Efficient Resource Allocation in Cloud Data Centers using the Whale Optimization Algorithm with Adaptive Penalty Function.

The efficient allocation of resources is critical for improving the performance of data centers in cloud environments. It is considered an NP-hard problem. Traditional optimization methods for resource allocation are often inadequate due to their complexity, lack of performance guarantees, and lengthy training times. The Whale Optimization Algorithm (WOA) has recently emerged as a well-accepted alternative for solving constraint optimization problems. This article furthers into the potential of WOA to address resource allocation problems in cloud data centers. To overcome the limitations of WOA and constraints optimization, an adaptive penalty function-based method, namely APFWOA, is proposed to minimize the makespan and execution cost. The APFWOA is evaluated on real and synthetic datasets in a simulated cloud infrastructure environment and compared to other contemporary techniques. The findings of this study indicate that the proposed algorithm surpasses alternative methods in minimizing makespan and execution costs.