Systems Research Group – Redmond
Projets
Établi:
Formal verification is a promising approach to eliminate bugs at compile time, before software ships. Unfortunately, verifying the correctness of system software traditionally requires heroic developer effort. In this project, we aim to enable accessible, faster, cheaper verification of rich properties…
The Zissou project is exploring immersion cooling in large-scale cloud platforms. Our main motivation is that chip power has been steadily increasing since the end of Dennard scaling.
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Function as a Service (FaaS) is a software paradigm that is becoming increasingly popular. Multiple cloud providers offer FaaS as the interface to usage-driven, stateless (serverless) backend services. FaaS offers an intuitive, event-based interface for developing cloud-based applications. In contrast…
Project Everest aims to build and deploy a verified HTTPS stack, constructing a high-performance, standards-compliant, and verified implementation of the full HTTPS ecosystem.
The Demikernel is a new library OS architecture for kernel-bypass I/O in datacenter servers. The Demikernel defines a new kernel-bypass I/O abstraction and uses library OSes to flexibly provide that abstraction across different kernel-bypass devices (e.g., DPDK, RDMA). This standardized,…
Project Prometheus is building faster, more efficient datacenter systems by co-designing distributed systems with new network primitives. Prometheus takes advantage of new programmable hardware to accelerate applications.
Power Capping and Oversubscription is a collaboration between MSR, Azure Compute, CO+I, and AHSI to harvest stranded datacenter resources via smart performance-aware power capping and oversubscription.
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No public cloud can host large latency-sensitive services, such as search engines, in a way that is economic for those services today! Project LEAP (short for Lean, Efficient, And Predictable) addresses the research challenges in enabling cloud platforms to host…
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Resource Central is a general ML and prediction-serving system deployed in Azure Compute. It trains ML models offline and uses them to produce predictions online.