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Power modeling is an essential building block for computer systems in support of energy optimization, energy profiling, and energy-aware application development. We introduce VESTA, a novel approach to modeling the power consumption of applications with one key insight: language runtime events are often correlated with a sustained level of power consumption. When compared with the established approach of power modeling based on hardware performance counters (HPCs), VESTA has the benefit of solely requiring application-scoped information and enabling a higher level of explainability, while achieving comparable or even higher precision. Through experiments performed on 37 real-world applications on the Java Virtual Machine (JVM), we find the power model built by VESTA is capable of predicting energy consumption with a mean absolute percentage error of 1.56%, while the monitoring of language runtime events incurs small performance and energy overhead. 

The current techniques and tools for collecting, aggregating, and reporting verifiable sustainability data are vulnerable to cyberattacks and misuse, requiring new security and privacy-preserving solutions. This article outlines security challenges and research directions for addressing these requirements. 

Despite several calls from the community for improving the sustainability of computing, sufficient progress is yet to be made on one of the key prerequisites of sustainable computing---the ability to define and measure computing sustainability holistically. This position paper proposes metrics that aim to measure the end-to-end sustainability footprint in data centers. To enable useful sustainable computing efforts, these metrics can track the sustainability footprint at various granularities---from a single request to an entire data center. The proposed metrics can also broadly influence sustainable computing practices by incentivizing end-users and developers to participate in sustainable computing efforts in data centers.