Trace replay is a common approach for evaluating systems by rerunning historical traffic patterns, but it’s not always possible to find suitable real-world traces at the desired level of system load. To experiment with different loads, one needs to downscale a trace to decrease the load or upscale a trace to artificially increase the load. This article expands upon our work, TraceUpscaler [ 92 ], by considering the interaction of upscaling and downscaling. In addition to evaluating upscaling with traces collected from a subset of the cluster, we also evaluate upscaling with traces that were downscaled with the state-of-the-art downscaling tool, TraceSplitter [ 91 ], to demonstrate that the upscaling and downscaling techniques are compatible and do not introduce unexpected artifacts in the scaling. In addition to comparing against prior approaches, we develop a novel upscaling technique, TraceOverlap , based on the idea of overlapping different time periods in a trace, where we identify the most similar time periods to overlap. Our evaluation demonstrates that TraceUpscaler and TraceOverlap are both more accurate in maintaining latency characteristics than prior approaches, with TraceUpscaler matching the original trace latency more closely. Finally, we provide a unified framework, TraceScaler , that combines TraceUpscaler with TraceSplitter to provide experimenters a common tool for their trace scaling needs.