Genti Cuni - Mountain View CA, US Jay Steven Nitikman - Santa Cruz CA, US Andrea Mayumi Nagao - Santa Cruz CA, US Ricardo A. Negrete - Scotts Valley CA, US
Assignee:
VIRTUAL INSTRUMENTS CORPORATION - Scotts Valley CA
International Classification:
G06F 15/16
US Classification:
709224
Abstract:
A system may comprise a network diagnostic device. The network diagnostic device may be configured to instantiate objects of data collection classes. The data collection classes may be configured to collect data from nodes of a network. The data collection classes may, for instance, inherit an interface object from an ancestor data collection class, and the interface object may be configured to use a protocol to collect data from nodes of a network. The network diagnostic device may be configured to automatically detect a node type associated with a node of a network. The network diagnostic device may also be configured to instantiate a node-specific data collection object associated with the detected node type. The node-specific data collection object may be configured to collect data from nodes of the detected node type at intervals less than or equal to fifteen seconds.
Network Diagnostic Systems And Methods For Light Levels Of Optical Signals
Ricardo A. Negrete - Scotts Valley CA, US Genti Cuni - Mountain View CA, US
Assignee:
VIRTUAL INSTRUMENTS CORPORATION - Scotts Valley CA
International Classification:
G06F 11/28 G08B 21/00 G01J 1/42
US Classification:
714 37, 340540, 356218
Abstract:
A network diagnostic system may include a network diagnostic device. The network diagnostic device may be configured to receive data indicating a light level of an optical signal and to perform at least one network diagnostic function at least partially in response to the receipt of the data. A network diagnostic method may include detecting a light level of an optical signal; and performing at least one network diagnostic function at least partially in response to the detection of the light level of the optical signal. Exemplary network diagnostic functions may include triggering an alarm; triggering a capture of at least a portion of one or more network messages; storing data indicating the light level of the optical signal on a computer readable medium (e.g., for use in subsequent reports); and/or any other suitable network diagnostic function.
- Palo Alto CA, US Rangaswamy Jagannathan - Sunnyvale CA, US Michael Bello - Mountain View CA, US Ricardo A. Negrete - Scotts Valley CA, US Elizaveta Tavastcherna - San Jose CA, US Vitoo Suwannakinthorn - San Jose CA, US
Assignee:
Virtual Instruments Worldwide, Inc. - Palo Alto CA
International Classification:
H04L 41/0803 H04L 67/10 H04L 9/40 H04L 41/14
Abstract:
A method comprising discovering workload attributes and identify dependencies, receiving utilization performance measurements including memory utilization measurements of at least a subset of workloads, grouping workloads based on the workload attributes, the dependencies, and the utilization performance measurements into affinity groups, determining at least one representative synthetic workload for each affinity group, each representative synthetic workload including a time slice of a predetermined period of time when there are maximum performance values for any number of utilization performance measurements among virtual machines of that particular affinity group, determining at least one cloud service provider (CSP)'s cloud services based on performance of the representative synthetic workloads, and generating a report for at least one of the representative synthetic workloads, the report identifying the at least one of the representative synthetic workloads and the at least one CSP's cloud services including cloud workload cost.
- San Jose CA, US Rangaswamy Jagannathan - San Jose CA, US Michael Bello - Mountain View CA, US Ricardo A. Negrete - Scotts Valley CA, US Elizaveta Tavastcherna - San Jose CA, US Vitoo Suwannakinthorn - San Jose CA, US
Assignee:
Virtual Instruments Worldwide, Inc. - San Jose CA
International Classification:
H04L 29/08 H04L 12/26
Abstract:
A method comprising discovering workload attributes and identify dependencies, receiving utilization performance measurements including memory utilization measurements of at least a subset of workloads, grouping workloads based on the workload attributes, the dependencies, and the utilization performance measurements into affinity groups, determining at least one representative synthetic workload for each affinity group, each representative synthetic workload including a time slice of a predetermined period of time when there are maximum performance values for any number of utilization performance measurements among virtual machines of that particular affinity group, determining at least one cloud service provider (CSP)'s cloud services based on performance of the representative synthetic workloads, and generating a report for at least one of the representative synthetic workloads, the report identifying the at least one of the representative synthetic workloads and the at least one CSP's cloud services including cloud workload cost.
- San Jose CA, US Jagan Jagannathan - San Jose CA, US Michael Bello - Mountain View CA, US Ricardo A. Negrete - Scotts Valley CA, US Elizaveta Tavastcherna - San Jose CA, US Vitoo Suwannakinthorn - San Jose CA, US
International Classification:
H04L 29/08 H04L 12/26
Abstract:
A method comprising discovering workload attributes and identify dependencies, receiving utilization performance measurements including memory utilization measurements of at least a subset of workloads, grouping workloads based on the workload attributes, the dependencies, and the utilization performance measurements into affinity groups, determining at least one representative synthetic workload for each affinity group, each representative synthetic workload including a time slice of a predetermined period of time when there are maximum performance values for any number of utilization performance measurements among virtual machines of that particular affinity group, determining at least one cloud service provider (CSP)'s cloud services based on performance of the representative synthetic workloads, and generating a report for at least one of the representative synthetic workloads, the report identifying the at least one of the representative synthetic workloads and the at least one CSP's cloud services including cloud workload cost.
Finisar Corporation (Nasdaq: Fnsr) 2005 - 2008
Senior Director Enterprise Engineering
Virtual Instruments 2005 - 2008
Senior Director Services and Technology
Avaya 1997 - 2005
Chief Technology Officer - Outsourcing Services
Shs Software Hardware Solutions Feb 1994 - May 2004
President and Founder
Comverse Jan 1994 - Dec 1996
Engineering Manager
Education:
Tecnológico De Monterrey
Bachelors, Bachelor of Science In Electrical Engineering, Communications
University of California, Berkeley
Skills:
Cloud Computing Enterprise Software Virtualization Product Management Pre Sales Saas Storage Software Development Professional Services Agile Methodologies Telecommunications Solution Architecture San Management Crm Data Center Start Ups Vmware Strategy Business Intelligence Linux Disaster Recovery Networking Storage Area Network Product Marketing Managed Services Storage Virtualization Testing Software Engineering Servers System Architecture Training Fibre Channel Enterprise Architecture Executive Management Storage Area Networks Solaris Technical Support Switches Service Delivery Software Design Product Development High Availability Customer Relationship Management Cross Functional Team Leadership Project Portfolio Management Hardware Development Post Sales Budget Strategic Planning