International Business Machines Corporation

Cisco Technology, Inc.

International Busniss Machines Coporation

John Michael Lake

Tanveer Fathima Syeda-Mahmood

Guy Maring Lohman

Peter Haas

David Louis Kaminsky

Scot MacLellan

Russell C Blaisdell

John E Dinger

Richard Victor Kisley

David Breitgand

Kristian Jon Stewart

Onn Menahem Shehory

Ealan Abraham Henis

David R Franklin

Durga Devi Mannaru

Carlos M Pignataro

Ruthie D Lyle

Eric Albin Voit

Kyle Andrew Donald Mestery

David Mark Ogle

Alpesh S Patel

James Corvin Fletcher

Robert Thomas Uthe

Angela Richards Jones

Christopher Paul Vignola

Wenjian Qiao

Maayan Goldstein

Ashutosh Singh

Keith P Biegert

Jonathan Mayer Sobel

Wendell Davis Jones

Keith Barber

Robert Hagan

Adam J Friedlander

Srinivasan K Rangaswamy

Gregory Philip Hamm

Jonathan G Rossie

Greg Kaszycki

Kalena Charisee Kelly

Fonda J Daniels-Farrar

Randall Benjamin Pittman

Kent Filmore Hayes, Jr

John Dinger

David Richard Franklin

A system and method are provided for placing network functions among respective locations in a network. The locations at which the network functions are placed can be nodes and network devices within the network. These nodes can be selected, e.g., based on which network devices have available capacity and or specialized hardware (e.g., accelerator sin a data processing units (DPUs)) that is optimized for particular network functions. The network functions can include an inline network function that is provisioned directly in a data plane of one of the network devices (e.g., in-lined directly in a hardware offload device without a virtual machine and without a container). The decision of where to place the network functions can be based on a performance metric (e.g., representing available computational/memory resources at the network nodes) and/or a network-function metric (e.g., representing consumed computational/memory resources by the network functions) to improve system performance.

Network security functions for dynamic construction and programmatic placement

Techniques for ultra-short-term resource forecasting for a network device are described. A selection of a time series algorithm from a set of time series algorithms for determining capacity right-sizing of a local resource is received, the is selection based at least in part on current local traffic conditions. Based on current local traffic conditions, parameter values to be used in the algorithm are determined, the parameters are associated with the time series algorithm selection. A number of data points for input to the time series algorithm are determined, the data points are a sequence of values representing an amount of the local resource used by the network device at a point in time and are collected at predetermined time intervals. Based on a calculation of the time series algorithm using the number of data points and parameter values, the right-size capacity of the local resource for the network device is determined and provided.

Seasonal parameter provisioning for ultra-short-term forecasts

Techniques and architecture are described for inducing precise delays in a network device (network node) that has the capability to act on packets/traffic flows based on policy configurations of the network device and delays experienced by traffic in the network device. This capability may be used for testing and verification of the network device to verify that the network device meets the configured policies. Additionally, this capability may be utilized in an operational network to selectively induce delays and measure its impact for purposes such as, for example, planning, stress testing, resiliency, etc.

Inducing delay from a peer node to quantify latency-based behavior

Methods, apparatus, and computer program products for determining software complexity. A plurality of versions of a software module whose complexity is to be determined are compressed. Lengths of the compressed versions are compared, one with another, to provide complexity metrics.

Determining software complexity

A destination address is processed to determine if the destination address is a fake web address or hyperlink. The destination address may be compared with a database of known domain names to see if the domain name is legitimate or illegitimate. The designation address may also be compared to other domain names to see if it is an honest or dishonest transformation of the other domain names. Appropriate action may be taken if the designation address is a dishonest transformation of another domain name.

Fake web addresses and hyperlinks

Systems, methods, and computer program products to perform an operation comprising determining, based on actual coverage point data for a first time interval and expected coverage point data, that a first set of lines of source code associated with the actual and expected coverage point data have not been executed by a system, instantiating, in the system, an action code associated with the first set of lines of source code and an effect code associated with the action code, and determining, based on a final state of the effect code, whether the action code executed correctly in the system.

Introspective fault and workload injection for service assurance

In a typical computer network, at least some of the managed resources are monitored to determine whether those resources are meeting predetermined performance goals or service level objectives. To simplify the process of configuring a network monitor, information about the service level objectives is loaded into the resource itself. When the resource is detected, the service level objective information is extracted from the resource information and made available to a translating engine. The translating engine converts the extracted information to monitoring directions that are used to configure the network monitor. Embodiments in which new resources are detected either buying a registration process or a polling process are described.

Monitoring of computer network resources having service level objectives

Fake web address detection

An apparatus for predicting a network event flood comprises an event rate detector for detecting rates of event emissions from one or more devices; an aggregator for producing an aggregate rate and an aggregate rate trend of the rates of event emissions from a plurality of the devices; a level generator for generating a plurality of levels comprising maximum acceptable event rate values of a plurality of the aggregate rate trends over plural time periods; a storage component for storing the plurality of levels; a comparator for comparing a current aggregate rate trend with at least a selected one of the levels; and a signaller for signalling a predicted event flood responsive to the comparator detecting that the current aggregate rate trend will exceed the at least a selected one of the levels at a first point in time.

Network event management

A method, apparatus and computer instructions are provided to improve the push/pull workload management model with intelligent routing to effectively collect data from systems that consist of dynamic sub-systems. The invention improves the push/pull model of the referenced invention with intelligent request routing to solve the above problem. An API is exposed in the workload manager enabling its request router to decline requests to route to idle sub-systems. This allows the monitoring agent to avoid sending a pull request to an idle sub-system. Each sub-system will push the data to the agent as it enters the idle state. The agent caches the data it receives from idle sub-systems and combines it with data it pulls from active and stopped sub-systems.

Method to effectively collect data from systems that consists of dynamic sub-systems

Growing community of inventors

Cary, NC, United States of America

John Michael Lake