Citation :

Sidagouda Basagouda Patil, Mukund A kulkarni, "Secure Resource Allocation in the Cloud for Seamless Transactions using Average Sample Learning Strategy-based Horse Herd Optimization," International Journal of Engineering Trends and Technology (IJETT), vol. 74, no. 5, pp. 52-61, 2026. Crossref, https://doi.org/10.14445/22315381/IJETT-V74I5P104

Abstract

Recently, optimization of the resource allocation in Cloud Computing (CC) has become more important because of an increase in users, who send or access data from the cloud. Resource allocation is performed based on Quality of Service (QoS) parameters of cloud services that optimize the solutions for allocating resources according to the scheduled tasks to reduce overall costs for end-users/services. However, the existing resource allocation failed to allocate the resource efficiently, making it difficult to ensure secure resource allocation during dynamic demand fluctuations. To overcome this limitation, an Adaptive Sample Learning strategy-based Horse Herd Optimization algorithm (ASL-HHO) model is employed for secure resource allocation within cloud environments. The proposed HHO algorithm allocates additional resources with security measures when a threat is detected and ensures secure transactions in small finance organizations. This defense structure for resource allocation ensures data integrity and privacy, which are crucial for financial transactions. To ensure secure and efficient resource allocation in a cloud environment, the ASL-HHO algorithm-based resource allocation model incorporates the QoS parameters, which include trust, resource utilization, energy consumption, and makespan. Experimental results of the ASL-HHO algorithm attained efficient resource utilization for tasks 100, 200, and 500 are 2896, 3589, and 42741, which is higher when compared to existing resource allocation models such as Adaptive Multi- Objective-Teaching Learning Optimization (AMO-TLO).

Keywords

Adaptive Sample Learning strategy, Cloud Computing, Horse Herd Optimization Algorithm, Quality of Service, Resource allocation.

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