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Samujjwal Bhandari

from San Jose, CA
Age ~46
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Related to:
Krishna B Bhandari, 47Kripesh Bhandari
Connected to:
Akshay A Bhandari, 48Allison F Bhandari, 66Anil Bhandary, 48Anita Bhandari, 53Arpan Bhandari, 101Arushi Bhandari, 47Arvin S Bhandari, 69Barun B Bhandari, 49Bibek Bhandari, 43Darshan G Bhandari, 41

Samujjwal Bhandari Phones & Addresses

  • 16 Chatham Ct, San Jose, CA 95139
  • Newark, CA
  • Santa Clara, CA
  • Lubbock, TX

Work

Company: Texas tech university Sep 2008 Position: Research assistant

Education

School / High School: TexasTech University- Lubbock, TX 2008 Specialities: PhD in Computer Science

Skills

Event Stream Processing • Programming Languages (Java • Python • C • C++) • Data Mining • Machine Learning.

Resumes

Resumes

Samujjwal Bhandari Photo 1

Samujjwal Bhandari Lubbock, TX

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Work:
Texas Tech University

Sep 2008 to 2000
Research Assistant
Little Angel's College of Management
Lalitpur, Nepal
Aug 2005 to Jul 2008
Visiting Lecturer
Tribhuvan University
Kathmandu
Jan 2005 to Jul 2008
Lecturer
Education:
TexasTech University
Lubbock, TX
2008 to 2013
PhD in Computer Science
Tribhuvan University
Kathmandu
2002 to 2004
MSCSIT in Computer Science
Skills:
Event Stream Processing, Programming Languages (Java, Python, C, C++), Data Mining, Machine Learning.

Publications

Us Patents

Third-Party Network Computing Resource Management

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US Patent:
20190327179, Oct 24, 2019
Filed:
Apr 20, 2018
Appl. No.:
15/958347
Inventors:
- Santa Clara CA, US
Chowie Chunyan Lin - San Jose CA, US
Ashok Ganesan - Milpitas CA, US
Samujjwal Bhandari - Santa Clara CA, US
Arjun Badarinath - San Jose CA, US
International Classification:
H04L 12/911
H04L 12/24
H04L 12/26
Abstract:
A remote network management platform may include a database containing records relating to units of reserved and on-demand computing resources provided by a third-party network and a processor disposed within a computational instance. The processor may be configured to obtain utilization reports regarding the managed network from the third-party network and calculate, for each hour-of-day across one or more days of usage, respective hourly average units of utilization, by the managed network, of the reserved and on-demand computing resources. The processor may also calculate output values respectively associated with different combinations of the reserved and on-demand computing resources that jointly satisfy the hourly average units of utilization and select an allocation of the reserved computing resources that is within a threshold of a minimum output value of the output values. The processor may further change the number of allocated units to be the selected allocation of reserved computing resources.
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Samujjwal Bhandari from San Jose, CA, age ~46 Get Report