DR-STRaNGe: End-to-End System Design for DRAM-based True Random Number Generators

Abstract

Random number generation is an important task in a wide variety of critical applications including cryptographic algorithms, scientific simulations, and industrial testing tools. True Random Number Generators (TRNGs) produce truly random data by sampling a physical entropy source that typically requires custom hardware and suffers from long latency. To enable high-bandwidth and low-latency TRNGs on commodity devices, recent works propose TRNGs that use DRAM as an entropy source. Although prior works demonstrate promising DRAM-based TRNGs, integration of such mechanisms into real systems poses challenges. We identify three challenges for using DRAM-based TRNGs in current systems: (1) generating random numbers can degrade system performance by slowing down concurrently-running applications due to the interference between RNG and regular memory operations in the memory controller (i.e., RNG interference), (2) this RNG interference can degrade system fairness by unfairly prioritizing applications that intensively use random numbers (i.e., RNG applications), and (3) RNG applications can experience significant slowdowns due to the high RNG latency. We propose DR-STRaNGe, an end-to-end system design for DRAM-based TRNGs that (1) reduces the RNG interference by separating RNG requests from regular requests in the memory controller, (2) improves the system fairness with an RNG-aware memory request scheduler, and (3) hides the large TRNG latencies using a random number buffering mechanism with a new DRAM idleness predictor that accurately identifies idle DRAM periods. We evaluate DR-STRaNGe using a set of 186 multiprogrammed workloads. Compared to an RNG-oblivious baseline system, DR-STRaNGe improves the average performance of non-RNG and RNG applications by 17.9% and 25.1%, respectively. DR-STRaNGe improves average system fairness by 32.1% and reduces average energy consumption by 21%.