Profiling Distributed Virtual Environments by Tracing Causality

Real-time interactive systems such as virtual environments have high performance requirements, and profiling is a key part of the optimisation process to meet them. Traditional techniques based on metadata and static analysis have difficulty following causality in asynchronous systems. In this paper we explore a new technique for such systems. Timestamped samples of the system state are recorded at instrumentation points at runtime. These are assembled into a graph, and edges between dependent samples recovered. This approach minimises the invasiveness of the instrumentation, while retaining high accuracy. We describe how our instrumentation can be implemented natively in common environments, how its output can be processed into a graph describing causality, and how heterogeneous data sources can be incorporated into this to maximise the scope of the profiling. Across three case studies, we demonstrate the efficacy of this approach, and how it supports a variety of metrics for comprehensively bench-marking distributed virtual environments.