TL;DR
The findings indicate that integrating a provider-independent resource selection strategy with a structured cloud-native deployment approach can enhance the efficiency of scientific workload execution in HPC cloud environments.
Results show that selecting instances based on the second PI achieves at least 97% of the best achievable execution time in most cases, while highlighting cases where additional PIs improve selection accuracy.
The findings show that adaptive algorithm and hybrid algorithm is better in scalability, robustness and the overall performance of the system compared to the traditional centralized algorithms.
An Energy-Driven Adaptive Scheduling (EDAS) algorithm is proposed that dynamically prioritizes queries based on estimated CPU utilization, disk I/O costs, and historical energy profiles without requiring modifications to the underlying database engine.
The results reveal that x86 instances outperform ARM-based instances in raw HPL throughput, that latest CPU generations offer substantially better price-performance, and that spot pricing offers additional opportunities for reducing execution costs.
A resource-aware optimization framework that dynamically selects the MPI process count and performs node- and NUMA-aware process placement and reduces task-sequence execution time and improves the evaluated resource-utilization metrics by more than 30%.
A hybrid nature-inspired algorithm called fruit fly optimization–ant colony optimization (FOA-ACO), which combines the exploitative ant colony optimization (ACO) and the exploratory fruit fly optimization algorithm (FOA) is suggested, which enhances overall cloud performance.
We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.
