TL;DR
Experimental evaluations demonstrate that GABS reduces processing time, increases system throughput, decreases execution cost, and enhances data locality by 24% compared to Hadoop’s default schedulers, highlighting the effectiveness of GABS in enhancing scalability, performance, and resource efficiency in heterogeneous computing environments.
Experiments show that the proposed Hybrid Framework for Joint Optimization of Resource Allocation and Load Balancing that spans two layers in heterogeneous cloud computing systems obtains 25-30% energy savings compared with ordinary methods, significantly reduces p95 latency and also achieves a relatively better Qualit...
A random forest enhanced particle swarm optimization algorithm (RFPSO) is proposed, which implements intelligent initialization of resource allocation through a random forest model, which improves the efficiency of finding optimal solutions and ensures that critical tasks can prioritize access to higher-performance com...
An intelligent workflow scheduling framework that improves performance through adaptive decision-making, predictive analytics, and machine learning, and addresses key challenges like load balancing, scalability, energy efficiency, and fault tolerance is proposed.
Resource allocation in the cloud-edges is extremely important to reduce the time of execution, energy usage, and service-level agreement (SLA) breaches when experiencing dynamic workloads. In this paper, the author has proposed an elite co-evolution hybrid optimization model, Hybrid Hierarchical Optimization for Cloud...
Cloud computing has transformed the delivery of modern applications and services by providing scalable, flexible, and cost-effective access to computing resources. One of the most critical challenges in cloud environments is the efficient distribution of dynamic workloads across heterogeneous resources, commonly addres...
The findings suggest that HORAM is far better at using resources; fewer tasks are completed, and the total power consumed is lower than with traditional scheduling algorithms, suggesting the suggested architecture is a viable solution to sustainable cloud infrastructure management.
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