Jia Jing Wang

6928485, Aug 9, 2005

Jun 23, 2000

09/603154

Balachander Krishnamurthy - New York NY, US
Jia Wang - White Plains NY, US

AT&T Corp. - New York NY

G06F015/173

709242, 709220, 709223, 709224

A method for clustering together network clients for guiding of placement of network servers is disclosed. A number of routing table prefix/netmask entries are aggregated and unified into a tubular format. The routing table entries may be converted into a singular format. A network server log is used to extract a number of client IP addresses which are compared to the entries within the unified routing table. A common prefix shared by a number of the client IP addresses and an entry in the unified routing table is determined and used to cluster the clients together in a client cluster. Network servers, such as proxy server, cache servers, content distribution servers and mirror server may be placed in the network according to the client clusters.

7889666, Feb 15, 2011

Dec 26, 2007

12/005191

Dan Pei - Jersey City NJ, US
Zhuoqing Morley Mao - Ann Arbor MI, US
Jia Wang - Randolph NJ, US
Ying Zhang - Ann Arbor MI, US

AT&T Intellectual Property II, L.P. - Reno NV

H04L 12/26

370242

A system and method is disclosed for a scalable and robust network troubleshooting framework for VPN backbones addressing scalability and data imperfection. The framework takes advantage of lattice data structures to identify multi-dimensional hierarchical events that occur frequently across multiple data sources. Correlation schemes and inference rules are created for diagnosing network events and generating actionable reports for important events.

7958075, Jun 7, 2011

Jun 28, 2007

11/824073

David L. Applegate - Maplewood NJ, US
Gruia Calinescu - Wilmette IL, US
David S. Johnson - Madison NJ, US
Howard Karloff - New York NY, US
Katrina Ligett - Pittsburgh PA, US
Jia Wang - Randolph NJ, US

AT&T Intellectual Property II, LP - Atlanta GA

G06F 17/00
G06N 5/02

706 47

A geometric model is considered for the problem of minimizing access control lists (ACLs) in network routers. A colored rectilinear pattern is created within an initially white rectangular canvas, and the basic operation is to choose a subrectangle and paint it a single color, overwriting all previous colors in the rectangle. The method operates on rectangular rule lists (RRLs) and access control lists (ACLs) in which all rectangles are strips that extend either the full length or the full height of the canvas. A polynomial-time algorithm optimally constructs such patterns when, as in the ACL application, the only colors are black and white (permit or deny). That algorithm is complemented by a significantly faster approximation algorithm that is guaranteed to be no worse than 3/2 optimal.

8111626, Feb 7, 2012

Dec 31, 2008

12/347444

Aman Shaikh - Summit NJ, US
Cheng Ee - Rockaway NJ, US
Ajay Mahimkar - Austin TX, US
Jia Wang - Randolph NJ, US
Jennifer Yates - Morristown NJ, US
Yin Zhang - Austin TX, US
Zihui Ge - Florham Park NJ, US

AT&T Intellectual Property I, L.P. - Atlanta GA

H04L 12/26
G01R 31/08
H04J 1/16

370242

A method and apparatus for providing event correlation in a network are disclosed. For example, the method extracts a plurality of events of interest from a database, and creates one or more event time series from the plurality of events of interest, wherein each of the one or more event time series comprises a set of events of a same type and of a same location that occur within a given time period. The method forms one or more composite events from the one or more event time series, and performs one or more pair-wise correlations for at least one of: the event time-series, or the one or more composite events. The method then identifies one or more pair-wise correlations that are statistically significant.

20130074175, Mar 21, 2013

Oct 22, 2012

13/657350

Dan Pei - Jersey City NJ, US
Jia Wang - Randolph NJ, US

AT&T Intellectual Property I, L.P. - Reno NV

H04L 29/06

726 12, 726 23

A communication network is operated by identifying at least one potential hijack autonomous system (AS) that can be used to generate a corrupt routing path from a source AS to a destination AS. For each of the at least one potential hijack AS the following operations are performed: identifying at least one regional AS that is configured to adopt the corrupt routing path from the source AS to the destination AS and determining a reflector AS set such that, for each reflector AS in the set, a source AS to reflector AS routing path and a reflector AS to destination AS routing path do not comprise any of the at least one regional AS. A reflector AS is then identified that is common among the at least one reflector AS set responsive to performing the identifying and determining operations for each, of the at least one potential hijack AS.

20130097703, Apr 18, 2013

Nov 15, 2012

13/677896

Dan Pei - Jersey City NJ, US
Jia Wang - Randolph NJ, US
Tongqing Qiu - Randolph GA, US

AT&T INTELLECTUAL PROPERTY I, L.P. - Atlanta GA

G06F 21/55

726 22

Method, system and computer-readable device to locate a prefix hijacker of a destination prefix within a one-hop neighborhood. The method includes generating one-hop neighborhoods from autonomous system-level paths associated with a plurality of monitors to a destination prefix. The method also includes determining a suspect set of autonomous system identifiers resulting from a union of the one-hop neighborhoods. The method further includes calculating a count and a distance associated with each autonomous system identifier in the suspect set of autonomous system identifiers. The count represents how often an autonomous system identifier appears in the one-hop neighborhoods. The distance represents a total number of autonomous system identifiers from the autonomous system identifier to autonomous system identifiers associated with the plurality of monitors. Yet further, the method includes generating a one-hop suspect set including autonomous system identifiers in the suspect set that have a greatest sum of the count and the distance.

20160014622, Jan 14, 2016

Sep 25, 2015

14/865594

- Atlanta GA, US
Ioannis Broustis - Millburn NJ, US
Zihui Ge - Madison NJ, US
Sarat Puthenpura - Berkeley Heights NJ, US
Leonid Razoumov - Riverdale NY, US
Aman M. Shaikh - Berkeley Heights NJ, US
Nemmara K. Shankaranarayanan - Bridgewater NJ, US
Jia Wang - Randolph NJ, US
Xing Xu - Los Angeles CA, US
He Yan - Berkeley Heights NJ, US

H04W 24/02
H04W 72/04
H04W 52/26
H04W 16/22

To efficiently mitigate cell coverage disruptions caused by either unplanned equipment failures or planned outages during maintenance activities, an efficient cell site outage mitigation system is provided to calculate an optimal configuration for neighboring cell site devices before adjusting the cell site configuration settings to cover the coverage gap. The optimal configuration is determined using an offline model learner. Radio frequency propagation models and user demand and distribution models can be used to determine the offline solution. The optimal configuration setting learnt using an offline model is then implemented when an outage is determined to have occurred in the operational field.

20150146514, May 28, 2015

Nov 25, 2013

14/088684

- Atlanta GA, US
Ioannis Broustis - Millburn NJ, US
Zihui Ge - Madison NJ, US
Sarat Puthenpura - Berkeley Heights NJ, US
Leonid Razoumov - Riverdale NY, US
Aman M. Shaikh - Berkeley Heights NJ, US
Nemmara K. Shankaranarayanan - Bridgewater NJ, US
Jia Wang - Randolph NJ, US
Xing Xu - Los Angeles CA, US
He Yan - Berkeley Heights NJ, US

AT&T Intellectual Property I, L.P. - Atlanta GA

H04W 16/22

370216

To efficiently mitigate cell coverage disruptions caused by either unplanned equipment failures or planned outages during maintenance activities, an efficient cell site outage mitigation system is provided to calculate an optimal configuration for neighboring cell site devices before adjusting the cell site configuration settings to cover the coverage gap. The optimal configuration is determined using an offline model learner. Radio frequency propagation models and user demand and distribution models can be used to determine the offline solution. The optimal configuration setting learnt using an offline model is then implemented when an outage is determined to have occurred in the operational field.