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Computation offloading of 5G devices at the Edge nodes using WebAssembly

07 July 2021, 09:45

Master thesis project at RISE by Gustav Hansson

With an ever-increasing percentage of the human population connected to the internet, the amount of data produced and processed is at an all-time high. Edge Computing has emerged as a paradigm to handle this growth and, combined with 5G, enables complex time-sensitive applications running on resource-restricted devices.

This master thesis investigated the use of WebAssembly in the context of computational offloading at the Edge. The focus was on utilizing WebAssembly to move computational heavy parts of a system from an end device to an Edge Server. An objective was to improve program performance by reducing the execution time and energy consumption on the end device.

A proof-of-concept offloading system was developed to research this. The system was evaluated on three different use cases; calculating Fibonacci numbers, matrix multiplication, and image recognition. Each use case was tested on a Raspberry Pi 3 and Pi 4 comparing execution of the WebAssembly module both locally and offloaded. Each test were also run natively on both the server and the end device to provide some baseline for comparison.

When offloading the WebAssembly module, the results show that it has significant per­formance benefits. This gain can especially be seen with more complex computations which handle a large amount of data. For the execution time compared to running the application natively, the performance gain is minimal.

By offloading the WebAssembly module from the end device to the offloading server the end device power consumption is reduced. This reduction could lead to an increase in battery life for a mobile unit. With the offloading reducing the need for powerful hardware on the device, there are also possibilities for the use of less powerful but more energy-efficient components on the device while still having the performance of a more powerful device while at the same time improving the battery life.

See the thesis here: https://lnkd.in/dRVAAcP

Johan Kristiansson

Senior Forskare

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Daniel Olsson

Systemarkitekt

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