International Journal of Computer Network and Information Security (IJCNIS)
IJCNIS Vol. 17, No. 6, 8 Dec. 2025
Cover page and Table of Contents: PDF (size: 881KB)
A4C: A Novel Hybrid Algorithm for Resource-Aware Scheduling in Cloud Environment
PDF (881KB), PP.65-83
Views: 0 Downloads: 0
Author(s)
Santhosh Kumar Medishetti
1,*
Bigul Sunitha Devi
2
Maheswari Bandi
3
Rani Sailaja Velamakanni
1
Rameshwaraiah Kurupati
4
Ganesh Reddy Karri
5
Index Terms
Task Scheduling, NP-hard Problem, Metaheuristic Algorithms, Resource Utilization, ACO, A3C
Abstract
Scheduling in cloud computing is an NP-hard problem, where traditional metaheuristic algorithms often fail to deliver approximate solutions within a feasible time frame. As cloud infrastructures become increasingly dynamic, efficient Task Scheduling (TS) remains a major challenge, especially when minimizing makespan, execution time, and resource utilization. To address this, we propose the Ant Colony Asynchronous Advantage Actor-Critic (A4C) algorithm, which synergistically combines the exploratory strengths of Ant Colony Optimization (ACO) with the adaptive learning capabilities of the Asynchronous Advantage Actor-Critic (A3C) model. While ACO efficiently explores task allocation paths, it is prone to getting trapped in local optima. The integration with A3C overcomes this limitation by leveraging deep reinforcement learning for real-time policy and value estimation, enabling adaptive and informed scheduling decisions. Extensive simulations show that the A4C algorithm improves throughput by 18.7%, reduces makespan by 16%, execution time by 14.60%, and response time by 21.4% compared to conventional approaches. These results validate the practical effectiveness of A4C in handling dynamic workloads, reducing computational overhead, and ensuring timely task completion. The proposed model not only enhances scheduling efficiency but also supports quality-driven service delivery in cloud environments, making it well-suited for managing complex and time-sensitive cloud applications.
Cite This Paper
Santhosh Kumar Medishetti, Bigul Sunitha Devi, Maheswari Bandi, Rani Sailaja Velamakanni, Rameshwaraiah Kurupati, Ganesh Reddy Karri, " A4C: A Novel Hybrid Algorithm for Resource-Aware Scheduling in Cloud Environment", International Journal of Computer Network and Information Security(IJCNIS), Vol.17, No.6, pp.65-83, 2025. DOI:10.5815/ijcnis.2025.06.05
Reference
[1]Prity, Farida Siddiqi, Md Hasan Gazi, and KM Aslam Uddin. "A review of task scheduling in cloud computing based on nature-inspired optimization algorithm." Cluster computing 26.5 (2023): 3037-3067. https://doi.org/10.1007/s10586-023-04090-y
[2]Sandhu, Ramandeep, et al. "Enhancement in performance of cloud computing task scheduling using optimization strategies." Cluster Computing (2024): 1-24. https://doi.org/10.1007/s10586-023-04254-w
[3]Chandrashekar, Chirag, et al. "HWACOA scheduler: Hybrid weighted ant colony optimization algorithm for task scheduling in cloud computing." Applied Sciences 13.6 (2023): 3433. https://doi.org/10.3390/app13063433
[4]Wei, Pengcheng, et al. "VMP-A3C: virtual machines placement in cloud computing based on asynchronous advantage actor-critic algorithm." Journal of King Saud University-Computer and Information Sciences 35.5 (2023): 101549. https://doi.org/10.1016/j.jksuci.2023.04.002
[5]Mangalampalli, Sudheer, et al. "Multi-objective Prioritized Task Scheduler using improved Asynchronous advantage actor critic (a3c) algorithm in multi cloud environment." IEEE Access (2024). DOI: 10.1109/ACCESS.2024.3355092
[6]Kumar, M. Santhosh, and Ganesh Reddy Kumar. "EAEFA: An Efficient Energy-Aware Task Scheduling in Cloud Environment." EAI Endorsed Transactions on Scalable Information Systems 11.3 (2024). https://doi.org/10.4108/eetsis.3922
[7]Zuo, Liyun, et al., "A multi-objective optimization scheduling method based on the ant colony algorithm in cloud computing", Ieee Access, Vol. 3, pp. 2687-2699, 2015. DOI: 10.1109/ACCESS.2015.2508940
[8]Lin, Bing, et al., "A pretreatment workflow scheduling approach for big data applications in multicloud environments", IEEE Transactions on Network and Service Management, Vol. 13, No. 3, pp. 581-594, 2016. DOI: 10.1109/TNSM.2016.2554143
[9]Lin, Xue, et al., "Task scheduling with dynamic voltage and frequency scaling for energy minimization in the mobile cloud computing environment", IEEE Transactions on Services Computing, Vol. 8, No. 2, pp.175-186, 2014. DOI: 10.1109/TSC.2014.2381227
[10]Cheng, Feng, et al., "Cost-aware job scheduling for cloud instances using deep reinforcement learning", Cluster Computing, pp. 1-13, 2022. https://doi.org/10.1007/s10586-021-03436-8
[11]Zhou, Zhou, et al., "A modified PSO algorithm for task scheduling optimization in cloud computing", Concurrency and Computation: Practice and Experience, Vol. 30, No. 24, pp. e4970, 2018. https://doi.org/10.1002/cpe.4970
[12]Jangu, Nupur, and Zahid Raza., "Improved Jellyfish Algorithm-based multi-aspect task scheduling model for IoT tasks over fog integrated cloud environment", Journal of Cloud Computing, Vol. 11, No. 1, pp. 1-21, 2022. https://doi.org/10.1186/s13677-022-00376-5
[13]Singh, Gyan, and Amit K. Chaturvedi., "Hybrid modified particle swarm optimization with genetic algorithm (GA) based workflow scheduling in cloud-fog environment for multi-objective optimization", Cluster Computing, pp. 1-18, 2023. https://doi.org/10.1007/s10586-023-04071-1
[14]Zahra, Movahedi, Defude Bruno, and Amir mohammad Hosseininia., "An efficient population-based multi-objective task scheduling approach in fog computing systems." Journal of Cloud Computing, Vol. 10, No. 1, 2021. https://doi.org/10.1186/s13677-021-00264-4
[15]Iftikhar, Sundas, et al., "HunterPlus: AI based energy-efficient task scheduling for cloud–fog computing environments", Internet of Things Vol. 21, pp. 100667, 2023. https://doi.org/10.1016/j.iot.2022.100667
[16]Yin, Zhenyu, et al., "A multi-objective task scheduling strategy for intelligent production line based on cloud-fog computing", Sensors, Vol. 22, No. 4, pp. 1555, 2022. https://doi.org/10.3390/s22041555
[17]Pham, Xuan-Qui, et al., "A cost-and performance-effective approach for task scheduling based on collaboration between cloud and fog computing", International Journal of Distributed Sensor Networks, Vol. 13, No. 11, pp. 1550147717742073, 2017. https://doi.org/10.1177/1550147717742073
[18]Mangalampalli, Sudheer, Ganesh Reddy Karri, and Mohit Kumar., "Multi objective task scheduling algorithm in cloud computing using grey wolf optimization", Cluster Computing, pp. 1-20, 2022. https://doi.org/10.1007/s10586-022-03786-x
[19] Hosseinioun, Pejman, et al., "A new energy-aware tasks scheduling approach in fog computing using hybrid meta-heuristic algorithm", Journal of Parallel and Distributed Computing, Vol. 143, pp. 88-96, 2020. https://doi.org/10.1016/j.jpdc.2020.04.008
[20]Liu, Lindong, et al. "A task scheduling algorithm based on classification mining in fog computing environment." Wireless Communications and Mobile Computing, 2018. https://doi.org/10.1155/2018/2102348
[21]Bakshi, Mohana, Chandreyee Chowdhury, and Ujjwal Maulik., "Cuckoo search optimization-based energy efficient job scheduling approach for IoT-edge environment", The Journal of Supercomputing, pp. 1-29, 2023. https://doi.org/10.1007/s11227-023-05358-1
[22]Badri, Sahar, et al., "An Efficient and Secure Model Using Adaptive Optimal Deep Learning for Task Scheduling in Cloud Computing", Electronics, Vol. 12, No. 6, pp. 1441, 2023. https://doi.org/10.3390/electronics12061441
[23]Ahmed, Omed Hassan, et al., "Using differential evolution and Moth–Flame optimization for scientific workflow scheduling in fog computing", Applied Soft Computing, Vol. 112, pp. 107744, 2021. https://doi.org/10.1016/j.asoc.2021.107744
[24]Mangalampalli, Sudheer, Sangram Keshari Swain, and Vamsi Krishna Mangalampalli. "Multi objective task scheduling in cloud computing using cat swarm optimization algorithm." Arabian Journal for Science and Engineering 47.2 (2022): 1821-1830. https://doi.org/10.1007/s13369-021-06076-7
[25]Kumar, M. Santhosh, and Ganesh Reddy Karri. "Eeoa: cost and energy efficient task scheduling in a cloud-fog framework." Sensors 23.5 (2023): 2445. https://doi.org/10.3390/s23052445
[26]Calheiros, Rodrigo N., et al. "CloudSim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms." Software: Practice and experience 41.1 (2011): 23-50. https://doi.org/10.1002/spe.995
[27]Xing, Huanlai, et al. "An ACO for energy-efficient and traffic-aware virtual machine placement in cloud computing." Swarm and Evolutionary Computation 68 (2022): 101012. https://doi.org/10.1016/j.swevo.2021.101012
[28]Mangalampalli, Sudheer, et al. "Efficient Hybrid DDPG task scheduler for HPC and HTC in cloud environment." IEEE Access (2024). DOI: 10.1109/ACCESS.2024.3435914