Priority based parallel processing multi user multi task scheduling algorithm
Downloads
Published
DOI:
https://doi.org/10.58414/SCIENTIFICTEMPER.2025.16.2.04Keywords:
Task scheduling, Multi User, Parallel Processing, Edge server, Data centreDimensions Badge
Issue
Section
License
Copyright (c) 2025 The Scientific Temper

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
Mobile Edge computing is one of the emerging fields in cloud environments where numerous user applications leverage a wide range of strong and powerful resources. To ensure optimal utilization, cloud computing resources such as storage, applications, and other services require effective management and scheduling. Managing resources is particularly challenging in scientific workflows, which involve extensive computations and interdependent operations. Task scheduling is the crucial challenge in this setup since the edge setup is migrated near to the user’s environment most of the computation is going to be handled by the edge server. Various algorithms and techniques have been proposed to address this issue. This paper explores a novel scheduling method for tasks offloaded by different users in a multi-user access computing paradigm. Also, the priority of the task is being considered while the tasks from mobile users are assigned to the data center. Considering the priority of the task, the tasks are being scheduled parallelly to the data centers. The completion time and the CPU utilization are extremely enhanced by using the proposed PBPPMUMTSA- Priority Based Parallel Processing Multi User Multi Task Scheduling Algorithm.Abstract
How to Cite
Downloads
Similar Articles
- Sarika A. Nirmal, Nalanda D. Wani, The Relationship Between Artificial Intelligence and Consumer Decision Making in the Context of Personalized Cosmetic Products , The Scientific Temper: Vol. 16 No. 09 (2025): The Scientific Temper
- C. Premila Rosy, Clustering of cancer text documents in the medical field using machine learning heuristics , The Scientific Temper: Vol. 16 No. 05 (2025): The Scientific Temper
- K. Mohamed Arif Khan, A.R. Mohamed Shanavas, Energy efficient techniques for iot application on resource aware fog computing paradigm , The Scientific Temper: Vol. 16 No. 02 (2025): The Scientific Temper
- Abhishek Pandey, V Ramesh, Puneet Mittal, Suruthi, Muniyandy Elangovan, G.Deepa, Exploring advancements in deep learning for natural language processing tasks , The Scientific Temper: Vol. 14 No. 04 (2023): The Scientific Temper
- A. Sathya, M. S. Mythili, MOHCOA: Multi-objective hermit crab optimization algorithm for feature selection in sentiment analysis of Covid-19 Twitter datasets , The Scientific Temper: Vol. 15 No. 03 (2024): The Scientific Temper
- Sharayu Mirasdar, Mangesh Bedekar, Knowledge graphs for NLP: A comprehensive analysis , The Scientific Temper: Vol. 16 No. Spl-1 (2025): The Scientific Temper
- G. Chitra, Hari Ganesh S., Cultural algorithm based principal component analysis (CA-PCA) approach for handling high dimensional data , The Scientific Temper: Vol. 15 No. spl-1 (2024): The Scientific Temper
- Naveena Somasundaram, Vigneshkumar M, Sanjay R. Pawar, M. Amutha, Balu S, Priya V, AI-driven material design for tissue engineering a comprehensive approach integrating generative adversarial networks and high-throughput experimentation , The Scientific Temper: Vol. 15 No. 01 (2024): The Scientific Temper
- Aarthi Monalisa M, Anli Suresh, Adoptive bancassurance models transforming patronization among the insured , The Scientific Temper: Vol. 16 No. Spl-2 (2025): The Scientific Temper
- Rudrapati Bhuvaneswara Prasad, Avutala Mallikarjuna Reddy, Edge properties of lexicographic product graphs of open neighborhood graphs , The Scientific Temper: Vol. 16 No. 01 (2025): The Scientific Temper
<< < 1 2 3 4 5 6 7 8 9 10 > >>
You may also start an advanced similarity search for this article.

