UC Santa Cruz

The non-uniform improvement of computer hardware performance poses a significant challenge for contemporary data processing in managing the growing volume of data. General-purpose systems encounter obstacles such as design, power, and heat management that hinder their computing power improvement. As data processing becomes more expensive and the increasing performance demands from applications, academia and industry are evincing interest in offloading data services to embedded systems (i.e., system software that runs on peripherals such as storage or network devices) to improve data processing efficiency. Given the domain-specific nature of embedded systems, this approach opens up abundant research opportunities, particularly as more applications rely on big data analysis for insights.

Efficiently leveraging embedded systems for data services requires answering three critical questions concerning why, what, and how. The ``why'' question pertains to the potential benefits of offloading a data service to an embedded system. Answering this question requires developing a methodology that can accurately quantify the benefits by taking into account the embedded system's domain nature and the data service workload. The ``what'' question pertains to what data services to offload to an embedded system. Answering this question requires a comprehensive understanding of the intended system and function to identify potential matches for successful offloading. In this thesis, I focus specifically on composable data services, not only because they serve as fundamental building blocks in applications, but also because their composability allows for more convenient migration to diverse systems. The ``how'' question pertains to determining the strategies to use for offloading. Given that embedded systems are designed to operate within a constrained environment, effective offloading strategies are required to prevent suboptimal performance resulting from incapable or overloaded embedded systems.

This thesis makes contributions to addressing the challenges associated with each of these research questions. First, I develop a practical methodology focused on cost-benefit quantification and a mathematical model to evaluate the data availability benefit of offloading data services into storage devices. Second, I examine and evaluate composable data services in high-performance scientific workflows to identify potential functions suitable for offloading. Finally, I explore strategies aimed at reducing data processing overhead and scheduling workloads dynamically to improve performance efficiency for data services running on embedded systems.