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.

Burstable instances provide a low-cost option for consumers using the public cloud, but they come with significant resource limitations. They can be viewed as "fractional instances" where one receives a fraction of the compute and memory capacity at a fraction of the cost of regular instances. The fractional compute is achieved via rate limiting, where a unique characteristic of the rate limiting is that it allows for the CPU to burst to 100% utilization for limited periods of time. Prior research has shown how this ability to burst can be used to serve specific roles such as a cache backup and handling flash crowds. Our work provides a general-purpose approach to meeting latency SLOs via this burst capability while optimizing for cost. AutoBurst is able to achieve this by controlling both the number of burstable and regular instances along with how/when they are used. Evaluations show that our system is able to reduce cost by up to 25% over the state-of-the-art while maintaining latency SLOs.

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.