Netapp, Inc.

International Business Machines Corporation

Equinix, Inc.

Other

Kaladhar Voruganti

Vipul Mathur

Lakshmi Narayanan Bairavasundaram

Gokul Soundararajan

Sandeep Gopisetty

Ramani Ranjan Routray

Kang-Won Lee

Rukma Ameet Talwadker

Dakshi Agrawal

Garth Richard Goodson

Dinesh C Verma

Sandeep Madhav Uttamchandani

Sai Rama Krishna Susarla

Gauri Sudhir Shah

Kiran Nenmeli Srinivasan

Shankar Pasupathy

Konstantinos Magoutis

Utpal Mukhopadhyaya

Sindhu Payankulath

Murthy V Devarakonda

Norbert George Vogl

Eric Kevin Butler

Chung-hao Tan

Jayanta Basak

Madhukar R Korupolu

Siddhartha Nandi

Scott Joseph Colbeck

Pi-Wei Chin

Neeraja Yadwadkar

Gregory E McBride

David Michael Shackelford

Robert Beverley Basham

Prasenjit Sarkar

Aameek Singh

Akshat Verma

Sudhir Srinivasan

Brent William Yardley

Khalid Filali-Adib

David Alson George

Cuong Minh Le

Ravi Kumar Kavuri

James Mitchell Ratliff

Priya Sehgal

Syed Nawaz

Thirumale Niranjan

Seraphin Bernard Calo

David Slik

Divyesh Jadav

David Allan Pease

Craig Fulmer Everhart

Prashant Pandey

Rohit Jain

Vijaay Doraiswamy

Purvish Purohit

Parag Deshmukh

Darren Sawyer

Srinivasan Balasubramanian

Ranjit Kumar

Roberto C Pineiro

Guido Franciscus Wilhelmus Coenders

Kushal Wadhwani

Mahendra Malviya

Chhavi Sharma

Sudarshan M Srinivasan

Tushar Mohan

Amit Srivastava

Sai Rama Krishna Susaria

Vineet Pandey

David Darden Chambliss

Steven Robert Kleiman

Jaishankar Moothedath Menon

Lawrence Yiumchee Chiu

Chatschik Bisdikian

Rajesh Sundaram

Steve Richard White

Sumant Padbidri

John Davis Palmer

Krishnakumar Rao Surugucchi

Shailaja Kamila

Srinivasan Narayanamurthy

Danjue Li

Li Yin

Mustafa Arisoylu

Patrick Cozzi

James Hartwell Holl, Ii

Norman Jerome Pass

David James Asher Hall

Bhuvan Bamba

Lanfa Wang

Subramaniam Mohan

Jim Voll

Joseph C Czap, Jr

Prasenjit Sakar

Rodney Martin Elder

Ravi Kiran Pasula

Joseph D Ryan

Jain Rohit

Chinmay Ashok Deshpande

Nicholas Lloyd Armstrong-Crews

Patrick Randolph Eaton

In general, this disclosure describes techniques for configuring and provisioning, with a distributed artificial intelligence (AI) fabric controller, network resources in an AI fabric for use by AI applications. In one example, the AI fabric controller is configured to discover available resources communicatively coupled to a cloud exchange; obtain a set of candidate solutions, each candidate solution of the set of candidate solutions comprising an AI application and a configuration of resources for use by the AI application; filter, based on one or more execution metrics corresponding to each of the candidate solutions, the set of candidate solutions to generate a filtered set of candidate solutions; generate provisioning scripts for the filtered set of candidate solutions; execute the provisioning scripts to provision resources for each candidate solution in the filtered set of candidate solutions; and create an execution environment for each candidate solution in the filtered set of candidate solutions.

Distributed artificial intelligence fabric controller

It is detected that a metric associated with a first workload has breached a first threshold. It is determined that the first workload and a second workload access the same storage resources, wherein the storage resources are associated with a storage server. It is determined that the metric is impacted by the first workload and the second workload accessing the same storage resources. A candidate solution is identifier. An estimated impact of a residual workload is determined based, at least in part, on the candidate solution. A level of caching of at least one of the first workload or the second workload is adjusted based, at least in part, on the estimated impact of the residual workload.

Dynamic instantiation and management of virtual caching appliances

In general, this disclosure describes a multi-zone secure AI exchange. The multi-zone secure AI exchange may be implemented in a multi-cloud, multi-data center environment, where each zone may be in a different cloud or data center. The multi-zone secure AI exchange may include a data repository, a data exchange, and shared services. The data repository may be configured to store algorithms and datasets, each having a respective owning user. The data exchange may receive datasets and algorithms from the data repository, and may perform the algorithms to produce output data. Each of the data repository, data exchange, and shared services may have a different level of security. The data repository may implement the highest level of security, allowing the owner user, and only the owning user, to control how their data and algorithms move in and out of the data repository, or are changed while in the data repository.

Multi-zone secure artificial intelligence exchange and hub

An embodiment of the invention provides an apparatus and method for classifying a workload of a computing entity. In an embodiment, the computing entity samples a plurality of values for a plurality of parameters of the workload. Based on the plurality of values of each parameter, the computing entity determines a parameter from the plurality of parameters that the computing entity's response time is dependent on. Here, the computing entity's response time is indicative of a time required by the computing entity to respond to a service request from the workload. Further, based on the identified significant parameter, the computing entity classifies the workload of the computing entity by selecting a workload classification from a plurality of predefined workload classifications.

Workload identification

In one example, a method includes receiving, by a programmable network platform (PNP), a data transfer order for data from a first customer network located in a first data center to a second customer network located in a second data center; provisioning, by the PNP, a first interconnection from the first customer network to a first data transfer station of the first data center; notifying, by the PNP, the first customer network to send the data to the first data transfer station; directing, by the PNP, a transport vehicle to transport the data from the first data transfer station to a second data transfer station of the second data center; provisioning, by the PNP, a second interconnection from the second data transfer station to the second customer network; and notifying, by the PNP, the second customer network of the readiness of the data at the second data transfer station.

Inter-data center data transfer using unmanned vehicles

Techniques described in this disclosure are directed to a co-location facility provider generating an inter-metropolitan area service chain for application of a plurality of services offered by cloud service providers located in geographically distributed metropolitan areas. In some examples, a method includes receiving, by a controller executing at a computing device of a co-location facilities provider, a request for a plurality of services to be applied to data of a customer; in response to receiving the request, generating, by the controller, a service chain for application of each of the plurality of services provided by a different one of a plurality of cloud service providers, wherein the services are applied by each of the plurality of cloud service providers at co-location facilities in geographically distributed metropolitan areas; and providing, by the controller and to the customer, the service chain for application of the plurality of services.

Inter-metro service chaining

In some examples, a computing device comprises at least one computer processor; and a memory comprising instructions that when executed by the at least one computer processor cause the at least one computer processor to: receive feature usage data indicating respective usages of a plurality of network device features configured for the network device; receive resource utilization data indicating resource utilization of the network device resource by each of the network device features at different usages; determine, based on the features usage data and the resource utilization data, respective resource utilizations of the network device resource by the plurality of network device features; combine the respective resource utilizations of the network device resource to determine a total network device resource utilization for the network device resource; and output an indication of the total network device resource utilization for the network device resource.

Dynamic capacity assessment of devices in a data center

In some examples, a method includes acquiring status information of at least one resource of a data center, estimating, based on the status information, whether load on a power supply will exceed a threshold value at a time subsequent a time of the estimating, and in response to estimating that load on the power supply will exceed the threshold value at the time subsequent the time of the estimating, transmitting a command signal to a server rack that is coupled to the power supply to configure equipment of the server rack to operate at a reduced power supply level that is reduced from a power supply level at the time of the estimating and that is based on a service level agreement associated with the server rack.

Rack level power control

Technology is disclosed for managing network storage services by service level objectives (SLOs). The method receives multiple service level capability (SLC) templates; creates at least one storage service level (SSL) instance using at least one of the SLC templates; provisions a storage object located in a network storage infrastructure based on the SSL instance; and services storage requests using the storage object.

Network storage management at scale using service level objectives

Dynamic installation and management of virtual caching appliances

In some examples, a method includes identifying, by a service overlay model engine executing at a computing device of a co-location facility provider, a plurality of service providers offering a service, the plurality of service providers co-located at one or more co-location facilities of the co-location facility provider; obtaining, by the service overlay model engine, first data obtained from the plurality of service providers describing the service; generating, by the service overlay model engine, second data describing the service based on one or more attributes of the service as delivered by the plurality of service providers and determined for the one or more co-location facilities by the co-location facility provider; and outputting, by the service overlay model engine and for display to a display device, based on a request for the service by a co-location facility customer, information based on the first data and the second data.

Service overlay model for a co-location facility

Growing community of inventors

San Jose, CA, United States of America

Kaladhar Voruganti