Reproduce “Hybrid Genetic Algorithm for Cloud Computing Applications” on OpenResearch
Turn your favorite coding agents into research agents that review literature, develop hypotheses, and run experiments.
Universal app for Apple silicon and Intel. Requires macOS 11 or later.
About this paper
Saeed Javanmardi, Mohammad Shojafar, Danilo Amendola, Nicola Cordeschi, Hongbo Liu, Ajith Abraham
Researchers introduce a hybrid genetic-fuzzy algorithm for cloud job scheduling, integrating fuzzy logic into genetic algorithm operations to optimize resource assignments. Simulations on CloudSim show the approach reduces job makespan and improves load balancing compared to Ant Colony Optimization algorithms, reporting up to 50% reduction in total execution time and 45% in execution cost.
Key insights
- Integrating fuzzy logic into GA's fitness function and crossover operations can significantly improve resource matching precision and reduce the number of iterations required for convergence.
- By considering multiple job and VM characteristics through fuzzy rules, the algorithm intelligently assigns jobs to suitable resources, optimizing utilization.
- The hybrid approach effectively balances multiple objectives (makespan, cost, load balance) by guiding the GA search towards more promising solutions than traditional meta-heuristics.
Summary by alphaXiv.
Tip: change arxiv.org to autoarxiv.org in any arXiv abstract link to open the paper here.