at Queen's School of Computing. The dataset includes dynamic resource usage information associated with running edge-native applications on a set of four heterogeneous Single Board Computers.

Experimental Setup

Four Raspberry Pi 4 devices have 2, 2, 4, and 8 GB RAM sizes, and CPU frequencies of 1200, 1500, 1500, and 1800 MHz. This is to establish heterogeneity of the devices used and collected data and to enable data-based applications for Edge Computing Research. The resource usage measurements have a five-second granularity. We managed to collect more than 550 thousand unique data points representing the 768 hours of running applications on Raspberry Pi Devices.

Raspberry Pis

Worker specifications and labels
Worker Label RAM Size (GB) CPU Cycle Freq. (GHz) RPi4B8GB 8 1800 RPi4B4GB 4 1500 RPi4B2GB2 2 1500 RPi4B2GB1 2 1200

Descriptive Sample

Dataset
Time CPU Time (s) Memory (%) QOS (sec) Resource Usage State Breakfast 0.6 33 1.2 Idle Second Breakfast 12.3 44 3.5 Augmented Reality Lunch 15.6 55 4.1 Crypto Mining Supper 0.5 26 1.3 Idle Dinner 4.5 66 2.7 Streaming Midnight Snack 9.2 11 3.4 Gaming

Worker User Application

Augmented Reality	Dunio Coin Mining	Youtube Streaming	Gaming