Abstract: Network page latency reduction is facilitated by various embodiments. In one embodiment, a first distribution corresponding to a plurality of instances of a network page is estimated. A second distribution corresponding to a subset of the instances is also estimated. The network page is adapted based at least in part on a comparison of the first and second distributions in order to reduce network page latency.

Abstract: Disclosed are various embodiments for modifying network site behavior. At least one session-level performance metric associated with a client is determined. The one or more session-level performance metrics are determined from one or more latency times. Each one of the latency times represents a time elapsed between a sending of a network page request in the client and a rendering in the client of a network page received from a network page server in response to the network page request. A response to a next network page request from the client is modified according to the one or more session-level performance metrics in order to adjust a next latency time for the client.

Abstract: Disclosed are various embodiments for modifying network site behavior. At least one session-level performance metric associated with a client is determined. The one or more session-level performance metrics are determined from one or more latency times. Each one of the latency times represents a time elapsed between a sending of a network page request in the client and a rendering in the client of a network page received from a network page server in response to the network page request. A response to a next network page request from the client is modified according to the one or more session-level performance metrics in order to adjust a next latency time for the client.

Abstract: Disclosed are various embodiments for modifying network site behavior. At least one session-level performance metric associated with a client is determined. The one or more session-level performance metrics are determined from one or more latency times. Each one of the latency times represents a time elapsed between a sending of a network page request in the client and a rendering in the client of a network page received from a network page server in response to the network page request. A response to a next network page request from the client is modified according to the one or more session-level performance metrics in order to adjust a next latency time for the client.

Abstract: Disclosed are various embodiments of a system, method and computer readable medium for network page latency reduction. In one embodiment, among others, a system comprises a processor circuit and a memory associated with the processor circuit. The system further comprises a distribution estimator configured to estimate a distribution corresponding to latency data regarding a plurality of instances of a network page. The system further comprises an attribute comparator configured to identify an attribute associated with a subset of the instances of a network page. The subset of the instances corresponds to a latency data substantially represented by a tail of the distribution. Additionally, the system comprises a recommendation generator configured to generate a modification recommendation for the subset of the instances of the network page.

Abstract: Disclosed are various embodiments for modifying network site behavior. At least one session-level performance metric associated with a client is determined. The one or more session-level performance metrics are determined from one or more latency times. Each one of the latency times represents a time elapsed between a sending of a network page request in the client and a rendering in the client of a network page received from a network page server in response to the network page request. A response to a next network page request from the client is modified according to the one or more session-level performance metrics in order to adjust a next latency time for the client.

Abstract: An apparatus and methods advantageously select content items for dynamically-generated web pages in an intelligent and virtually autonomous manner. This permits the operator of the web site to rapidly identify and respond to trends, thereby advantageously updating the web site relatively quickly and efficiently without or with less time consuming and expensive manual labor. User interaction for a plurality of users with the web site is collected in a database. For various content items, the database is mined to extract relationships between probability and references of select attributes in probability models. When a new web page is requested, attributes, which can include attributes associated with a user, are used as references to the applicable probability models of selected content items, combined with value weighting to generate expected values, and selected for use in the web page at least partially based on the expected values.