The need to reduce datacenter carbon-footprint is urgent. While many sustainability techniques have been proposed, they are often evaluated in isolation, using limited setups or analytical models that overlook real-world dynamics and interactions between methods. This makes it challenging for researchers and operators to understand the effectiveness and trade-offs of combining such techniques. We design OpenDC-STEAM, an open-source customizable datacenter simulator, to investigate the individual and combined impact of sustainability techniques on datacenter operational and embodied carbon emissions, and their trade-off with performance. Using STEAM, we systematically explore three representative techniques-horizontal scaling, leveraging batteries, and temporal shifting-with diverse representative workloads, datacenter configurations, and carbon-intensity traces. Our analysis highlights that datacenter dynamics can influence their effectiveness and that combining strategies can significantly lower emissions, but introduces complex cost-emissions-performance trade-offs that STEAM can help navigate. STEAM supports the integration of new models and techniques, making it a foundation framework for holistic, quantitative, and reproducible research in sustainable computing. Following open-science principles, STEAM is available as FOSS: https://github.com/atlarge-research/OpenDC-STEAM.

Datacenters are vital for the digital society but represent a considerable fraction of global energy consumption. To improve their sustainability and performance when demand is foreseen to increase, we envision simulators will become primary decision-making tools. However, unlike other fields focusing on key societal infrastructure such as waterworks and mass transit, datacenter simulators cannot yet combine multiple, independent models into their operation. Addressing this challenge, in this work we propose M3SA, a datacenter simulation and analysis framework that uses discrete-event simulation to predict, per model and then combined into a meta-model, the impact on climate and performance of various realistic datacenter conditions. We design an architecture for simulating multiple concurrent models, a technique to integrate the results of multiple models into a meta-model, and a procedure to evaluate the accuracy of the meta-model. Through experiments with a prototype, we show that (i) M3SA can be used to reproduce peer-reviewed experiments, and enhance their output with more diverse metrics and more detailed analysis; (ii) M3SA can be configured with a variety of realistic parameters, such as diverse workload traces (using Grid Workloads Archive data in our experiments), and energy production data such as carbon intensity over time and location (gCO2/kWh data across all EU regions, from ENTSO-E); (iii) M3SA enables various types of what-if and how-to analysis, such as how to configure CO2-aware migration over yearly energy-production patterns. M3SA has been integrated into the open-source software simulator OpenDC and is available on https://github.com/atlarge-research/opendc-m3sa.

Cloud services are critical to society. However, their reliability is poorly understood. Towards solving the problem, we propose a standard repository for cloud uptime data. We populate this repository with the data we collect containing failure reports from users and operators of cloud services, web services, and online games. The multiple vantage points help reduce bias from individual users and operators. We compare our new data to existing failure data from the Failure Trace Archive and the Google cluster trace. We analyze the MTBF and MTTR, time patterns, failure severity, user-reported symptoms, and operator-reported symptoms of failures in the data we collect. We observe that high-level user facing services fail less often than low-level infrastructure services, likely due to them using fault-tolerance techniques. We use simulation-based experiments to demonstrate the impact of different failure traces on the performance of checkpointing and retry mechanisms. We release the data, and the analysis and simulation tools, as open-source artifacts available athttps://github.com/atlarge-research/cloud-uptime-archive.

Strongly Connected Components (SCCs) are useful for many applications, such as community detection and personalized recommendation. Determining the SCCs of a graph, however, can be very expensive, and parallelization is not an easy way out: the paral-lelization itself is challenging, and its performance impact varies non-trivially with the input graph structure. This variability is due to trivial components, i.e., SCCs consisting of a single vertex, which lead to significant workload imbalance. Trimming is an effective method to remove trivial components, but is inefficient when used on graphs with few trivial components.