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1. Managing Allocatable Resources

   Keahey, Kate; Riteau, Pierre; Anderson, Jason; Zhen, Zhuo (January 2019, IEEE ... International Conference on Cloud Computing)

   Infrastructure cloud computing allows its clients to allocate on-demand resources, typically consisting of a repre- sentation of a compute node. In general however, there is a need for allocating resources other than nodes and managing them in more controlled ways than simply on demand. This paper generalizes the familiar “compute power on demand” pattern by introducing the abstraction of an allocatable resource, describing its properties, and implementation for different types of resources. We further describe architecture for a generic allocatable resource management service that can be extended to manage diverse types of resources as well as the implementation of this architecture in the OpenStack Blazar service to manage resources ranging from bare-metal compute nodes to network segments. Finally, we provide a usage analysis of this service on the Chameleon testbed and use it to illustrate the effectiveness of resource management methods as well as the need for incentives in usage arbitration. 

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2. Online Fair Allocation of Perishable Resources

   https://doi.org/10.1145/3606376.3593558  

   Banerjee, Siddhartha; Hssaine, Chamsi; Sinclair, Sean R (June 2023, ACM SIGMETRICS Performance Evaluation Review)

   We consider a practically motivated variant of the canonical online fair allocation problem: a decision-maker has a budget of resources to allocate over a fixed number of rounds. Each round sees a random number of arrivals, and the decision-maker must commit to an allocation for these individuals before moving on to the next round. In contrast to prior work, we consider a setting in which resources are perishable and individuals' utilities are potentially non-linear (e.g., goods exhibit complementarities). The goal is to construct a sequence of allocations that is envy-free and efficient. We design an algorithm that takes as input (i) a prediction of the perishing order, and (ii) a desired bound on envy. Given the remaining budget in each period, the algorithm uses forecasts of future demand and perishing to adaptively choose one of two carefully constructed guardrail quantities. We characterize conditions under which our algorithm achieves the optimal envy-efficiency Pareto frontier. We moreover demonstrate its strong numerical performance using data from a partnering food bank. 

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3. Students’ Scientific Evaluations of Water Resources

   https://doi.org/10.3390/w12072048  

   Medrano, Josh; Jaffe, Joshua; Lombardi, Doug; Holzer, Margaret A.; Roemmele, Christopher (July 2020, Water)

   Socially-relevant and controversial topics, such as water issues, are subject to differences in the explanations that scientists and the public (herein, students) find plausible. Students need to be more evaluative of the validity of explanations (e.g., explanatory models) based on evidence when addressing such topics. We compared two activities where students weighed connections between lines of evidence and explanations. In one activity, students were given four evidence statements and two models (one scientific and one non-scientific alternative); in the other, students chose four out of eight evidence statements and three models (two scientific and one non-scientific). Repeated measures analysis of variance (ANOVA) showed that both activities engaged students’ evaluations and differentially shifted students’ plausibility judgments and knowledge. A structural equation model suggested that students’ evaluation may influence post-instructional plausibility and knowledge; when students chose their lines of evidence and explanatory models, their evaluations were deeper, with stronger shifts toward a scientific stance and greater levels of post-instructional knowledge. The activities may help to develop students’ critical evaluation skills, a scientific practice that is key to understanding both scientific content and science as a process. Although effect sizes were modest, the results provided critical information for the final development and testing stage of these water resource instructional activities. 

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4. Editorial: Horticultural genetic resources

   https://doi.org/10.3389/fpls.2023.1264597  

   Farajpour, Mostafa; Arab, Mohammad M.; Katam, Ramesh; Sadat-Hosseini, Mohammad (August 2023, Frontiers in Plant Science)
5. Mining resources from water

   https://doi.org/10.1016/j.resconrec.2021.105853  

   Lin, Shihong; Hatzell, Marta; Liu, Ruiping; Wells, George; Xie, Xing (December 2021, Resources, Conservation and Recycling)

   null (Ed.)