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2. Learning-NUM: Network Utility Maximization with Unknown Utility Functions and Queueing Delay

   https://doi.org/10.1145/3466772.3467031  

   Fu, Xinzhe; Modiano, Eytan (July 2021, ACM MobiHoc)

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3. Approximate Expected Utility Rationalization

   https://doi.org/10.1093/jeea/jvad028  

   Echenique, Federico; Imai, Taisuke; Saito, Kota (April 2023, Journal of the European Economic Association)

   Abstract We propose a new measure of deviations from expected utility theory. For any positive number e, we give a characterization of the datasets with a rationalization that is within e (in beliefs, utility, or perceived prices) of expected utility (EU) theory, under the assumption of risk aversion. The number e can then be used as a measure of how far the data is to EU theory. We apply our methodology to data from three large-scale experiments. Many subjects in these experiments are consistent with utility maximization, but not with EU maximization. Our measure of distance to expected utility is correlated with the subjects’ demographic characteristics. 

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4. Contextual factors affecting hint utility

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5. Unleashing True Utility Computing with Quicksand

   https://doi.org/10.1145/3593856.3595893  

   Ruan, Zhenyuan; Li, Shihang; Fan, Kaiyan; Aguilera, Marcos K.; Belay, Adam; Park, Seo Jin; Schwarzkopf, Malte (June 2023, ACM)

   Today's clouds are inefficient: their utilization of resources like CPUs, GPUs, memory, and storage is low. This inefficiency occurs because applications consume resources at variable rates and ratios, while clouds offer resources at fixed rates and ratios. This mismatch of offering and consumption styles prevents fully realizing the utility computing vision. We advocate for fungible applications, that is, applications that can distribute, scale, and migrate their consumption of different resources independently while fitting their availability across different servers (e.g., memory at one server, CPU at another). Our goal is to make use of resources even if they are transiently available on a server for only a few milliseconds. We are developing a framework called Quicksand for building such applications and unleashing the utility computing vision. Initial results using Quicksand to implement a DNN training pipeline are promising: Quicksand saturates resources that are imbalanced across machines or rapidly shift in quantity. 

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