Cloud computing has become one of the most influential technologies in the computer science field. In the modern-day world, people are dependent on cloud services in every aspect of life. Cloud services process a vast number of user requests through various data centers. So often, data center selection plays a vital role in user satisfaction and the success of cloud services. Researchers are working relentlessly to use the behaviors of nature to solve real-world problems. In this paper, we used swarm intelligence to find optimal data centers for userbases. The swarm intelligence algorithms use their experience and knowledge of their neighbors to direct the algorithm toward an optimal solution. We have designed a particle swarm optimization-based data center selection policy inspired by swarm intelligence. To explore the accuracy and performance of the algorithm, we simulated it with different real-world scenarios using CloudAnalyst. The simulation results of response time and data processing time of the cloud environment exhibit that the proposed data center selection policy outperforms the traditional data center policy, such as optimized response time and closest data center policy.
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Optimal Datacenter Selection for Cloud Services Using Swarm Intelligence
Semantic Scholar · Computer Science · 2023
Abstract
Cloud computing has become one of the most influential technologies in the computer science field. In the modern-day world, people are dependent on cloud services in every aspect of life. Cloud services process a vast number of user requests through various data centers. So often, data center selection plays a vital role in user satisfaction and the success of cloud services. Researchers are working relentlessly to use the behaviors of nature to solve real-world problems. In this paper, we used swarm intelligence to find optimal data centers for userbases. The swarm intelligence algorithms use their experience and knowledge of their neighbors to direct the algorithm toward an optimal solution. We have designed a particle swarm optimization-based data center selection policy inspired by swarm intelligence. To explore the accuracy and performance of the algorithm, we simulated it with different real-world scenarios using CloudAnalyst. The simulation results of response time and data processing time of the cloud environment exhibit that the proposed data center selection policy outperforms the traditional data center policy, such as optimized response time and closest data center policy.