A Framework for SLO, Carbon, and Wastewater-Aware Sustainable FaaS Cloud Platform Management

Function-as-a-Service (FaaS) is a growing cloud computing paradigm that is expected to reduce the user cost of service over traditional serverful approaches. However, the environmental impact of FaaS has not received much attention. We investigate FaaS scheduling and scaling from a sustainability perspective in this work. We find that the service-level objectives (SLOs) of FaaS and carbon emissions conflict with each other. We also find that SLO-focused FaaS scheduling can exacerbate water use in a datacenter. We propose a novel sustainability-focused FaaS scheduling and scaling framework to co-optimize SLO performance, carbon emissions, and wastewater generation.

The SaTML '24 CNN Interpretability Competition: New Innovations for Concept-Level Interpretability

Interpretability techniques are valuable for helping humans understand and oversee AI systems. The SaTML 2024 CNN Interpretability Competition solicited novel methods for studying convolutional neural networks (CNNs) at the ImageNet scale. The objective of the competition was to help human crowd-workers identify trojans in CNNs. This report showcases the methods and results of four featured competition entries. It remains challenging to help humans reliably diagnose trojans via interpretability tools. However, the competition's entries have contributed new techniques and set a new record on the benchmark from Casper et al., 2023.

Adaptability of Computer Vision at the Tactical Edge: Addressing Environmental Uncertainty

Computer Vision (CV) systems are increasingly being adopted into Command and Control (C2) systems to improve intelligence analysis on the battlefield, the tactical edge. CV systems leverage Artificial Intelligence (AI) algorithms to help visualize and interpret the environment, enhancing situational awareness. However, the adaptability of CV systems at the tactical edge remains challenging due to rapidly changing environments and objects which can confuse the deployed models. A CV model leveraged in this environment can become uncertain in its predictions, as the environment and the objects existing in the environment begin to change. Additionally, mission objectives can rapidly change leading to adjustments in technology, camera angles, and image resolutions. All of which can negatively affect the performance of and potentially introduce uncertainty into the system. When the training environment and/or technology differs from the deployment environment, CV models can perform unexpectedly. Unfortunately, most scenarios at the tactical edge do not incorporate Uncertainty Quantification (UQ) into their deployed C2 and CV systems. This concept paper explores the idea of synchronizing robust data operations and model fine-tuning driven by UQ all at the tactical edge. Specifically, curating datasets and training child models based on the residuals of predictions, using these child models to calculate prediction intervals (PI), and then using these PI to calibrate the deployed models. By incorporating UQ into the core operations surrounding C2 and CV systems at the tactical edge, we can help drive purposeful adaptability on the battlefield.