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UBC Theses and Dissertations

An observation-based runtime configuration framework for the front-end network edge Mohammad, Rafiuzzaman


Despite the prominence of automated runtime configuration procedures, relatively little is known about managing the runtime configurations of general-purpose programming in resource-constrained IoT platforms at the network edge. For example, high-level language-written application programming (e.g., video/audio surveillance) in IoT enables local data processing to decrease latency, bandwidth, and infrastructure costs and address data safety and privacy concerns. However, without a good configuration, such computing generates undesirable performance or sudden and unexpected resource outages, leading to an application or a complete system failure. On the other hand, stringent resources in IoT make the performance of general-purpose programming highly discontinuous, which the existing linear or non-linear models can not capture. As a result, while the current configuration techniques make typical computing (e.g., cloud, High-Performance Computing (HPC)) efficient, it still needs to be determined whether or not they are efficient enough to manage general-purpose edge computing. This research systematically analyzed the runtime configuration challenges for general-purpose programming in IoT. In the process, we discovered several new application performance associations and system resource variance patterns in this state space with which we address the constraints, heterogeneity, discontinuity, and scalability issues of IoT at the network edge. We applied these performance associations and other systematic state space sampling methods to address these issues as they arise in two important and prominent areas of automated runtime configuration: (1) resource-exhaustion detection and (2) performance optimization. The latter area is divided more into a pipeline configuration and b collocated performance approximation. With cross-platform failure prediction, configuration management, and approximation techniques, we apply an intelligent and general set of configuration capabilities to general-purpose edge computing. Across various real-world case studies, our techniques outperform conventional runtime configuration techniques regarding performance improvements and approximation accuracy and pave the way for a new direction toward general-purpose edge computing.

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