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we build a dataset for operation direct water usage in the cooling systems and indirect water embedded in electricity generation. Our dataset consists of the hourly water efficiency of major U.S. cities and states from 2019 to 2023. We also offer cooling system models that capture the impact of weather on water efficiency. We present a preliminary analysis of our dataset and discuss three potential applications that can benefit from it.

In this paper, we propose MPR (Market-based Power Reduction), a scalable market-based approach where users actively participate in reducing the HPC system’s power consumption to mitigate overloads. In MPR, HPC users bid to supply, in exchange for incentives, the resource reduction required for handling the overloads.

In this paper, we propose PEMA (Practical Efficient Microservice Autoscaling), a lightweight microservice resource manager that finds efficient resource allocation through opportunistic resource reduction. PEMA’s lightweight design enables novel workload-aware and adaptive resource management.

In this paper, we propose a deep Q-learning based task offloading solution, DeepTO, for online task offloading. DeepTO learns edge server performance in a model-free manner and takes future battery needs of the mobile device into account.

This paper describes a way to reduce the pick load selecting the victim consumer list and curtailing load in real time environment providing the consumer a minimum support rather than complete power cut off and thus optimizing the user satisfaction.