Iridescent: A Framework Enabling Online System Implementation Specialization
Vaastav Anand, Deepak Garg, Antoine Kaufmann

TL;DR
Iridescent is a framework that automates online system specialization by dynamically generating and testing different configurations guided by system performance metrics, improving performance adaptability.
Contribution
It introduces a novel framework for automated online system specialization that leverages runtime performance metrics to optimize configurations dynamically.
Findings
Feasibility demonstrated through implementation
Effective in improving system performance
Easy for developers to specify specialization space
Abstract
Specializing systems to specifics of the workload they serve and platform they are running on often significantly improves performance. However, specializing systems is difficult in practice because of compounding challenges: i) complexity for the developers to determine and implement optimal specialization; ii) inherent loss of generality of the resulting implementation, and iii) difficulty in identifying and implementing a single optimal specialized configuration for the messy reality of modern systems. To address this, we introduce Iridescent, a framework for automated online system specialization guided by observed overall system performance. Iridescent lets developers specify a space of possible specialization choices, and then at runtime generates and runs different specialization choices through JIT compilation as the system runs. By using overall system performance metrics to…
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