: Edge computing, a geographically distributed set of computing platforms, is crucial in modern IoT applications for faster processing and application execution. Effective task scheduling in edge-cloud computing enhances resource utilization, lowers makespan, reduces energy consumption, and achieves cost-effectiveness, meeting new time measure requirements for the modern world. This paper presents a new Hybrid Hitchcock Bird Algorithm (HHBA) and Genetic Algorithm (GA)-based dynamic task scheduling method that combines the advantages of HHBA and GA algorithms. While GA operates on the solution space to evolve a more effective solution through selection and mutation operations, refining the task allocation by promoting the use of two-point crossover and uniform crossover for the topic planning in trades, HHBA can be considered as the optimizer, honing in on the set of sub-tasks through local comparisons while ensuring an equal workload among the resources. Experiments indicate that HHBA-GA achieves the best performance in makespan, energy consumption, and cost efficiency compared with GA, PSO, and standalone HHBA. The proposed pathway leads to an increase in energy savings of up to 24% and a reduction in cost reduction of about 22% when compared to traditional approaches. Furthermore, the model significantly enhances resource usage, proving its effectiveness in large-scale edge-cloud applications.
P. Thai, S. Chandre· Proceedings of the 1st Inter...· 0 citations
Cloud Computing (CC) is the cornerstone of modern information technology that provides scalable, flexible, and cost-efficient services across diverse applications. Dynamic workloads and heterogeneous infrastructure face some difficulties in effective load balancing and resource provisioning which results in resource underutilization, overload and response time increases. This paper presents a comprehensive and comparative analysis of current methods addressing these issues. It also presents active resource provisioning frameworks, namely: probabilistic load balancing models, Machine Learning (ML)-based, Deep Learning (DL)-based, workload prediction techniques, genetic algorithms, Reinforcement Learning (RL) strategies, and hybrid meta-heuristic methods. Each method is analyzed in terms of methodology, advantages, limitations, and performance metrics, therefore providing an insight of their applicability in dynamic and large-scale cloud environments. A taxonomy architecture is presented to categorize the systematic comparison and research gaps. The comparative evaluation segment demonstrates enhancement in throughput, resource utilization, and cost efficiency, while also identifying limitations such as computational overhead and scalability constraints. The survey concludes by highlighting the necessity for intelligent, adaptive, and energy-aware solutions to confirm resilient and efficient cloud infrastructures.
Prasanna Mandala, S. Chandre· 2026 5th International Confe...· 0 citations
: Cloud Computing (CC) is one of the widely used technologies due to its advanced features such as pay-per-use, scalability, and flexibility. The primary objective of CC is to allow users to access and purchase cloud services that are on demand through internet-based applications. Efficient load-balancing in the cloud faces challenges of high-dimensional state spaces and scalability with increasing tasks. To solve this problem, the Masterpiece Optimization Algorithm (MOA) with a priority constraint is employed for load-balancing according to the tasks efficiently. The MOA is integrated with a priority-based cost function to enhance the task scheduling process by introducing a multi-dimensional approach for load balancing. The priority-based framework helps the scheduler to dynamically recalibrate workloads. The experimental results achieve a total energy consumption of 39.8 W and an average CPU resource utilization of 99.54%, which is better than the existing algorithms, such as the hybrid Particle Swarm Grey Wolf Optimization (PSGWO) algorithm.
S. Vijaykumar, S. Chandre· Journal of Computer Science· 0 citations