Using machine learning (ML) to tackle computer systems tasks is gaining popularity. One of the shortcomings of such ML-based approaches is the inability of models to generalize to out-ofdistribution data i.e., data whose distribution is different than the training dataset. We showcase that this issue exists in cloud environments by analyzing various ML models used to improve resource balance in Google’s fleet. We discuss the trade-offs associated with different techniques used to detect out-of-distribution data. Finally, we propose and demonstrate the efficacy of using Bayesian models to detect the model’s confidence in its output when used to improve cloud server resource balance.

The rapid increase in computing demand and corresponding energy consumption have focused attention on computing's impact on the climate and sustainability. Prior work proposes metrics that quantify computing's carbon footprint across several lifecycle phases, including its supply chain, operation, and end-of-life. Industry uses these metrics to optimize the carbon footprint of manufacturing hardware and running computing applications. Unfortunately, prior work on optimizing datacenters' carbon footprint often succumbs to the sunk cost fallacy by considering embodied carbon emissions (a sunk cost) when making operational decisions (i.e., job scheduling and placement), which leads to operational decisions that do not always reduce the total carbon footprint.

Handheld devices are becoming increasingly common in today's world. With every passing year, the diversity of applications (apps) being supported by mobile platforms is growing manyfold. In addition, Android, the most popular handheld OS in the market is releasing a new version every year, with a newer and richer set of APIs, enabling the next generation of feature-rich applications. To support these apps, hardware requirements of these devices are increasing rapidly. Mobile SoCs need a larger number of faster cores, better GPUs and above all, higher DRAM capacities to do justice to user experience. To augment capacity requirements, non-volatile memories (NVMs) have been proposed as a potential addition to LPDDR variants, which have been the mainstay of mobile SoCs. However, few tools exist to carry out architectural design space exploration of main memory hierarchies featuring NVMs for newer Android and app versions. In this paper, we present META, a trace based tool for facilitating the exploration of memory hierarchies in mobile devices. META uses an enhanced version of Android emulator for generating raw instruction traces. These traces are then fed into a cache hierarchy and memory simulation modules to carry out design space exploration for a wide variety of apps and memory technologies.