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Abstract Proceedings of the 2022 ACM SIGMETRICS/IFIP PERFORMANCE Joint International Conference on Measurement and Modeling of Computer Systems
Mean field approximation is a powerful technique to study the performance of large stochastic systems represented as 𝑛 interacting objects. Applications include load balancing models, epidemic spreading, cache replacement policies, or large-scale data centers. Mean field approximation is asymptotically exact for systems composed of 𝑛 homogeneous objects under mild conditions. In this paper, we study what happens when objects are heterogeneous. This can represent servers with different speeds ordoi:10.1145/3489048.3522653 fatcat:l2lfv5r2tveh7odgvfex4yqot4