Publications

On this page we list publications related to OpenDC or publications that have used OpenDC in their work.

Publications about OpenDC

1. OpenDC 2.0: Convenient Modeling and Simulation of Emerging Technologies in Cloud Datacenters
   CCGrid, 2021
   Fabian Mastenbroek, Georgios Andreadis, Soufiane Jounaid, Wenchen Lai, Jacob Burley, Jaro Bosch, Erwin van Eyk, Laurens Versluis, Vincent van Beek, Alexandru Iosup
   Abstract

   Cloud datacenters are important for the digital society, serving stakeholders across industry, government, and academia. Simulation is a critical part of exploring datacenter technologies, enabling scalable experimentation with millions of jobs and hundreds of thousands of machines, and what-if analysis in a matter of minutes to hours. Although the community has already developed powerful simulators, emerging technologies and applications in modern datacenters require new approaches. Addressing this requirement, in this work we propose OpenDC, a new platform for datacenter simulation. OpenDC includes novel models for emerging cloud-datacenter technologies and applications, such as serverless computing with FaaS deploy- ment and TensorFlow-based machine learning. Our design also focuses on convenience, with a web-based interface for interactive experimentation, support for experiment automation, a library of prefabs for constructing and sharing datacenter designs, and sup- port for diverse input formats and output metrics. We implement, validate, and open-source OpenDC 2.0, a significant redesign and release after a multi-year research and development process. We demonstrate the benefits of OpenDC for the field through a set of representative use-cases: serverless, machine learning, procure- ment of HPC-as-a-Service infrastructure, educational practices, and reproducibility studies. Overall, OpenDC helps understand how datacenters work, design datacenter infrastructure, and train the next generation of experts.

2. The OpenDC vision: Towards collaborative datacenter simulation and exploration for everybody
   ISPDC, 2017
   Alexandru Iosup, Georgios Andreadis, Vincent van Beek, Matthijs Bijman, Erwin van Eyk, Mihai Neacsu, Leon Overweel, Sacheendra Talluri, Laurens Versluis, Maaike Visser
   Abstract

   In the new Digital Economy, massive computer sys- tems, often grouped in datacenters, serve as factories “producing” cloud services with massive consumption. However, to afford cloud services globally, we must address new research challenges in designing, operating, and using modern datacenters. We must also address challenges in educating and training the next generation of datacenter engineers. Addressing such challenges, in this work we present our vision on OpenDC: we envision the exploration of various datacenter concepts and technologies, using existing and new scientific methods, enabling new education practices and topics, and leading to the creation of new software and data artifacts. We present the datacenter concepts and technologies we are currently planning to explore using OpenDC. We identify the scientific methods we want to use, and explain our vision of education practices. We present the architecture and open-source program underlying the OpenDC software, and the format and open-access data we use for datacenter experiments. We conclude with an open invitation for the community to join our effort.

Publications using OpenDC

1. M3SA: Exploring Datacenter Performance and Climate-Impact with Multi- and Meta-Model Simulation and Analysis
   Computing Frontiers, 2026
   Radu Nicolae, Dante Niewenhuis, Sacheendra Talluri, Alexandru Iosup
   Abstract

   Datacenters are vital to our digital society, but consume a con- siderable fraction of global electricity and demand is projected to increase. To improve their sustainability and performance, we en- vision that simulators will become primary decision-making tools. However, and unlike other fields focusing on key societal infrastruc- ture such as waterworks and mass transit, datacenter simulators do not yet combine multiple independent models into their operation and thus suffer from issues associated with singular models, such as specialization, and lack of adaptability to operational phenomena. To address this challenge, we propose M3SA, a datacenter simula- tion and analysis framework that uses discrete-event simulation to predict, for each model, the impact on climate and performance under various realistic datacenter conditions, and then combines these predictions. We design an architecture for simulating multi- ple concurrent models (Multi-Model), a technique for integrating the results of multiple models into a Meta-Model, and a procedure for quantifying Meta-Model accuracy. Through experiments with an M3SA prototype, we reproduce and enhance a peer-reviewed experiment with multi-model analysis and identify that M3SA can halve error rates of singular models (from 7.59% to 3.81%), with under 20% computational overhead.

2. OpenDC-STEAM: Realistic Modeling and Systematic Exploration of Composable Techniques for Sustainable Datacenters
   CCGrid, 2026
   Dante Niewenhuis, Sacheendra Talluri, Alexandru Iosup, Tiziano de Matteis
   Abstract

   The need to reduce datacenter carbon-footprint is urgent. While many sustainability techniques have been pro- posed, 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 emis- sions, 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 nav- igate. 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. This is an extended version of a paper published at CCGRID 2026.

3. OpenDT: Exploring Datacenter Performance and Sustainability with a Self-Calibrating Digital Twin
   HotCloudPerf, 2026
   Radu Nicolae, Jules van der Toorn, Stavriana Kraniti, Houcen Liu, Alexandru Iosup
   Abstract

   Datacenters are the backbone of our digital society, but raise numerous operational challenges. We envision digital twins becoming primary instruments in datacenter operations, continuously and autonomously helping with major operational decisions and with adapting ICT infrastructure, live, with a human-in-the-loop. Although fields such as aviation and autonomous driving successfully employ digital twins, an open-source digital twin for datacenters has not been demonstrated to the community. Addressing this challenge, we design, implement, and experiment using OpenDT, an Open-source, Digital Twin for monitoring and operating datacenters through a continuous integration cycle that includes: (1) live and continuous telemetry data; (2) discrete-event simulation using live telemetry from the physical ICT, with self-calibration; and (3) SLO-aware and human-approved feedback to physical ICT. Through trace-driven experiments with a prototype mainly covering stages 1 and 2 of the cycle, we show that (i) OpenDT can be used to reproduce peer-reviewed experiments and extend the analysis with performance and energy-efficiency results; (ii) OpenDT's online re-calibration can increase digital-twinning accuracy, quantified to a MAPE 4.39% vs. 7.86% in peer-reviewed work. OpenDT adheres to FAIR/FOSS principles and is available at: https://github.com/atlarge-research/opendt/tree/hcp

4. Cloud Uptime Archive: Open-Access Availability Data of Web, Cloud, and Gaming Services
   TPDS, 2026
   Sacheendra Talluri, Dante Niewenhuis, Xiaoyu Chu, Jakob Kyselica, Mehmet Cetin, Alexander Balgavy, Alexandru Iosup
   Abstract

   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 sever- ity, 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 at https://github.com/atlarge- research/cloud-uptime-archive.

5. FootPrinter: Quantifying Data Center Carbon Footprint
   HotCloudPerf, 2024
   Dante Niewenhuis, Sacheendra Talluri, Alexandru Iosup, Tiziano de Matteis
   Abstract

   Data centers have become an increasingly significant contributor to the global carbon footprint. In 2021, the global data center industry was responsible for around 1% of the worldwide greenhouse gas emissions. With more resource-intensive workloads, such as Large Language Models, gaining popularity, this percentage is expected to increase further. Therefore, it is crucial for data center service providers to become aware of and accountable for the sustainability impact of their design and operational choices. However, reducing the carbon footprint of data centers has been a challenging process due to the lack of comprehensive metrics, carbon-aware design tools, and guidelines for carbon-aware optimization. In this work, we propose FootPrinter, a first-of-its-kind tool that supports data center designers and operators in assessing the environmental impact of their data center. FootPrinter uses coarse-grained operational data, grid energy mix information, and discrete event simulation to determine the data center's operational carbon footprint and evaluate the impact of infrastructural or operational changes. FootPrinter can simulate days of operations of a regional data center on a commodity laptop in a few seconds, returning the estimated footprint with marginal error. By making this project open source, we hope to engage the community in the development of methodologies and tools for systematically assessing and exploring the sustainability of data centers.

6. Capelin: Data-Driven Compute Capacity Procurement for Cloud Datacenters Using Portfolios of Scenarios
   IEEE Transactions on Parallel and Distributed Systems (TPDS), 2022
   Georgios Andreadis, Fabian Mastenbroek, Vincent van Beek, Alexandru Iosup
   Abstract

   Cloud datacenters provide a backbone to our digital society. Inaccurate capacity procurement for cloud datacenters can lead to significant performance degradation, denser targets for failure, and unsustainable energy consumption. Although this activity is core to improving cloud infrastructure, relatively few comprehensive approaches and support tools exist for mid-tier operators, leaving many planners with merely rule-of-thumb judgement. We derive requirements from a unique survey of experts in charge of diverse datacenters in several countries. We propose Capelin, a data-driven, scenario-based capacity planning system for mid-tier cloud datacenters. Capelin introduces the notion of portfolios of scenarios, which it leverages in its probing for alternative capacity-plans. At the core of the system, a trace-based, discrete-event simulator enables the exploration of different possible topologies, with support for scaling the volume, variety, and velocity of resources, and for horizontal (scale-out) and vertical (scale-up) scaling. Capelin compares alternative topologies and for each gives detailed quantitative operational information, which could facilitate human decisions of capacity planning. We implement and open-source Capelin, and show through comprehensive trace-based experiments it can aid practitioners. The results give evidence that reasonable choices can be worse by a factor of 1.5-2.0 than the best, in terms of performance degradation or energy consumption.

7. A Reference Architecture for Datacenter Scheduling
   SC, 2018
   Georgios Andreadis, Laurens Versluis, Fabian Mastenbroek, Alexandru Iosup
   Abstract

   Datacenters act as cloud-infrastructure to stakehold- ers across industry, government, and academia. To meet growing demand yet operate efficiently, datacenter operators employ increasingly more sophisticated scheduling systems, mechanisms, and policies. Although many scheduling techniques already exist, relatively little research has gone into the abstraction of the scheduling process itself, hampering design, tuning, and comparison of existing techniques. In this work, we propose a reference architecture for datacenter schedulers. The architecture follows five design principles: components with clearly distinct responsibilities, grouping of related components where possible, separation of mechanism from policy, scheduling as complex workflow, and hierarchical multi-scheduler structure. To demon- strate the validity of the reference architecture, we map to it state- of-the-art datacenter schedulers. We find scheduler-stages are commonly underspecified in peer-reviewed publications. Through trace-based simulation and real-world experiments, we show underspecification of scheduler-stages can lead to significant variations in performance.