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
Ultra-dense data storage and extreme parallelism with electronic-molecular systems
Read more about Ultra-dense data storage and extreme parallelism with electronic-molecular systems
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4,732 results found
Publication
Ekya: Continuous Learning of Video Analytics Models on Edge Compute Servers
With the widespread deployment of video analytics applications, edge compute servers are preferred for the analytics of the videos (for reasons of bandwidth and privacy). DNNs are deployed on the edge servers for inference, but when out in the field, they suffer from data drift where the live video data is significantly different than the training data. Continuous learning techniques are designed to handle data drift by periodically retraining the models on new data. Our…
Event
The 3rd Workshop on Hot Topics in Video Analytics and Intelligent Edges
The 3rd Workshop on Hot Topics in Video Analytics and Intelligent Edges will be hosted in conjunction with ACM MobiCom 2021 on October 25, 2021.
Publication
Spider: A Multi-Hop Millimeter-Wave Network for Live Video Analytics
Massive video analytics systems, comprised of many densely deployed cameras and supporting edge servers, are driving innovation in many areas including smart retail stores and security monitoring. To support such systems the challenge lies in collecting video footage in a way that maximizes end-to-end application goals, and scales this performance as camera density increases to meet application needs. This paper presents Spider, a multi-hop, millimeter-wave (mmWave) wireless relay network design that meets these needs. To…
Publication
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…
Publication
Etna: Harvesting Action Graphs from Websites
Knowledge bases, such as Google knowledge graph, contain millions of entities (people, places, etc.) and billions of facts about them. While much is known about entities, little is known about the actions these entities relate to. On the other hand, the Web has lots of information about human tasks. A website for restaurant reservations, for example, implicitly knows about various restaurant-related actions (making reservations, delivering food, etc.), the inputs these actions require and their expected…
Publication
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…
Publication
Flexible High-resolution Object Detection on Edge Devices with Tunable Latency
Object detection is a fundamental building block of video analytics applications. While Neural Networks (NNs)-based object detection models have shown excellent accuracy on benchmark datasets, they are not well positioned for high-resolution images inference on resource-constrained edge devices. Common approaches, including down-sampling inputs and scaling up neural networks, fall short of adapting to video content changes and various latency requirements. This paper presents Remix, a flexible framework for high-resolution object detection on edge devices. Remix…
Publication
Towards Memory-Efficient Inference in Edge Video Analytics
Video analytics pipelines incorporate on-premise edge servers to lower analysis latency, ensure privacy, and reduce bandwidth requirements. However, compared to the cloud, edge servers typically have lower processing power and GPU memory, limiting the number of video streams that they can manage and analyze. Existing solutions for memory management, such as swapping models in and out of GPU, having a common model stem, or compression and quantization to reduce the model size incur high overheads…
Publication
HALO: Hierarchy-aware Fault Localization for Cloud Systems
A typical cloud system has a large amount of telemetry data collected by pervasive software monitors that keep tracking the health status of the system. The telemetry data is essentially multi-dimensional data, which contains attributes and failure/success status of the system being monitored. By identifying the attribute value combinations where the failures are mostly concentrated (which we call fault-indicating combination), we can localize the cause of system failures into a smaller scope, thus facilitating fault…
Career Opportunity
Senior Research Software Engineering – Research for Industry
Are you interested in working for one of the most cutting-edge teams in Microsoft? Interested in a fast-paced start-up like environment, passionate about distributed systems, machine learning, and Cloud Computing technology and driving growth in one of Microsoft’s core businesses? Then look no further than the Research for Industry team spanning Azure and Microsoft Research! We are a team relentlessly focused on research, innovation, and advancing Microsoft Cloud strategy. Azure is the fastest-growing business in…