MUMBAI, India, July 11 -- Intellectual Property India has published a patent application (202521059663 A) filed by Mrs. Babeetta Bbhagat; and Dr. Pallavi Jha, Pune, Maharashtra, on June 21, for 'kookaburra-walrus optimization algorithm for multi-objective task scheduling in cloud.'
Inventor(s) include Mrs. Babeetta Bbhagat; and Dr. Pallavi Jha.
The application for the patent was published on July 11, under issue no. 28/2025.
According to the abstract released by the Intellectual Property India: "Kookaburra-Walrus Optimization Algorithm for Multi-Objective Task Scheduling in Cloud Abstract This paper provides a comprehensive survey on task scheduling in cloud computing environments, highlighting its critical role in ensuring efficient resource allocation, maintaining service quality, and optimizing cost-effectiveness. The introduction establishes that despite cloud computing's scalable and flexible offerings, the dynamic and heterogeneous nature of these systems renders traditional static scheduling algorithms insufficient. The literature survey delves into various advanced task scheduling algorithms, including hybrid and enhanced metaheuristic approaches like HO-SSA, IARO-PS, and ICSO, as well as fault-tolerant and neighborhood-based schedulers. A particular emphasis is placed on novel bio-inspired algorithms such as the Walrus Optimization Algorithm (WaOA) and the Kookaburra Optimization Algorithm (KOA), discussing their operational phases and how they balance exploration and exploitation to overcome complex optimization challenges. The survey also identifies key research gaps, noting limitations in scalability, insufficient integration of machine learning, and overlooked considerations for edge and fog computing. Regarding methodology, the paper outlines a proposed framework where a task scheduler allocates user-submitted tasks to virtual machines based on critical constraints such as waiting time, makespan, resource utilization, and execution time. Looking to the future, the research emphasizes the need for more dynamic and adaptive algorithms, deeper integration of AI and machine learning for predictive modeling, a focus on multi-objective optimization, and the expansion of scheduling strategies to distributed cloud, edge, and fog environments, while also addressing security and trust aspects."
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