Systems & networking
Researching and inventing new technologies for next-generation infrastructures and platforms for sensors, devices and the cloud.
Highlights
Researchers seek to simplify the complex in cloud computing
Read more about Researchers seek to simplify the complex in cloud computing
High performance container networking
Read more about High performance container networking
Microsoft Research webinar series presents Project Rocket: Hybrid Edge + Cloud Video Analytics Platform
Read more about Microsoft Research webinar series presents Project Rocket: Hybrid Edge + Cloud Video Analytics Platform
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4,528 results found
Publication
KARD: Lightweight Data Race Detection with Per-Thread Memory Protection
Finding data race bugs in multi-threaded programs has proven challenging. A promising direction is to use dynamic detectors that monitor the program’s execution for data races. However, despite extensive work on dynamic data race detection, most proposed systems for commodity hardware incur prohibitive overheads due to expensive compiler instrumentation of memory accesses; hence, they are not efficient enough to be used in all development and testing settings. KARD is a lightweight system that detects data…
Publication
Narya: Predictive and Adaptive Failure Mitigation to Avert Production Cloud VM Interruptions
This technical report is an extended version of our OSDI 2020 paper: Predictive and Adaptive Failure Mitigation to Avert Production Cloud VM Interruptions. Abstract: When a failure occurs in production systems, the highest priority is to quickly mitigate it. Despite its importance, failure mitigation is done in a reactive and ad-hoc way: taking some fixed actions only after a severe symptom is observed. For cloud systems, such a strategy is inadequate. In this paper, we…
Publication
Predictive and Adaptive Failure Mitigation to Avert Production Cloud VM Interruptions
When a failure occurs in production systems, the highest priority is to quickly mitigate it. Despite its importance, failure mitigation is done in a reactive and ad-hoc way: taking some fixed actions only after a severe symptom is observed. For cloud systems, such a strategy is inadequate. In this paper, we propose a preventive and adaptive failure mitigation service, NARYA, that is integrated in a production cloud, Microsoft Azure’s compute platform. Narya predicts imminent host…
Publication
Efficient Algorithms for Device Placement of DNN Graph Operators
Modern machine learning workloads use large models, with complex structures, that are very expensive to execute. The devices that execute complex models are becoming increasingly heterogeneous as we see a flourishing of domain-specific accelerators being offered as hardware accelerators in addition to CPUs. These trends necessitate distributing the workload across multiple devices. Recent work has shown that significant gains can be obtained with model parallelism, i.e, partitioning a neural network’s computational graph onto multiple devices.…
Webinar
Foundations of causal inference and its impacts on machine learning webinar
In this webinar, join Microsoft researchers Amit Sharma and Emre Kıcıman to learn about the fundamentals of causal inference. You will learn how a target question of cause and effect can be captured in a formal graphical model and answered systematically using available data. The researchers will introduce a four-step causal modeling framework for analyzing decision-making tasks and walk-through code examples using the DoWhy Python library that implements the framework. You will also discover how…
Publication
Collage Inference: Using Coded Redundancy for Lowering Latency Variation in Distributed Image Classification Systems
MLaaS (ML-as-a-Service) offerings by cloud computing platforms are becoming increasingly popular. Hosting pre-trained machine learning models in the cloud enables elastic scalability as the demand grows. But providing low latency and reducing the latency variance is a key requirement. Variance is harder to control in a cloud deployment due to uncertainties in resource allocations across many virtual instances. We propose the collage inference technique which uses a novel convolutional neural network model, collage-cnn, to provide…
Event
Developing Secure Systems Summit – Online Seminar Series: Fall 2020
The Developing Secure Systems Summit (DS3) is meant to be an inclusive event, building on several existing communities to forge its identity. On the one hand, we aim to reach out to the security research community that congregates at flagship events like IEEE Security and Privacy, the USENIX Security Symposium, and the ACM Conference on Computer and Communications Security. Equally, we seek to connect with the open source and industrial software security community that gathers…
Career Opportunity
Internship opportunities: FPGA
The fast growth of the cloud is continuously driving hardware efficiency and performance in cloud infrastructure. Our work is exploring the technologies beyond traditional computer systems. Hardware customization is playing a major role in a variety of projects across the lab. Today, nearly every new server in Microsoft data centers integrates an FPGA into a unique distributed architecture, which creates an interconnected and configurable hardware layer that extends the CPU layer. We have an unprecedented…
Video
Creating Secure IoT Devices
Five years ago, my research team set out on a mission to make it possible for any device manufacturer to create secure IoT devices. While getting security right is hard in any domain, it is especially hard in the domain of IoT, a domain where a passion to get things done quickly often runs up against the stark realities of the threat landscape of the modern internet. Our imagination and mission were fired by the…
Career Opportunity
Internship Opportunities for PhD Students – GSL Researcher
Gray Systems Lab (GSL) is an applied research lab under Azure Data. We do research and development for databases, big-data, and cloud systems, working in close collaboration with product teams to design innovative solutions for real-world problems. Our inclusive and welcoming team currently has focus areas in topics such as machine learning for systems, systems for machine learning, resource management, query optimization and execution, storage systems, modern/future hardware (e.g., NVM and FPGAs), program analysis, and…