Optimizing Resource Allocation in Cloud for Large-Scale Deep Learning Models in Natural Language Processing

Main Article Content

Gauri Dhopavkar, Rashmi R. Welekar, Piyush K. Ingole, Chandu Vaidya, Shalini Vaibhav Wankhade, Bharati P. Vasgi

Abstract

The need for big deep learning models in Natural Language Processing (NLP) keeps rising, it's important to find the best way to divide up cloud resources so that they can be used efficiently and at high speeds. This solves the problems that come with setting up and handling large NLP models by suggesting a complete strategy for making the best use of cloud-based platforms' resources. Combining model parallelism, data parallelism, and dynamic scaling methods, the suggested approach spreads the computing load across multiple cloud instances in better way. The framework constantly changes how resources are allocated to handle changes in workload by taking into account the specifics of NLP tasks, such as the need for different model designs and data processing needs. To improve scale and cut down on inference delay, a new auto-scaling method is introduced that lets computing resources be changed automatically based on demand in real time. The framework uses machine learning-based prediction models to figure out what resources will be needed in the future. This lets you make proactive decisions about scaling and keeps you from underusing or overprovisioning resources. It also solves the problem of communication overhead in distributed environments by improving data exchange protocols and using advanced inter-process communication techniques. The results of the experiments show that the proposed framework works well at improving both cost-effectiveness and prediction performance for large-scale NLP models by making the best use of resources. The framework is flexible enough to work with a wide range of natural language processing (NLP) tasks. It makes a useful addition to the efficient use of deep learning models in cloud settings.

Article Details

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Articles
Author Biography

Gauri Dhopavkar, Rashmi R. Welekar, Piyush K. Ingole, Chandu Vaidya, Shalini Vaibhav Wankhade, Bharati P. Vasgi

1Dr. Gauri Dhopavkar

2Dr. Rashmi R. Welekar

3Dr. Piyush K. Ingole

4Chandu Vaidya

5Shalini Vaibhav Wankhade

6Bharati P. Vasgi

1Department of Computer Technology, Yeshwantrao Chavan College of Engineering, Nagpur, Maharashtra,India.

gauri.ycce@gmail.com

2Department of Computer Science and Engineering (Cyber Security), Shri Ramdeobaba College of Engineering and Management, Nagpur, Maharashtra, India

welekarr@rknec.edu

3Assistant Professor Department of Computer Science and Engineering, Jhulelal Institute of Technology, Nagpur , Maharashtra, India

piyush.ingole@gmail.com

4Department of Computer Science and Engineering
S. B. Jain Institute of Technology, Management and Research, Nagpur, India

chandu.nyss@gmail.com

5Vishwakarma Institute of Information Technology, Pune, Maharashtra, India

Email: shalini.wankhade@viit.ac.in

6Marathwada Mitra Mandal's College of Engineering, Pune, Maharashtra, India.

Email- bharativasgi@gmail.com

Copyright © JES 2023 on-line : journal.esrgroups.org

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