Cost effective hybrid genetic algorithm for workflow scheduling in cloud
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Дата
2022
Науковий керівник
Назва журналу
Номер ISSN
Назва тому
Видавець
КПІ ім. Ігоря Сікорського
Анотація
Cloud computing plays a significant role in everyone’s lifestyle by snugly
linking communities, information, and trades across the globe. Due to its NP-hard
nature, recognizing the optimal solution for workflow scheduling in the cloud is a
challenging area. We proposed a hybrid meta-heuristic cost-effective load-balanced
approach to schedule workflow in a heterogeneous environment. Our model is based
on a genetic algorithm integrated with predict earliest finish time (PEFT) to minimize
makespan. Instead of assigning the task randomly to a virtual machine, we apply
a greedy strategy that assigns the task to the lowest-loaded virtual machine. After
completing the mutation operation, we verify the dependency constraint instead
of each crossover operation, which yields a better outcome. The proposed model incorporates
the virtual machine’s performance variance as well as acquisition delay,
which concedes the minimum makespan and computing cost. One of the most astounding
aspects of our cost-effective hybrid genetic algorithm (CHGA) is its capacity
to anticipate by creating an optimistic cost table (OCT) while maintaining quadratic
time complexity. Based on the results of our meticulous experiments on some
real-world workflow benchmarks and comprehensive analysis of some recently successful
scheduling algorithms, we concluded that the performance of our CHGA is
melodious. CHGA is 14.58188%, 11.40224%, 11.75306%, and 9.78841% cheaper
than standard Ant Colony Optimization (ACO), Particle Swarm Optimization
(PSO), Cost Effective Genetic Algorithm(CEGA), and Cost-Effective Loadbalanced
Genetic Algorithm (CLGA), respectively.
Опис
Ключові слова
cloud computing, cost effective, genetic algorithm, metaheuristic algorithm, predict earliest finish time, Workflow scheduling, хмарні обчислення, економічно вигідні, генетичний алгоритм, метагевристичний алгоритм, прогнозування раннього часу оброблення, планування робочого процесу
Бібліографічний опис
Kumar Bothra, S. Cost effective hybrid genetic algorithm for workflow scheduling in cloud / Sandeep Kumar Bothra, Sunita Singhal, Hemlata Goyal // Системні дослідження та інформаційні технології : міжнародний науково-технічний журнал. – 2022. – № 3. – С. 121-138. – Бібліогр.: 34 назв.