At&t Intellectual Property I, LP

Georgia Tech Research Corporation

Jun Xu

Jia Wang

Ashwin Lall

Qi Zhao

Zihui Ge

Jennifer Yates

Ajay Mahimkar

Oliver Spatscheck

Seungjoon Lee

He Yan

Dongmei Wang

Tongqing Qiu

Jingfan Meng

Sen Yang

Mitsunori Ogihara

Haiquan Zhao

Long Gong

An exemplary sliding window scheduling method and system are disclosed. The exemplary sliding window scheduling method and system can schedule multiple packets in a given scheduling frame with a sliding window scheduling frame. The scheduling operation can be performed using bitmap operators and can achieve a lowest time complexity of O(1) per matching computation and per port using distributed parallelization hardware. The exemplary sliding window scheduling method and system can be performed in the context of a queue-proportional scheduler (QPS) as well as iSLIP. In alternative embodiments, the SW-QPS operation can be performed in a batching window rather than in a sliding window.

Multi-packet sliding window scheduler and method for input-queued switches

Various embodiments disclosed herein provide for a tower outage impact predictor that can determine the service impact on an end user during a cellular tower outage. Radio signal profiling divides an area into grids, and then constructs a radio signal profile for each grid based on user equipment (UE) measurement data for each grid. The number of UEs in each grid is then determined, and then the number of UEs that lose service for a simulated cellular tower outage is determined. Based on the impact analysis, a more resilient backhaul network can be implemented by determining backhaul rehoming changes that optimally home cellular towers to various backhaul network devices.

Predictive impact analysis for designing a resilient cellular backhaul network

Systems and methods to model user activity information associated with a network system are provided. A particular method includes receiving, at a computing device, a request for user activity information associated with selected channels of a television access network that provides multimedia content to users. The method includes executing a model of user activity associated with the television access network at the computing device. The model estimates the user activity information as user multimedia access demands during particular time periods within a day. The method also includes storing the user activity information at a computer-readable non-transitory storage medium.

Modeling user activity information associated with a network system

A device detects and diagnoses correlated anomalies of a network. The device includes an anomaly detection module receiving a first data stream including an event-series related to the network. The anomaly detection module executes at least one algorithm to detect a potential anomaly in the event-series. The device further includes a correlating module receiving a second data stream including other event-series related to the network. The correlating module determines whether the potential anomaly is false and determines whether the potential anomaly is a true anomaly.

Device and method for detecting and diagnosing correlated network anomalies

The preferred embodiments of the present invention are directed to estimating entropy of origin-destination (OD) data flows in a network. To achieve this, first and second sketches are created corresponding to ingress (i.e. origin) and egress (i.e. destination) flows. The sketches allow estimating entropy associated with data streams as well as entropy associated with an intersection of two or more of the data streams, which provides a mechanism for estimating the entropy OD flows in a network.

Estimating origin-destination flow entropy

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Atlanta, GA, United States of America

Jun Xu