A Hybrid Optimized Resource Allocation Model for Multi-Cloud Environment Using Bat and Particle Swarm Optimization Algorithms

  • D. Selvapandian Karpagam Academy of Higher Education
  • R. Santosh Karpagam Academy of Higher Education


In cloud computing, scheduling and resource allocation are the major factors that define the overall quality of services. An efficient resource allocation module is required in cloud computing since resource allocation in a single cloud environment is a complex process. Whereas resource allocation in a multi-cloud environment further increases the complexity of allocation procedures. Earlier, resources from the multi-cloud environment were allocated based on task requirements. However, it is essential to analyze the present resource availability status and resource capability before allocating to the requested tasks. So, in this research work, a hybrid optimized resource allocation model is presented using bat optimization algorithm and particle swarm optimization algorithm to allocate the resource considering the resource status, distance, bandwidth, and task requirements. Proposed model performance is evaluated through simulation and compared with conventional optimization algorithms. For a set of 500 tasks, the proposed approach allocates resources in 47 s, with a minimum energy consumption of 200 kWh. Compared to conventional approaches, the performance of the proposed model is much better in terms of deadline missed tasks, resource requirement, energy consumption, and allocation time.


Multi-cloud computing, Resource allocation, Hybrid optimization, BAT Algorithm, Particle Swarm Optimization, Quality of services (QoS),


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Mar 17, 2022
How to Cite
SELVAPANDIAN, D.; SANTOSH, R.. A Hybrid Optimized Resource Allocation Model for Multi-Cloud Environment Using Bat and Particle Swarm Optimization Algorithms. Computer Assisted Methods in Engineering and Science, [S.l.], v. 29, n. 1–2, p. 87–103, mar. 2022. ISSN 2956-5839. Available at: <https://cames.ippt.pan.pl/index.php/cames/article/view/405>. Date accessed: 30 sep. 2023. doi: http://dx.doi.org/10.24423/cames.405.