Alcatel Lucent

Lucent Technologies Inc.

Alcatel-lucent USA Inc.

Nokia Technologies Oy

Nokia of America Corporation

Thomas Y Woo

Thyagarajan Nandagopal

Krishan Kumar Sabnani

Tirunell V Lakshman

Ramachandran Ramjee

Thomas F LaPorta

Martin Havemann

Katherine Hua Guo

Tian Bu

Furquan Ahmed Ansari

Samphel Norden

Li Erran Li

Lampros Kalampoukas

Markus Andreas Hofmann

James Robert Ensor

Mun-Choon Chan

Kerry Fendick

Richard Dennis Gitlin

Dong Hui Liu

Aleksandr Stolyar

Aiyou Chen

Emina Soljanin

Eric Henry Grosse

Sharad Jaiswal

Sandra Ramos Thuel

Armen Aghasaryan

Thyaga Nandagopal

Makram Bouzid

Animesh Nandi

Fangzhe Chang

Dimitre Davidov Kostadinov

Kerry W Fendick

Jingdong Chen

Yiteng Arden Huang

Saurabh Panjwani

Nisheeth Shrivastava

Saurabh Shukla

Scott Alan Vander Wiel

Murakami Kazutaka

James Ensor

In one embodiment, the method includes determining one or more nodes associated with a treatment of a query; generating one or more stimuli associated with the treatment of the query wherein the or each stimulus are likely to perturb one or more resources within a system; measuring data at the or each node relating to the resources to determine the effect of the or each stimuli at the or each node; identifying one or more pairs of nodes which have a correlation in the measured data; transforming the correlation into a causal relationships where the cause is a measuring device measuring the response and the consequences are the other correlated measuring devices; generating a list of causal relationships; and combining different causal relationships into a causal model so that a chain of causal propagations can be built.

Determining performance in a distributed application or system

Systems and methods for training neural networks. One embodiment is a system that includes a memory configured to store samples of training data for a Deep Neural Network (DNN), and a distributor. The distributor identifies a plurality of work servers provisioned for training the DNN by processing the samples via a model of the DNN, receives information indicating Graphics Processing Unit (GPU) processing powers at the work servers, determines differences in the GPU processing powers between the work servers based on the information, and allocates the samples among the work servers based on the differences.

Coordinated heterogeneous processing of training data for deep neural networks

A chat-based communication capability is presented. In at least some embodiments, a chat-based communication capability utilizes a chat-based communication paradigm to support one or more communication interaction types not typically supported by chat-based communication paradigms. In at least some embodiments, a chat-based communication capability supports chat-based communication between a human entity and a non-human entity (e.g., a device, a program, a process, an organization, or the like). In at least some embodiments, a chat-based communication capability supports chat-based communication between multiple non-human entities (e.g., where the non-human entities may include devices, programs, processes, organizations, or the like).

Chat-based support of multiple communication interaction types

A capability is provided for securely transferring a file within network-based storage. A capability is provided for securely transferring a user file of a user from a first server to a second server. The first server may be associated with a first service provider and the second server may be associated with a second service provider. The secure transfer of a user file from the first server to the second server may be performed based on a One-Click File Transfer capability in which only a single click by the user is needed in order for the user file to be transferred. The secure transfer of a user file from the first server to the second server may be performed based on a Zero-Click File Transfer capability in which the user file may be transferred without any interaction by the user.

Secure file transfers within network-based storage

Systems and methods for preventing web services from using sensitive data in customizing web pages for a user. In one embodiment, a protection system is implemented between the user and the web service. The protection system identifies past interactions of the user with the web service, identifies text in the past interactions between the user and the web service, and identifies subjects discussed in the text. The protection system then communicates with the user to determine which of the subjects are sensitive to the user, and deletes a history stored by the web service for the user that relates to the sensitive subjects.

Protection of sensitive data of a user from being utilized by web services

Various embodiments relate to a method, device, and machine-readable storage medium including: receiving a work request for distribution among a plurality of servers; selecting a server of the plurality to receive the work request including: determining whether at least one of the plurality of servers has previously indicated a capacity for additional work, and when at least one has indicated such capacity, selecting the server from a set of servers identified from the plurality as having previously indicated a capacity for additional work; and transmitting the work request to the server. Various embodiments relate to a method, device, and machine-readable storage medium including: maintaining and processing a plurality of requests; determining, based on the plurality of requests, that the server has capacity to process at least one additional request; selecting a load distributor from a plurality of load distributors; and transmitting an indication of capacity to the load distributor.

Efficient and scalable pull-based load distribution

Various embodiments provide a method and apparatus of providing wireless access point selection based on performance metric(s) and signal strength. In particular, a client selecting between one of a number of wireless access points bases the selection decision on performance metrics as well as signal strength.

Method and apparatus for selecting a wireless access point

At least one example embodiment discloses a method for allocating uplink transmissions from a plurality of user equipments (UEs) to a base station. The method includes allocating uplink transmission air-time of the base station to a plurality of groups, associating each of the plurality of UEs to the plurality of groups, a throughput of each associated UE in a same group being affected by the remaining UEs in the same group and determining a resource sharing mode associated with each of the plurality of groups, the resource sharing mode being one of a first mode and a second mode, the base station allocating the same throughput to each associated UE in the associated group in the first mode and the base station allocating a same transmission air-time to each associated UE in the associated group in the second mode.

Methods for allocating and scheduling uplink and downlink transmissions and apparatuses thereof

At least one example embodiment discloses a method for allocating downlink transmissions to a plurality of user equipments (UEs) from a base station. The method includes allocating downlink transmission air-time of the base station to a plurality of groups, associating each of the plurality of UEs to the plurality of groups, a throughput of each associated UE in a same group being affected by the remaining UEs in the same group and determining a resource sharing mode associated with each of the plurality of groups, the resource sharing mode being one of a first mode and a second mode, the base station allocating the same throughput to each associated UE in the associated group in the first mode and the base station allocating the same transmission time to each associated UE in the associated group in the second mode.

The SoftRouter architecture separates the implementation of control plane functions from packet forwarding functions. In this architecture, all control plane functions are implemented on general purpose servers called the control elements (CEs) that may be multiple hops away from the forwarding elements (FEs). A network element (NE) or a router is formed using dynamic binding between the CEs and the FEs. There is a protocol failover mechanism for handling failovers initiated by FEs to transfer control from one CE to another CE.

SoftRouter protocol failovers

A method and computer product are presented for identifying Internet worm propagation based upon changes in packet arrival rates at a network connection. First, unsolicited (i.e., packets that were not requested by the receiver) traffic is separated from solicited traffic at the network connection. The unsolicited traffic arrival patterns are monitored and analyzed for any changes. Once changes in the unsolicited traffic arrival patterns are detected, the changes are mathematically analyzed to detect growth trends. The presence of growth trends that follow certain key characteristics indicate whether the changes are due to worm propagation.

Evaluation of a fast and robust worm detection algorithm

An embodiment of the exemplary SoftRouter architecture includes two physically separate networks, a control plane network and a data plane network. The data plane network is one physical network for the data traffic, while the control plane network is another physical network for the control traffic. The topology of the data plane network is made up of interconnected forwarding elements (FEs). The topology of the control plane network is made up interconnected control elements (CEs). This physical independence of the control plane network from the data plane network provides for a secure mechanism to communicate among the CEs in the control plane network. In addition, this physical independence provides improved reliability and improved scalability, when compared to the traditional router architecture, where control plane message are in-band with the data plane.

Softrouter separate control network

The invention includes a method and apparatus for providing load balancing of control traffic received by a mobility home agent implemented using multiple control elements. A method includes receiving, from a node, a control message intended for the network element, performing a load-balancing operation to select one of the control elements to handle the control message, and propagating the control message toward the selected one of the control elements. The load-balancing operation is performed using at least two load-balancing metrics comprising a first metric and a second metric. The load-balancing operation is performed in a manner for maintaining a context between the node from which the control message is received and the selected one of the control elements, such that subsequent control messages received from the node are propagated to the selected one of the control elements.

Method and apparatus for performing load balancing for a control plane of a mobile communication network

A network architecture includes one or more feature servers and control servers in a control plane that is logically separate from a data plane that includes forwarding elements. Feature servers facilitate adding network-based functionality in a centralized way that is has better scalability than the traditional router architecture. Some examples of network-based functionality are voice over IP, enhancing QoS support, scaling BGP route reflectors, network-based VPN support, scaling mobile IP support, introducing IPv6 into existing and future networks, and enhancing end-to-end network security. Feature servers remove complexity from routers, allow functions to be implemented on a standard-off-the-shelf server platform, facilitate easy introduction of value-added functions, and scale well.

Softrouter feature server

Growing community of inventors

Red Bank, NJ, United States of America

Thomas Y Woo