Microsoft Technology Licensing, LLC

Tsuwang Hsieh

Paramvir Bahl

Ganesh Ananthanarayanan

Matthai Philipose

Peter Bodik

Ranveer Chandra

Srikanth Kandula

Yuanchao Shu

Santiago Martin Segarra

Sathiya Kumaran Mani

Securing and optimizing communications for a cloud service provider includes collecting connection summary information at network interface devices associated with host computing devices for a group of resources allocated to a customer of the cloud computing environment. The connection summary information includes local address information, remote address information, and data information, each connection established via the network interface devices. At least one communication graph is generated for the group of resources using the connection summary information. The graph includes nodes that represent communication resources of the group of resources and edges extending between nodes that characterize communication between the nodes. At least one analytics process is performed on data from the graph to identify at least one of a micro-segmentation strategy, a communication pattern, and a flow prediction for the group of resources.

Communication visualization and analytics system for public clouds

Systems and methods are provided for continuous learning of models across hierarchies under a multi-access edge computing. In particular, an on-premises edge server, using a model, generates inference data associated with captured stream data. A data drift determiner determines a data drift in the inference data by comparing the data against reference data generated using a golden model. The data drift indicates a loss of accuracy in the inference data. A gateway model maintains one or more models in a model cache for update the model. The gateway model instructs the one or more servers to train the new model. The gateway model transmits the trained model to update the model in the on-premises edge server. Training the new model includes determining an on-premises edge server with computing resources available to train the new model while generating other inference data for incoming stream data in the data analytic pipeline.

Continuous learning models across edge hierarchies

A method can include classifying, using a compressed and specialized convolutional neural network (CNN), an object of a video frame into classes, clustering the object based on a distance of a feature vector of the object to a feature vector of a centroid object of the cluster, storing top-k classes, a centroid identification, and a cluster identification, in response to receiving a query for objects of class X from a specific video stream, retrieving image data for each centroid of each cluster that includes the class X as one of the top-k classes, classifying, using a ground truth CNN (GT-CNN), the retrieved image data for each centroid, and for each centroid determined to be classified as a member of the class X providing image data for each object in each cluster associated with the centroid.

Querying video data with reduced latency and cost

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Redmond, WA, United States of America

Tsuwang Hsieh