Hybrid biogeography algorithm for reducing power consumption in cloud computing

Singhal, Sunita; Grover, Jyoti

doi:10.1109/icacci.2017.8125827

Cited by 2 publications

(1 citation statement)

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“…In the static category, all task characteristics, including the costs of computation and communication for each activity as well as how those activities relate to one another, are known in advance. However, the dynamic category makes such information unavailable and makes judgments at runtime [4]. Furthermore, static scheduling refers to compile-time scheduling, and dynamic scheduling refers to scheduling at runtime.…”

Section: Introductionmentioning

confidence: 99%

Cost effective hybrid genetic algorithm for workflow scheduling in cloud

Bothra

Singhal²,

Goyal³

2022

SRIT

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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 Load-balanced Genetic Algorithm (CLGA), respectively.

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Section: Introductionmentioning

confidence: 99%