The term "cloud computing" refers to an infrastructure that allows for the provision of shared, internet-based services
on an as-needed or subscription basis. Cloud services are available for rent from a number of cloud service providers
(CSP). Demand for cloud services is on the rise, therefore it's important to make sure the current infrastructure can
handle it. But with all those data centers and servers, scaling comes with a hefty price tag in terms of energy usage. The
operational expenses are impacted by the unnecessary power usage, which ultimately impacts its consumers. The
environment is also impacted by CO2 emissions. Inadequate distribution of resources, such as data centers, virtual
machines, and servers, also raises operating expenses. In the long run, this can cause customers to lose interest in the
cloud service. Overall, it's necessary to make the most efficient use of the resources. This study suggests using an
enhanced Antlion (ALO) algorithm to compute various multi-objective functions in order to determine the best solution
for resource usage and allocation. In order to optimize resource usage, the suggested technique is tested in cloud
simulation settings, where it calculates energy consumption for varying workload quantities and improves the
performance of various multi-objective functions. Experimental findings demonstrate that the proposed framework
outperforms both current and previous frameworks.