Microsoft Technology Licensing, LLC

Nimish Aggarwal

Parag Sharma

Xinyan Zan

Hemant Kumar

Deepak Bansal

Rishabh Tewari

Tomas Talius

Harish Kumar Chandrappa

Bryston Mitsuo Nitta

Shekhar Agarwal

Anitha Adusumilli

Divyesh Shah

Longzhang Fu

Dhruv Malik

Joemmanuel Ponce Galindo

Aarthi Thangamani

Chris Day

A distributed resource may be mapped into a virtual network, where the resource is distributed across a large number of nodes that are uniquely addressable within the distributed resource service's address space. The resource can be represented using a relatively small number of private VIP addresses within the virtual network, while still enabling access to all of the nodes that are uniquely addressable within the address space of the distributed resource service. A resource map may be created that relates the distributed resource service's address space to the virtual network's address space. The resource map may be used by a gateway that facilitates access to a distributed resource by clients. The resource map may also be used to translate packets that are sent from clients within a virtual network into the distributed resource service's address space.

Efficiently mapping a distributed resource to a virtual network

Techniques for allowing access to shared cloud resource using private network addresses are disclosed herein. In one embodiment, a connection packet representing a connection request to a shared cloud resource in the cloud computing system can be intercepted. In response, the connection packet can be encapsulated with data representing one or more of a VNET ID, a VNET source address, or a VNET destination address of a virtual network from which the connection packet is received. The encapsulated connection packet can then be forwarded to the shared cloud resource while retaining the data representing one or more of the VNET ID, the VNET source address, or the VNET destination address for access control at the shared cloud resource.

Accessing cloud resources using private network addresses

A population of devices provides telemetry data and receives software changes or updates. Event buckets for respective events are found. Event buckets have counts of event instances, where each event instance is an occurrence of a corresponding event reported as telemetry by a device. Records of the software changes are provided, each change record representing a software change on a corresponding device. The event buckets are analyzed to identify which indicate an anomaly. Based on the change records and the identified event buckets, correlations between the software changes and the identified event buckets are found.

Anomaly analysis for software distribution

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Redmond, WA, United States of America

Nimish Aggarwal