Akshitha Sriraman

To combat climate change, we must reduce carbon emissions from hyperscale cloud computing. We find that compute servers cause the majority of a general-purpose cloud’s emissions. Thus, we motivate designing carbon-efficient compute server Stock Keeping Units (SKUs), or GreenSKUs, using recently-available low-carbon components. We build three GreenSKU prototypes, integrating energy-efficient CPUs, reused old DRAM via CXL, and reused old SSDs. We reveal challenges that limit GreenSKUs’ carbon savings at scale and may prevent their adoption by cloud providers. To address these challenges, we develop a novel framework, GSF (GreenSKU Framework), that enables cloud providers to systematically evaluate GreenSKUs’ carbon savings at scale. By implementing GSF within Microsoft Azure’s production constraints, we demonstrate that GreenSKUs reduce net cloud emissions by 8%, which is globally significant. This work is the first to demonstrate and quantify how carbon-efficient server designs translate to measurable cloud-scale emissions reductions, enabling meaningful contributions to cloud sustainability goals.

The Internet today is owned, managed and controlled by a heterogeneous mix of autonomous systems. As a result, there's no one single entity that holds the "Internet kill switch". However, for emerging Low-Earth Orbit satellite Internet services, few gatekeepers control access globally, going against the fundamental principle of the Internet as a distributed and decentralized system. While satellite Internet remains a small part of the Internet today, it is growing exponentially and is often the only connectivity option for regions that are sparsely populated, experience political instability, or are prone to natural disasters that are likely to damage equipment. We first discuss why the satellite Internet world is ripe for monopolies, global ownership, and vertical integration. We then lay out OpenSpace, an architectural roadmap for a more open and heterogeneous satellite Internet paradigm, where many players build, launch, and manage satellites that communicate, to collectively deliver a reliable Internet service. We also discuss several open problems and research challenges in making satellite Internet more interoperable and heterogeneous, facilitating accessibility for big and small firms alike.

In recent decades, software systems have grown significantly in size and complexity. As a result, such systems are more prone to bugs which can cause performance and correctness challenges. Using run-time monitoring tools is one approach to mitigate these challenges. However, these tools maintain metadata for every byte of application data they monitor, which precipitates performance overheads from additional metadata accesses. We propose Address Scaling , a new hardware framework that performs fine-grained metadata management to reduce metadata access overheads in run-time monitoring tools. Our mechanism is based on the observation that different run-time monitoring tools maintain metadata at varied granularities. Our key insight is to maintain the data and its corresponding metadata within the same cache line, to preserve locality. Address Scaling improves the performance of Memcheck , a dynamic monitoring tool that detects memory-related errors, by 3.55× and 6.58× for sequential and random memory access patterns respectively, compared to the state-of-the-art systems that store the metadata in a memory region that is separate from the data.

Modern web services run across hundreds of thousands of servers in a data center, i.e., at hyperscale. With the end of Moore's Law and Dennard scaling, successive server generations running these web services exhibit diminishing performance returns, resulting in architects adopting hardware customization. An important question arises: Which web service software operations are worth building custom hardware for To answer this question, we comprehensively analyze seven important Facebook production services and identify key acceleration opportunities. We then develop an open-source analytical model, Accelerometer, to help make well-informed hardware decisions for the acceleration opportunities we identify.