A hybrid optimized resource allocation modelfor multi-cloud environment using batand particle swarm optimization algorithms

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

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Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).