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
- Vijay Sharma, Nishu, Anshu Malhotra, An encryption and decryption of phonetic alphabets using signed graphs , The Scientific Temper: Vol. 15 No. spl-2 (2024): The Scientific Temper
- Shemal Dave, Dhaval Vyas, Jyotindra Jani, Capital adequacy and systemic risk: Evidence from selected Indian private sector banks , The Scientific Temper: Vol. 15 No. spl-2 (2024): The Scientific Temper
- Anushka Jaiswal, Neerja Pandey, Seema R Sarraf, Correlation between personality traits and coping strategies of young adults in India , The Scientific Temper: Vol. 14 No. 04 (2023): The Scientific Temper
- Sadanand Maurya, Manikant Tripathi, Karunesh K. Tiwari, Awadhesh K. Shukla, Isolation and molecular characterization of microbial isolates from Saryu river water , The Scientific Temper: Vol. 14 No. 04 (2023): The Scientific Temper
- Suprabha Amit Kshatriya, Jaymin K Bhalani, Early detection of fire and smoke using motion estimation algorithms utilizing machine learning , The Scientific Temper: Vol. 15 No. 04 (2024): The Scientific Temper
- Brijesh Singh, Ajay Massand, Determinants of Gen Z’s adoption of chatbots in online shopping: An empirical investigation , The Scientific Temper: Vol. 15 No. 04 (2024): The Scientific Temper
- Arenlila Jamir, Sangeeta Kharde, Anita Dalal, Health-seeking behavior of first-time mothers toward pregnancy , The Scientific Temper: Vol. 14 No. 04 (2023): The Scientific Temper
- P. Susai Raj, A. Edward William Benjamin, Evaluating the effectiveness of academic resilience intervention for at-risk students at higher secondary level , The Scientific Temper: Vol. 15 No. 04 (2024): The Scientific Temper
- Santima Uchukanokkul, Bijal Zaveri, Impact of emerging global educational trends on overseas education programs for aspiring students in South East Asia and South Asia: A decadal analysis , The Scientific Temper: Vol. 15 No. 04 (2024): The Scientific Temper
- T. Ramyaveni, V. Maniraj, Hyperparameter tuning of diabetes prediction using machine learning algorithm with pelican optimization algorithm , The Scientific Temper: Vol. 15 No. 03 (2024): The Scientific Temper
You may also start an advanced similarity search for this article.

