Abstract: Network management for providing and managing Quality of Service (QoS) in converged networks, and particularly management of bursty, short-lived data loads, in an opaque network where knowledge of or control over network elements is not required. Preferential treatment is provided to some subset of the network users that require better QoS assurances from the underlying network by applying probabilistic admission control decisions in conjunction with estimated network state provides improved performance for high priority data with bursty data loads.

Abstract: With respect to a current quarter of unreported revenue for certain Internet companies, by processes performed by a computer revenue to date is analytically determined and future revenue for the remaining quarter is statistically projected by modeling revenue based on “Internet metrics”. Actual revenue performance is obtained and one or more “Internet metrics” are measured for a given Internet company. Using the revenue and measured Internet metric data from prior quarters, a regression analysis is performed in order to generate multiple models that reflect the relationship between the Internet metrics and revenue. From these models, one is selected that will most likely yield the best revenue estimates. This resultant model and current Internet metric data are subsequently used to estimate the company's revenue for the current day, week, month, or quarter. These estimates are also used to project the company's revenue for future days, weeks, months, and quarters.

Abstract: Network management for providing and managing Quality of Service (QoS) in converged networks, and particularly management of bursty, short-lived data loads, in an opaque network where knowledge of or control over network elements is not required. Preferential treatment is provided to some subset of the network users that require better QoS assurances from the underlying network by applying probabilistic admission control decisions in conjunction with estimated network state provides improved performance for high priority data with bursty data loads.

Abstract: With respect to a current quarter of unreported revenue for certain Internet companies, by processes performed by a computer revenue to date is analytically determined and future revenue for the remaining quarter is statistically projected by modeling revenue based on “Internet metrics”. Actual revenue performance is obtained and one or more “Internet metrics” are measured for a given Internet company. Using the revenue and measured Internet metric data from prior quarters, a regression analysis is performed in order to generate multiple models that reflect the relationship between the Internet metrics and revenue. From these models, one is selected that will most likely yield the best revenue estimates. This resultant model and current Internet metric data are subsequently used to estimate the company's revenue for the current day, week, month, or quarter. These estimates are also used to project the company's revenue for future days, weeks, months, and quarters.

Abstract: With respect to a current quarter of unreported revenue for certain Internet companies, by processes performed by a computer revenue to date is analytically determined and future revenue for the remaining quarter is statistically projected by modeling revenue based on “Internet metrics”. Actual revenue performance is obtained and one or more “Internet metrics” are measured for a given Internet company. Using the revenue and measured Internet metric data from prior quarters, a regression analysis is performed in order to generate multiple models that reflect the relationship between the Internet metrics and revenue. From these models, one is selected that will most likely yield the best revenue estimates. This resultant model and current Internet metric data are subsequently used to estimate the company's revenue for the current day, week, month, or quarter. These estimates are also used to project the company's revenue for future days, weeks, months, and quarters.

Abstract: Methods and systems are provided for identifying congestion and anomalies at individual nodes, links, and segments in a network. In accordance with an embodiment of the invention, a monitoring station determines for each link in the network a statistical model based on a harmonic analysis. The monitoring station collects from each link metric data. The monitoring station then selects for each link a model that includes one or more harmonic components. By estimating parameters of the harmonic components, the monitoring station then captures the periodicity of the collected metric data in the harmonic components of the model. The monitoring station uses the model to estimate the state of the network from which estimated values of the metric data are determined. The monitoring station then determines whether congestion or anomalies exist in the links based on the estimated metrics.

Abstract: With respect to a current quarter of unreported revenue for certain Internet companies, by processes performed by a computer revenue to date is analytically determined and future revenue for the remaining quarter is statistically projected by modeling revenue based on “Internet metrics”. Actual revenue performance is obtained and one or more “Internet metrics” are measured for a given Internet company. Using the revenue and measured Internet metric data from prior quarters, a regression analysis is performed in order to generate multiple models that reflect the relationship between the Internet metrics and revenue. From these models, one is selected that will most likely yield the best revenue estimates. This resultant model and current Internet metric data are subsequently used to estimate the company's revenue for the current day, week, month, or quarter. These estimates are also used to project the company's revenue for future days, weeks, months, and quarters.