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Chaitra Jayaram

from Pleasanton, CA
Age ~45
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Also known as:
Gururaj Pare
Related to:
Sylvia E Mcculloch, 88John F Mcculloch, 91Raviraj Pare, 43
Connected to:
Aditya Jayaram, 56Akhil Jayaram, 34Anil K Jayaraman, 56Anusha S Jayaram, 31Arthi Jayaraman, 45Dharani Jayaraman, 40Girish B Jayaram, 54Gopal Jayaraman, 49Gowtham Jayaram, 56Haricharan Jayaramachandra

Chaitra Jayaram Phones & Addresses

  • Pleasanton, CA
  • San Jose, CA
  • Sunnyvale, CA
  • Chicago, IL
  • Mount Pleasant, MI

Work

Company: Oracle Dec 2013 to Nov 1, 2015 Position: Senior member of technical staff

Education

Degree: Master of Science, Masters School / High School: Central Michigan University 2003 to 2005 Specialities: Computer Science

Skills

Java • Unix • Software Engineering • C++ • Software Development • High Availability • Distributed Systems • Agile Methodologies • Python • C • J2Ee Application Development • Xml • Rest • Cross Platform • Shell Scripting • Cross Platform Development • Soap • Scons • Jenkins • Webdav • Java Enterprise Edition • Scrum • Hibernate • Open Source • Tcp/Ip • Jboss Application Server • Databases • Linux

Industries

Computer Software

Resumes

Resumes

Chaitra Jayaram Photo 1

Senior Member Of Technical Staff

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Location:
San Francisco, CA
Industry:
Computer Software
Work:
Oracle Dec 2013 - Nov 1, 2015
Senior Member of Technical Staff
Cisco Nov 2007 - Dec 2013
Software Engineer
Reconnex Jan 1, 2006 - Nov 1, 2007
Software Engineer
Ctb Mcgraw Hill Oct 2005 - Jan 2006
Developer
Education:
Central Michigan University 2003 - 2005
Master of Science, Masters, Computer Science Karnataka Law College, Dharwad Mar 2002
Karnataka Law College, Dharwad
Bachelor of Engineering, Bachelors, Computer Science
Skills:
Java
Unix
Software Engineering
C++
Software Development
High Availability
Distributed Systems
Agile Methodologies
Python
C
J2Ee Application Development
Xml
Rest
Cross Platform
Shell Scripting
Cross Platform Development
Soap
Scons
Jenkins
Webdav
Java Enterprise Edition
Scrum
Hibernate
Open Source
Tcp/Ip
Jboss Application Server
Databases
Linux

Publications

Us Patents

System And Method For Managing Data And Policies

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US Patent:
20130246925, Sep 19, 2013
Filed:
Mar 25, 2009
Appl. No.:
12/410905
Inventors:
Ratinder Paul Singh Ahuja - Saratoga CA, US
Joel Christener - San Jose CA, US
Jitendra Gaitonde - Cupertino CA, US
Sirisha Ganti - Sunnyvale CA, US
Sridevi Haridasa - San Jose CA, US
Damodar K. Hegde - Cupertino CA, US
Chaitra Jayaram - Sunnyvale CA, US
Rahila Kasim - Santa Clara CA, US
Faizel Lakhani - Campbell CA, US
Swati Patil - Sunnyvale CA, US
Harsimran S. Sandhu - Mountain View CA, US
International Classification:
G06F 15/173
G06F 3/00
US Classification:
715738, 709224
Abstract:
In one embodiment, a method is provided and includes capturing a plurality of packet streams, recreating a plurality of flows from the packet streams, and analyzing the flows to identify one or more incidents. The incidents indentify one or more pieces of data. The incidents are filtered and the incidents are rendered on a display for an end user that initiated the filtering operation. In other embodiments, the display allows the end user to view a selected one of a group of attributes for the incidents. The display allows the end user to open a captured object associated with a specific incident. In still other embodiments, the display allows a user to filter the incidents using a selected one of a group of group options such as content, destination IP, destination location, destination port, filename, host IP, etc.

Rate Limiting Compliance Assessments With Multi-Layer Fair Share Scheduling

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US Patent:
20210132993, May 6, 2021
Filed:
Oct 31, 2019
Appl. No.:
16/670072
Inventors:
- Redwood Shores CA, US
Prasad Ravuri - San Jose CA, US
Karl Dias - Foster City CA, US
Gayatri Tripathi - Bengaluru, IN
Shriram Krishnan - Oakland CA, US
Chaitra Jayaram - Pleasanton CA, US
Assignee:
Oracle International Corporation - Redwood Shores CA
International Classification:
G06F 9/50
G06F 9/54
G06F 9/32
Abstract:
The embodiments disclosed herein relate to predictive rate limiting. A workload for completing a request is predicted based on, for example, characteristics of a ruleset to be applied and characteristics of a target set upon which the ruleset is to be applied. The workload is mapped to a set of tokens or credits. If a requestor has sufficient tokens to cover the workload for the request, the request is processed. The request may be processed in accordance with a set of processing queues. Each processing queue is associated with a maximum per-tenant workload. A request may be added to a processing queue as long as adding the request does not result in exceeding the maximum per-tenant workload. Requests within a processing queue may be processed in a First In First Out (FIFO) order.
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Chaitra Jayaram from Pleasanton, CA, age ~45 Get Report