Radu Nicolae

Radu Nicolae, Jules van der Toorn, Stavriana Kraniti et al.

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 of 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.

Radu Nicolae, Alexandru Iosup, Animesh Trivedi et al.

Large Language Models (LLMs) are widely used by our increasingly digitalized society, but raise sustainability, performance, and financial concerns, especially as inference workloads grow. To improve the design and operation of LLM ecosystems, we envision simulators and simulation-based digital twins becoming primary decision-making tools. LLM ecosystems leverage many heterogeneous components, making simulation a non-trivial, yet critical operation. The simulation challenge is exacerbated by the absence of a comprehensive reference architecture of LLM ecosystems; the lack of such a conceptual model can be costly and could misguide the designers and engineers. Without a reference architecture, even the most experienced stakeholders could tinker in researching, engineering, or maintaining LLM ecosystems. In this work, we bring a three-fold contribution to the scientific community. Firstly, we synthesize, propose, and validate a reference architecture (RA) of LLM ecosystems under inference. Then, adhering to the reference architecture, we design Kavier, the first simulation instrument able to predict the performance, sustainability, and efficiency of LLM ecosystems under inference, through discrete-event and cache-aware simulation, focusing on Key-Value-(KV-)Caching and prompt prefix caching policies. Through experiments with a Kavier prototype and real-world traces, (i) we measure the accuracy of Kavier and its performance in massive-scale simulations, (ii) we compare the performance of different KV-Caching policies, and (iii) we analyze the performance, sustainability, and efficiency of LLM ecosystems under various prefix caching policies. Overall, we show that Kavier enables operators, researchers, and engineers to predict LLM ecosystems in a time, performance, and cost-efficient way.

Radu Nicolae, Dante Niewenhuis, Sacheendra Talluri et al.

Datacenters are vital to our digital society, but consume a considerable fraction of global electricity and demand is projected to increase. To improve their sustainability and performance, we envision that simulators will become primary decision-making tools. However, and unlike other fields focusing on key societal infrastructure 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 simulation 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 multiple 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 show that (i) M3SA can reproduce and enhance peer-reviewed experiments; (ii) M3SA can predict operational phenomena (e.g., failures) of datacenters, running fundamentally different workload traces; (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 simulator OpenDC and is available at: https://github.com/atlarge-research/opendc-m3sa.