Microsoft Technology Licensing, LLC

Chittaranjan Sadanand Pattekar

Anil K Ruia

Won Yoo

Clifford Paul Strom

Ning Lin

Daniel Kevin McBride

Arvind Ramakrishnan

Yashraj Motilal Borse

Konstantin E Ryvkin

Srinivasachakrapani Kotipalli

Himanshu Patel

Venkat Raman Don

Paul James Andrew

Wade Hilmo

Brad D Rutkowski

Saurabh Vats

Bryan Louis Porter

Vijay Mohan Ankireddi

Shayon Gupta

Michael Andres Carrillo Pinzon

Embodiments of the disclosure provide application management capabilities to enterprises. A computing device of a user, associated with the enterprise, receives an enrollment token signed with a certificate. The enrollment token includes an enterprise identifier associated with the enterprise. The computing device receives a package containing one or more applications. The package also includes an enterprise identifier. Installation and execution of one or more applications from the received package is accepted or rejected based on a comparison of the enterprise identifier from the enrollment token with the enterprise identifier from the received package or application. A web service provides validation services by monitoring the installation and execution of applications on the computing devices associated with the enterprise.

Enterprise application management with enrollment tokens

A latency processing system detects traffic at a cloud service end point and analyzes packets in the detected traffic to identify a network configuration of a client that is accessing the cloud service. Latency components corresponding to different parts of the network configuration are identified and quantified. A recommendation engine is controlled to generate and surface an output indicative of recommendations for reducing network latency.

Detecting and quantifying latency components in accessing cloud services

Non-personal network attributes of a user's network connection are detected. A user's physical location is classified as being onsite or remote based upon the non-personal network attributes. Connectivity performance information is generated indicative of a performance of a user device in connecting with the host computing system over the network, and a control signal is generated to surface performance information through an administrative system.

Detecting non-personal network and connectivity attributes for classifying user location

A caching system segments content into multiple, individually cacheable chunks cached by a cache server that caches partial content and serves byte range requests with low latency and fewer duplicate requests to an origin server. The system receives a request from a client for a byte range of a content resource. The system determines the chunks overlapped by the specified byte range and sends a byte range request to the origin server for the overlapped chunks not already stored in a cache. The system stores the bytes of received responses as chunks in the cache and responds to the received request using the chunks stored in the cache. The system serves subsequent requests that overlap with previously requested ranges of bytes from the already retrieved chunks in the cache and makes requests to the origin server only for those chunks that a client has not previously requested.

Byte range caching

An intelligent caching system is described herein that intelligently consolidates the name-value pairs in content requests containing query strings so that only substantially non-redundant responses are cached, thereby saving cache proxy resources. The intelligent caching system determines which name-value pairs in the query string can affect the redundancy of the content response and which name-value pairs can be ignored. The intelligent caching system organically builds the list of relevant name-value pairs by relying on a custom response header or other indication from the content server. Thus, the intelligent caching system results in fewer requests to the content server as well as fewer objects in the cache.

Intelligent caching for requests with query strings

Requests for content can be received from clients and forwarded to servers, and responses to the requests can be received from the servers and forwarded to the clients. A health model can also be maintained. The health model can be based on information in the responses and possibly also on information in the requests, and the health model can indicate the health of the servers in responding to different types of requests. The health model may differentiate between health in responding to requests with different features in URLs of the requests, such as different namespaces and/or different extensions.

Request-based server health modeling

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Redmond, WA, United States of America

Chittaranjan Sadanand Pattekar