Software Infrastructure for Isolation and Performance Monitoring in Virtualized Systems

Abstract

Modern multiprocessor System-on-Chip (SoC) architectures host a rich tapestry of heterogeneous components, enabling multiple workloads with differing requirements to run simultaneously on the same hardware platform. However, managing and isolating these concurrently running applications presents significant challenges. Traditional virtualization techniques, even with static partitioning hypervisors, could struggle to ensure robust isolation due to contention in shared system resources such as caches and memory bandwidth. To address this issue, this thesis investigates memory bandwidth contention among cores and explores isolation strategies by implementing MemGuard in the Bao Hypervisor on ARMv8-based systems. This implementation is complemented by cache coloring and DRAM bank partitioning techniques. The results, evaluated using the San Diego Vision Benchmark Suite, quantify the effectiveness of these mechanisms in reducing interference and provide insights into program behavior under varying isolation parameters.

Beyond improving isolation, performance monitoring must extend beyond core-level observation to encompass system-wide interactions. To this end, this thesis develops a comprehensive software infrastructure for an Advanced Performance Monitoring Unit (APMU), designed for event-driven monitoring and dynamic runtime reconfiguration. By leveraging an LLVM-based toolchain to support custom instructions and integrating seamlessly with the hypervisor and guest OS layers, the APMU framework enables diverse applications while optimizing memory utilization and execution time.

Collectively, the results and infrastructure presented in this work contribute to more predictable, secure, and efficient computing systems, advancing the state of the art in virtualization, performance isolation, and heterogeneous system analysis.