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1. Treehouse: A Case For Carbon-Aware Datacenter Software

   https://doi.org/10.1145/3630614.3630626  

   Anderson, Thomas; Belay, Adam; Chowdhury, Mosharaf; Cidon, Asaf; Zhang, Irene (October 2023, ACM SIGEnergy Energy Informatics Review)

   The end of Dennard scaling and the slowing of Moore's Law has put the energy use of datacenters on an unsustainable path. Datacenters are already a significant fraction of worldwide electricity use, with application demand scaling at a rapid rate. We argue that substantial reductions in the carbon intensity of datacenter computing are possible with a software-centric approach: by making energy and carbon visible to application developers on a fine-grained basis, by modifying system APIs to make it possible to make informed trade offs between performance and carbon emissions, and by raising the level of application programming to allow for flexible use of more energy efficient means of compute and storage. We also lay out a research agenda for systems software to reduce the carbon footprint of datacenter computing. 

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2. WattScope: Non-intrusive application-level power disaggregation in datacenters

   https://doi.org/10.1016/j.peva.2023.102369  

   Guan, Xiaoding; Bashir, Noman; Irwin, David; Shenoy, Prashant (November 2023, Performance Evaluation)

   Datacenter capacity is growing exponentially to satisfy the increasing demand for many emerging computationally-intensive applications, such as deep learning. This trend has led to concerns over datacenters’ increasing energy consumption and carbon footprint. The most basic prerequisite for optimizing a datacenter’s energy- and carbon-efficiency is accurately monitoring and attributing energy consumption to specific users and applications. Since datacenter servers tend to be multi-tenant, i.e., they host many applications, server- and rack-level power monitoring alone does not provide insight into the energy usage and carbon emissions of their resident applications. At the same time, current application-level energy monitoring and attribution techniques are intrusive: they require privileged access to servers and necessitate coordinated support in hardware and software, neither of which is always possible in cloud environments. To address the problem, we design WattScope, a system for non-intrusively estimating the power consumption of individual applications using external measurements of a server’s aggregate power usage and without requiring direct access to the server’s operating system or applications. Our key insight is that, based on an analysis of production traces, the power characteristics of datacenter workloads, e.g., low variability, low magnitude, and high periodicity, are highly amenable to disaggregation of a server’s total power consumption into application-specific values. WattScope adapts and extends a machine learning-based technique for disaggregating building power and applies it to server- and rack-level power meter measurements that are already available in data centers. We evaluate WattScope’s accuracy on a production workload and show that it yields high accuracy, e.g., often 10% normalized mean absolute error, and is thus a potentially useful tool for datacenters in externally monitoring application-level power usage. 

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3. The Sunk Carbon Fallacy: Rethinking Carbon Footprint Metrics for Effective Carbon-Aware Scheduling

   https://doi.org/10.1145/3698038.3698542  

   Bashir, Noman; Gohil, Varun; Subramanya, Anagha Belavadi; Shahrad, Mohammad; Irwin, David; Olivetti, Elsa; Delimitrou, Christina (November 2024, ACM)

   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. In this paper, we evaluate carbon-aware job scheduling and placement on a given set of servers for several carbon accounting metrics. Our analysis reveals state-of-the-art carbon accounting metrics that include embodied carbon emissions when making operational decisions can increase the total carbon footprint of executing a set of jobs. We study the factors that affect the added carbon cost of such suboptimal decision-making. We then use a real-world case study from a datacenter to demonstrate how the sunk carbon fallacy manifests itself in practice. Finally, we discuss the implications of our findings in better guiding effective carbon-aware scheduling in on-premise and cloud datacenters. 

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4. Towards Improved Power Management in Cloud GPUs

   https://doi.org/10.1109/LCA.2023.3278652  

   Patel, Pratyush; Gong, Zibo; Rizvi, Syeda; Choukse, Esha; Misra, Pulkit; Anderson, Thomas; Sriraman, Akshitha (July 2023, IEEE Computer Architecture Letters)

   As modern server GPUs are increasingly power intensive, better power management mechanisms can significantly reduce the power consumption, capital costs, and carbon emissions in large cloud datacenters. This letter uses diverse datacenter workloads to study the power management capabilities of modern GPUs. We find that current GPU management mechanisms have limited compatibility and monitoring support under cloud virtualization. They have sub-optimal, imprecise, and non-intuitive implementations of Dynamic Voltage and Frequency Scaling (DVFS) and power capping. Consequently, efficient GPU power management is not widely deployed in clouds today. To address these issues, we make actionable recommendations for GPU vendors and researchers. 

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5. When Green Computing Meets Performance and Resilience SLOs.

   https://doi.org/10.1109/DSN-S60304.2024  

   Qiu, H; Mao, W; Wang, C; Jha, S; Franke, H; Narayanaswami, C; Kalbarczyk, ZT; Basar, T; Iyer, R_K (January 2024, Institute of Electrical and Electronics Engineers)

   nd (Ed.)

   This paper addresses the urgent need to transition to global net-zero carbon emissions by 2050 while retaining the ability to meet joint performance and resilience objectives. The focus is on the computing infrastructures, such as hyperscale cloud datacenters, that consume significant power, thus producing increasing amounts of carbon emissions. Our goal is to (1) optimize the usage of green energy sources (e.g., solar energy), which is desirable but expensive and relatively unstable, and (2) continuously reduce the use of fossil fuels, which have a lower cost but a significant negative societal impact. Meanwhile, cloud datacenters strive to meet their customers’ requirements, e.g., service-level objectives (SLOs) in application latency or throughput, which are impacted by infrastructure resilience and availability. We propose a scalable formulation that combines sustainability, cloud resilience, and performance as a joint optimization problem with multiple interdependent objectives to address these issues holistically. Given the complexity and dynamicity of the problem, machine learning (ML) approaches, such as reinforcement learning, are essential for achieving continuous optimization. Our study highlights the challenges of green energy instability which necessitates innovative MLcentric solutions across heterogeneous infrastructures to manage the transition towards green computing. Underlying the MLcentric solutions must be methods to combine classic system resilience techniques with innovations in real-time ML resilience (not addressed heretofore). We believe that this approach will not only set a new direction in the resilient, SLO-driven adoption of green energy but also enable us to manage future sustainable systems in ways that were not possible before. 

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