CROSS-REFERENCE TO RELATED APPLICATIONS

This application is related to U.S. patent application Ser. No. 16/775,807, titled “MACHINE LEARNING BASED END TO END SYSTEM FOR TCP OPTIMIZATION,” by Tejaswini Ganapathi, Satish Raghunath and Shauli Gal, filed on equal day, the contents of all of which are incorporated herein by reference in their entireties.

TECHNOLOGY

The present invention relates generally to optimizing content delivery, and in particular, to estimation of network quality metrics from network request data.

BACKGROUND

Access service networks are very volatile and diverse. Link conditions may vary in different access service networks and different locations. Metrics such as latency, jitter, throughput, and losses are hard to bound or predict. The diversity comes from the various network technologies, domain name services, plethora of devices, platforms, and operating systems in use at the various locations of the access service networks.

Transmission Control Protocol (TCP) plays an important role in the content delivery business: it provides a reliable, ordered, and error-checked delivery of a stream of octets between applications running on hosts communicating by an IP network. Major Internet applications, such as the World Wide Web, email, remote administration, and file transfer, rely on TCP. Numerous parameters may be used in TCP to help in ordered data transfer, retransmission of lost packets, error-free data transfer, flow control, and congestion control. However, identifying an optimal data value for a single TCP parameter based on changing network characteristics remains a challenge.

The approaches described in this section are approaches that could be pursued, but not necessarily approaches that have been previously conceived or pursued. Therefore, unless otherwise indicated, it should not be assumed that any of the approaches described in this section qualify as prior art merely by virtue of their inclusion in this section. Similarly, issues identified with respect to one or more approaches should not assume to have been recognized in any prior art on the basis of this section, unless otherwise indicated.

