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Platform-independent live process migration for edge computing applications

University of British Columbia

The past decade has witnessed the rise of the Internet of Things (IoT) devices with single-board computers such as the Raspberry Pi. The increased programmability and connectivity allow realization of the edge computing paradigm, in which we run on these devices complex distributed applications that were traditionally run on the cloud. Since IoT devices are subject to resource constraints like available battery power, we need to dynamically migrate a running process from one machine to another to prevent losing state. In cloud systems, VM migration techniques based on virtual memory snapshots are used in similar failure scenarios. However, it is challenging to apply virtualization-based migration techniques in the IoT domain, due to the differences in processor and platform architecture between IoT devices. In this thesis, we present a platform-independent migration technique, which we call ThingsMigrate, to address this challenge. Given a program, ThingsMigrate automatically instruments the source code to expose the hidden states such as closures and continuations. During run-time, the instrumented program produces on demand a platform-agnostic snapshot of the process, from which new code is generated to resume execution. Thus, ThingsMigrate enables process migration without any modifications to the underlying virtual machine, providing platform-independence. Using standard JavaScript (JS) benchmarks, we demonstrate that it can migrate resource-intensive applications, with average run-time latency overhead of 33% and memory overhead of 78%. ThingsMigrate supports multiple subsequent migrations without introducing additional overhead over each subsequent migration.

Text

2021-02-12

Attribution-NonCommercial-NoDerivatives 4.0 International

http://hdl.handle.net/2429/77288

Electrical and Computer Engineering

University of British Columbia

UBCV

http://creativecommons.org/licenses/by-nc-nd/4.0/