

Systems & networking
Researching and inventing new technologies for next-generation infrastructures and platforms for sensors, devices and the cloud.
Highlights

The 3rd Workshop on Hot Topics in Video Analytics and Intelligent Edges
We are all living in the golden era of AI that is being fueled by game-changing systemic infrastructure advancements. Among numerous applications, video analytics in particular, has shown tremendous potential to impact science and society due to breakthroughs in machine learning, copious training data, and pervasive deployment of video capture devices. Analyzing live video streams is arguably the most challenging of domains for “systems-for-AI”. Unlike text or numeric processing, video analytics require higher bandwidth, consume considerable…
AsyMo: Scalable and Efficient Deep-Learning Inference on Asymmetric Mobile CPUs
On-device deep learning (DL) inference has attracted vast interest. Mobile CPUs are the most common hardware for on-device inference and many inference frameworks have been developed for them. Yet, due to the hardware complexity, DL inference on mobile CPUs suffers from two common issues: the poor performance scalability on the asymmetric multiprocessor, and energy inefficiency. We identify the root causes are improper task partitioning and unbalanced task distribution for the poor scalability, and unawareness of…
PECAM: Privacy-Enhanced Video Streaming and Analytics via Securely-Reversible Transformation
As Video Streaming and Analytics (VSA) systems become increasingly popular, serious privacy concerns have risen on exposing too much unnecessary private information to the VSA providers. Yet, it is challenging to protect privacy while still preserving desired VSA features, i.e., effective analytics, forensic support, resource efficiency, and real-time execution. In this paper, we present a VSA privacy enhancement system (PECAM), which addresses the above challenge with no change in the VSA back-end. PECAM leverages a…
Authenticating Drivers Using Automotive Batteries
Automakers have been improving, or even trying to replace, key-based driver authentication solutions, owing to their vulnerability to cyber attacks and single-point-of-failures, as well as their inability of driver identification. In line with this effort, we design a novel driver authentication system using automotive batteries, called Batteries-as-Authenticators (BAuth), to mitigate the limitations of key-based solutions by providing a second-factor authentication. BAuth is an add-on module installed between vehicles and their batteries, which uses the batteries…
Stars Can Tell: A Robust Method to Defend against GPS Spoofing Attacks using Off-the-shelf Chipset
The GPS has empowered billions of users and various critical infrastructures with its positioning and time services. However, GPS spoofing attacks also become a growing threat to GPS-dependent systems. Existing detection methods either require expensive hardware modifications to current GPS devices or lack the basic robustness against sophisticated attacks, hurting their adoption and usage in practice. In this paper, we propose a novel GPS spoofing detection framework that works with off-the-shelf GPS chipsets. Our basic…
Fast and Uniform Optically-Switched Data Centre Networks Enabled by Amplitude Caching
Data centres are fast approaching a networking bottleneck, where their incumbent electrically-switched networks may struggle to keep pace with the exponentially growing demand for bandwidth. This looming crunch has renewed interest in fast optical circuit switching (OCS), which can be used to create flat, energy-efficient, low latency networks. A wide variety of OCS architectures have been proposed based on wavelength routing, space switching, or a combination thereof, but all OCS systems face the common challenges…
Resource-Guided Configuration Space Reduction for Deep Learning Models
Deep learning models, like traditional software systems, provide a large number of configuration options. A deep learning model can be configured with different hyperparameters and neural architectures. Recently, AutoML (Automated Machine Learning) has been widely adopted to automate model training by systematically exploring diverse configurations. However, current AutoML approaches do not take into consideration the computational constraints imposed by various resources such as available memory, computing power of devices, or execution time. The training with…
SmartHarvest: Harvesting Idle CPUs Safely and Efficiently in the Cloud
We can increase the efficiency of public cloud datacenters by harvesting allocated but temporarily idling CPU cores from customer virtual machines (VMs) to run batch or analytics workloads. Even small efficiency gains translate into substantial savings, since provisioning and operating a datacenter costs hundreds of millions of dollars per year. The main challenge is to harvest idle cores with little or no impact on customer VMs, which could be running latency-sensitive services and are essentially…
EuroSys 2021
Microsoft is proud to be a gold sponsor of The European Conference on Computer Systems (EuroSys) 2021. See more details on our contributions on the sessions tab.
Researcher – Systems and Networking
Microsoft Research Asia (MSRA) is looking for strong, passionate researchers and developers at various levels in the areas of system and networking. You will be part of the elite industrial research team, working together with world-class experts on exciting and challenging projects in directions such as next generation systems support for AI, distributed system infrastructures and tools, intelligent cloud and edge computing, distributed database and transaction systems, large scale storage system, security and blockchain, custom…