Open Access
·Peer-reviewed·ISSN (Online): 2169-0103·ISSN (Print): 0252-2667
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The Journal of Information and Optimization Sciences (JIOS) is a world leading journal publishing high quality, rigorously peer-reviewed original research in all mathematically-oriented theoretical and applied topics in information sciences, optimization sciences and related areas since 1980. Subjects include but are not limited to:
• Information Sciences
• Optimization Sciences
• Control Theory
• Operational Research
• Decision Sciences
• Information Theory
• Information Technology
• Computer Networks and Communications
• Mathematical Programming
• Modelling and Simulation
• Database Management
• Applications to Engineering Sciences
• Applications to Technology
Issues up to 2022 co-published with and available at:
Implementing machine learning for optimized resource allocation in cloud computing environments
Leena Bharat Chaudharileenapc23@gmail.comDepartment of Electronics and Telecommunication Bharati Vidyapeeth College of Engineering LavalePune, Maharashtra, 412115, IndiaView full profile →
, Manisha Sagar Pawarmanisha.pawar@vit.eduDepartment of Engineering Science and Humanities Vishwakarma Institute of TechnologyPune, Maharashtra, 411037, IndiaView full profile →
, Anjali Bhardwajanjali.bhardwaj@niu.edu.inDepartment of Computer Science & Engineering Noida International UniversityGreater Noida, Uttar Pradesh, 203201, IndiaView full profile →
, *Mahesh SoniCorresponding authormahesh.times@gmail.comNassau Financial GroupHartford, Connecticut, 06102, United States of AmericaView full profile →
, Deepak Suresh Asudanideepak.s.asudani@gmail.comDepartment of Computer Science and Engineering Symbiosis Institute of Technology Nagpur Campus Symbiosis International (Deemed University)Symbiosis Institute of Technology Nagpur Campus Symbiosis International (Deemed University) Pune, Maharashtra, 412115, IndiaView full profile →
, C. K. Rajashrirajashrick@maher.ac.inDepartment of Computer Science Meenakshi College of Arts and Science Meenakshi Academy of Higher Education and ResearchChennai, Tamil Nadu, 600078, IndiaView full profile →
* Corresponding author · click or hover a name for details
Self-driving cars need to learn how to drive in places that change all the time, like when the weather changes, the traffic patterns change, or the noise from sensors changes. RDS makes guidance more reliable and useful, which helps these cars get ready for and deal with these kinds of unknowns. These methods help self-driving cars get ready for problems, change their routes on the fly, and keep working well even when things go wrong. Compared to traditional methods, the results show improvements of up to 25% in utilisation, ~70% reduction in SLA violations, 23% lower energy consumption, and 24% lower cost.
[1] N. R. Varun, D. P. Shashank, T. S. Agarwal, H. M. Varun, and D. Shashank, “Optimizing Cloud Resource Allocation with AI and Machine Learning,” in Proc. 2025 Int. Conf. Comput. Technol. (ICOCT), Bengaluru, India, pp. 1–5 (2025). doi: 10.1109/ICOCT64433.2025.11118766.[2] Y. Zhang, B. Liu, Y. Gong, J. Huang, J. Xu, and W. Wan, “Application of Machine Learning Optimization in Cloud Computing Resource Scheduling and Management,” in Proc. 5th Int. Conf. Comput. Inf. Big Data Appl. (CIBDA ’24), New York, USA, pp. 171–175 (2024). doi: 10.1145/3671151.3671183.[3] A. S. Shete, S. Bhutada, M. B. Patil, P. H. Sen, N. Jain, and P. Khobragade, “Blockchain technology in pharmaceutical supply chain : Ensuring transparency, traceability, and security”, Journal of Statistics and Management Systems, vol. 27, no. 2, pp. 417–428 (2024), DOI: 10.47974/JSMS-1266.[4] S. Sharma and P. S. Rawat, “Efficient resource allocation in cloud environment using SHO-ANN-based hybrid approach,” Sustain. Oper. Comput., vol. 5, pp. 141–155 (2024). doi: 10.1016/j.susoc.2024.07.001.[5] L. Geng, J. Han, J. Jia, C. Dong, and R. Zhao, “Dynamic Programming of Complex Tasks Based on Regretting Greedy Algorithm,” in Proc. 2025 IEEE 20th Conf. Ind. Electron. Appl. (ICIEA), pp. 1–5 (2025). doi: 10.1109/ICIEA65512.2025.11149054.[6] H. Ghahremani, M. Damrudi, A. Ghaffari, and K. Aval, “Quality of Service Enhancement in Mobile Crowdsensing Through Metaheuristic Techniques: A Survey,” Concurrency Comput. Pract. Exp., pp. 1–20 (2025). doi: 10.1002/cpe.7016.[7] S. K. Mandal, R. Singh, N. Chandu, D. Mehta, S. K. Henge, D. Kothapeta, C. K. Hinge, A. Sharma, and T. Hussain, “Evolutionary multi-authentication cryptographic key implications for secure data access control,” Journal of Discrete Mathematical Sciences and Cryptography, vol. 28, no. 5-B, pp. 1865–1874 (2025), doi: 10.47974/JDMSC-2363.[8] M. Shifrin, R. Mitrany, E. Biton, and O. Gurewitz, “VM scaling and load balancing via cost optimal MDP solution,” IEEE Trans. Cloud Comput., vol. 7, no. 3, pp. 41–44 (2020).[9] S. Anjum, A. Ahmed, R. Kurup, S. Kidiya, and S. Kureshi, “Blockchain Based Image Steganography”, Int Journal Adv Comp Theory Engg, vol. 14, no. 1, pp. 147–152 (May 2025).
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