Work place: VNR Vignana Jyothi Institute of Engineering and Technology, Hyderabad, Telangana, India
E-mail: sriramamurthy_k@vnrvjiet.in
Website:
Research Interests: Deep Learning
Biography
Karumuri Sri Rama Murthy received his Ph.D. in Computer Science and Engineering from JNTU Hyderabad. He is currently serving as an Assistant Professor in the Department of Computer Science and Engineering at VNR Vignana Jyothi Institute of Engineering and Technology, Hyderabad, India. With over 20 years of experience in teaching, mentoring, and curriculum development, he is a dedicated academician and researcher. He has published more than 15 research articles in reputed journals, conferences, and patents. His research interests include deep learning, machine learning, e-learning, and agroforestry.
By Santhosh Kumar Medishetti Karumuri Sri Rama Murthy Venkateshwarlu Kajjam Sudha Singaraju Rameshwaraiah Kurupati
DOI: https://doi.org/10.5815/ijitcs.2025.04.06, Pub. Date: 8 Aug. 2025
Scheduling is an NP-hard problem, and heuristic algorithms are unable to find approximate solutions within a feasible time frame. Efficient Task Scheduling (TS) in Cloud-Fog Computing (CFC) environments is crucial for meeting the diverse resource demands of modern applications. This paper introduces the Sewing Training-Based Optimization (STBO) algorithm, a novel approach to resource-aware task scheduling that effectively balances workloads across cloud and fog resources. STBO categorizes Virtual Machines (VMs) into low, medium, and high resource utilization queues based on their computational power and availability. By dynamically allocating tasks to these queues, STBO minimizes delays and ensures that tasks with stringent deadlines are executed in optimal environments, enhancing overall system performance. The algorithm leverages processing delays, task deadlines, and VM capabilities to assign tasks intelligently, reducing response times and improving resource utilization. Experimental results demonstrate that STBO outperforms existing scheduling algorithms in reducing makespan by 21.6%, improved energy usage by 31%, and maximizing throughput by 27.8%, making it well-suited for real-time, resource-intensive applications in CFC systems.
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