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.

Trace replay is a common approach for evaluating systems by rerunning historical traffic patterns, but it is not always possible to find suitable real-world traces at the desired level of system load. Experimenting with higher traffic loads requires upscaling a trace to artificially increase the load. Unfortunately, most prior research has adopted ad-hoc approaches for upscaling, and there has not been a systematic study of how the upscaling approach impacts the results. One common approach is to count the arrivals in a predefined time-interval and multiply these counts by a factor, but this requires generating new requests/jobs according to some model (e.g., a Poisson process), which may not be realistic. Another common approach is to divide all the timestamps in the trace by an upscaling factor to squeeze the requests into a shorter time period. However, this can distort temporal patterns within the input trace. This paper evaluates the pros and cons of existing trace upscaling techniques and introduces a new approach, TraceUpscaler, that avoids the drawbacks of existing methods. The key idea behind TraceUpscaler is to decouple the arrival timestamps from the request parameters/data and upscale just the arrival timestamps in a way that preserves temporal patterns within the input trace. Our work applies to open-loop traffic where requests have arrival timestamps that aren't dependent on previous request completions. We evaluate TraceUpscaler under multiple experimental settings using both real-world and synthetic traces. Through our study, we identify the trace characteristics that affect the quality of upscaling in existing approaches and show how TraceUpscaler avoids these pitfalls. We also present a case study demonstrating how inaccurate trace upscaling can lead to incorrect conclusions about a system's ability to handle high load.

Realistic experimentation is a key component of systems research and industry prototyping, but experimental clusters are often too small to replay the high traffic rates found in production traces. Thus, it is often necessary to downscale traces to lower their arrival rate, and researchers/practitioners generally do this in an ad-hoc manner. For example, one practice is to multiply all arrival timestamps in a trace by a scaling factor to spread the load across a longer timespan. However, temporal patterns are skewed by this approach, which may lead to inappropriate conclusions about some system properties (e.g., the agility of auto-scaling). Another popular approach is to count the number of arrivals in fixed-sized time intervals and scale it according to some modeling assumptions. However, such approaches can eliminate or exaggerate the fine-grained burstiness in the trace depending on the time interval length.