Batch Autosys
Freelancer and Trainer
Hcl Axon Jun 2011 - Dec 2014
Associate Consultant
Hcl Technologies (Infrastructure Services Division) Jun 2011 - Dec 2014
Redwood Cps and Bpa and Ca Wa Principal Consultant
Education:
S.p. Jain Institute of Management & Research 2017
College of Engineering Roorkee 2007 - 2011
Bachelors, Bachelor of Technology
Skills:
Sap Erp Business Process Erp Requirements Analysis Business Analysis Team Management Redwood Cps Workload Automation Cawa Strategy Global Delivery
Interests:
Children Education
Languages:
English Hindi
Certifications:
Cdac,Noida, License 1093/22364/Erp/(09)/2014 Sap Operations License 1093/22364/Erp/(09)/2014
Dish Wireless
Principal Architect
Ranzure Networks 2016 - 2018
Director- Business Development and Products
Jio 2016 - 2018
Director, Product Management and Development
Cisco Feb 2013 - Sep 2016
Senior Solution Architect, Mobility Engineering
Intucell 2012 - 2013
Senior Manager Products and Performance , Son Engineering and Deployment
Education:
La Martiniere College, Lucknow 1985 - 1993
University Institute of Chemical Technology (Formerly Udct)
Bachelor of Engineering, Bachelors, Electronics Engineering
Skills:
Lte Wireless Gsm 3G Rf Umts Wcdma Wireless Networking Optimization Rf Engineering Business Development Team Leadership Communications Audits Das Muti Vendor Experience Strategic Planning Process Improvement Sales Proposal Writing Project Manager Proposal Evaluation Sow Developement Innovation Customer Relations Special Events Planning and Optimization High End Network Audits Performance and Optimization Telecom Network Design Son Auditing Iot Mobility 5G Sdn Nfv Telco Cloud Big Data Analytics Mongodb Business Intelligence Cloud Ran
Citi
Senior Vice President - Citi Fintech
Citi Aug 1, 2015 - Oct 2016
Senior Vice President Global Pmo
Citi Oct 2011 - Aug 2015
It Portfolio Manager
Citi Jun 2002 - Oct 2011
Testing Manager
Education:
Texas Christian University 1998 - 2002
Bachelors, Bachelor of Science, Computer Science
Vijay K. Kapur - Tarzana CA, US Joel Haber - Pasadena CA, US Vincent Kapur - Tarzana CA, US Ashish Bansal - Porter Ranch CA, US Dan Guevarra - Norwalk CA, US
Assignee:
International Solar Electric Technology, Inc. - Chatsworth CA
International Classification:
H01L 21/20 C23C 16/00
US Classification:
438478, 118724, 118725, 257E2109
Abstract:
Described herein are systems and methods method for forming semiconductor films. In some embodiment, the methods comprising depositing the source solution containing a solvent and plurality of types of metal ionic species and a second type on a substrate heated to a temperature at or above the boiling point of the solvent. In some embodiments, methods and apparatus for exposing a substrate to a gas are also provided.
User Plane Function (Upf) Load Balancing Based On Network Data Analytics To Predict Load Of User Equipment
- ENGLEWOOD CO, US Kazi Bashir - Lewisville TX, US Ash Khamas - Goffstown NM, US Ashish Bansal - Frisco TX, US Siddhartha Chenumolu - Broadlands VA, US
International Classification:
H04W 28/08 H04W 48/18
Abstract:
Embodiments are directed towards systems and methods for user plane function (UPF) and network slice load balancing within a 5G network. Example embodiments include systems and methods for load balancing based on current UPF load and thresholds that depend on UPF capacity; UPF load balancing using predicted throughput of new UE on the network based on network data analytics; UPF load balancing based on special considerations for low latency traffic; UPF load balancing supporting multiple slices, maintaining several load-thresholds for each UPF and each slice depending on the UPF and network slice capacity; and UPF load balancing using predicted central processing unit (CPU) utilization and/or predicted memory utilization of new UE on the network based on network data analytics.
User Plane Function (Upf) Load Balancing Supporting Multiple Slices
- Englewood CO, US Kazi Bashir - Lewisville TX, US Ash Khamas - Goffstown NH, US Ashish Bansal - Frisco TX, US Siddhartha Chenumolu - Broadlands VA, US
International Classification:
H04W 28/08 H04W 48/18
Abstract:
Embodiments are directed towards systems and methods for user plane function (UPF) and network slice load balancing within a 5G network. Example embodiments include systems and methods for load balancing based on current UPF load and thresholds that depend on UPF capacity; UPF load balancing using predicted throughput of new UE on the network based on network data analytics; UPF load balancing based on special considerations for low latency traffic; UPF load balancing supporting multiple slices, maintaining several load-thresholds for each UPF and each slice depending on the UPF and network slice capacity; and UPF load balancing using predicted central processing unit (CPU) utilization and/or predicted memory utilization of new UE on the network based on network data analytics.
User Plane Function (Upf) Load Balancing Based On Central Processing Unit (Cpu) And Memory Utilization Of The User Equipment (Ue) In The Upf
- ENGLEWOOD CO, US Kazi Bashir - Lewisville TX, US Ash Khamas - Goffstown NM, US Ashish Bansal - Frisco TX, US Siddhartha Chenumolu - Broadlands VA, US
International Classification:
H04W 28/08 H04L 47/127 H04L 47/125 H04L 47/215
Abstract:
Embodiments are directed towards embodiments are directed toward systems and methods for user plane function (UPF) and network slice load balancing within a 5G network. Example embodiments include systems and methods for load balancing based on current UPF load and thresholds that depend on UPF capacity; UPF load balancing using predicted throughput of new UE on the network based on network data analytics; UPF load balancing based on special considerations for low latency traffic; UPF load balancing supporting multiple slices, maintaining several load-thresholds for each UPF and each slice depending on the UPF and network slice capacity; and UPF load balancing using predicted central processing unit (CPU) utilization and/or predicted memory utilization of new UE on the network based on network data analytics.
User Plane Function (Upf) Load Balancing Based On Special Considerations For Low Latency Traffic
- Englewood CO, US Kazi Bashir - Lewisville TX, US Ash Khamas - Goffstown NH, US Ashish Bansal - Frisco TX, US Siddhartha Chenumolu - Broadlands VA, US
International Classification:
H04W 28/08
Abstract:
Embodiments are directed towards systems and methods for user plane function (UPF) and network slice load balancing within a 5G network. Example embodiments include systems and methods for load balancing based on current UPF load and thresholds that depend on UPF capacity; UPF load balancing using predicted throughput of new UE on the network based on network data analytics; UPF load balancing based on special considerations for low latency traffic; UPF load balancing supporting multiple slices, maintaining several load-thresholds for each UPF and each slice depending on the UPF and network slice capacity; and UPF load balancing using predicted central processing unit (CPU) utilization and/or predicted memory utilization of new UE on the network based on network data analytics.
User Plane Function (Upf) Load Balancing Based On Current Upf Load And Thresholds That Depend On Upf Capacity
- Englewood CO, US Kazi Bashir - Lewisville TX, US Ash Khamas - Goffstown NH, US Ashish Bansal - Frisco TX, US Siddhartha Chenumolu - Broadlands VA, US
International Classification:
H04W 28/08 H04W 72/12
Abstract:
Embodiments are directed towards systems and methods for user plane function (UPF) and network slice load balancing within a 5G network. Example embodiments include systems and methods for load balancing based on current UPF load and thresholds that depend on UPF capacity; UPF load balancing using predicted throughput of new UE on the network based on network data analytics; UPF load balancing based on special considerations for low latency traffic; UPF load balancing supporting multiple slices, maintaining several load-thresholds for each UPF and each slice depending on the UPF and network slice capacity; and UPF load balancing using predicted central processing unit (CPU) utilization and/or predicted memory utilization of new UE on the network based on network data analytics.
State Pooling For Stateful Re-Homing In A Disaggregated Radio Access Network
- Englewood CO, US Ashish Bansal - Frisco TX, US Dhaval Mehta - Aldie VA, US Manjari Asawa - Cupertino CA, US
International Classification:
H04W 40/02
Abstract:
Techniques described herein provide pooling of radio access network (RAN) resources in a disaggregated RAN in a manner that enables dynamic re-homing of RAN functions with minimal disruption to stateful communications. The RAN is a hierarchical arrangement of RAN units, each having an associated state at any given time. A RAN state pooling system (RSPS) is in communication with a pool of the RAN units via a high-speed communication channel. The RSPS is configured to maintain an updated repository of the state information for the RAN units in its serviced pool. In the event of re-homing from a first RAN unit to a second RAN unit in the pool (e.g., due to the first RAN unit failing, becoming overloaded, etc.), the RSPS provides the second RAN unit with the updated state information for the first RAN unit to facilitate a stateful re-homing.
Interfrequency And Inter-Technology Neighbor Planning On A Self-Organizing Network
In an example, a self-organizing network (SON) provides automated interfrequency load balancing for a base station such as a NodeB. The NodeB may provide a plurality of carriers, such as in a plurality of UARFCN frequencies, and the SON may provide configuration directives for increasing efficiency. For example, when one carrier becomes loaded, the SON may update neighbor associations to take advantage of relatively unloaded frequency carriers. A plurality of scenarios S may be provided, and a policy P may be defined for each. When the NodeB encounters a scenario S, SON may send configuration directives to implement policy P. Similar concept and policy could be applied in conjunction with INTER Technology Neighbor Definitions between LTE and UMTS and UMTS and GSM. Example if GSM Frequency Neighbors needs to be replaced with different Frequency Neighbors from UMTS based on Load or RF conditions.
Guru Gobind Singh Indraprastha University - B.Tech
Ashish Bansal
Work:
HCL Technologies - Software Engineer (10-2011)
Education:
Thapar University - BE Computer Sciences
Ashish Bansal
Work:
TCS - ASE (9)
Education:
BVU - Computer Application
Ashish Bansal
Work:
ORACLE India (2011)
Education:
JIET - BTech ECE
Ashish Bansal
Education:
Iipm - Noida
Tagline:
A person who has good thoughts cannot ever be ugly. You can have a wonky nose and a crooked mouth and a double chin and stick-out teeth, but if you have good thoughts they will shine out of your face like sunbeams and you will always look lovely.
Ashish Bansal
Work:
Advocate (2011)
Tagline:
If you avoid DIFFICULTIES you avoid your GROWTH !!!
Ashish Bansal
Work:
Amrit Group - Finance Manager
Education:
ICAI - CA
Ashish Bansal
Education:
Institute of Chartered Accountants of India - Commerce