  Cloud computing is a new archetype that provides dynamic computing services
to cloud users through the support of datacenters that employs the services of
datacenter brokers which discover resources and assign them Virtually. The
focus of this research is to efficiently optimize resource allocation in the
cloud by exploiting the Max-Min scheduling algorithm and enhancing it to
increase efficiency in terms of completion time (makespan). This is key to
enhancing the performance of cloud scheduling and narrowing the performance gap
between cloud service providers and cloud resources consumers/users. The
current Max-Min algorithm selects tasks with maximum execution time on a faster
available machine or resource that is capable of giving minimum completion
time. The concern of this algorithm is to give priority to tasks with maximum
execution time first before assigning those with the minimum execution time for
the purpose of minimizing makespan. The drawback of this algorithm is that, the
execution of tasks with maximum execution time first may increase the makespan,
and leads to a delay in executing tasks with minimum execution time if the
number of tasks with maximum execution time exceeds that of tasks with minimum
execution time, hence the need to improve it to mitigate the delay in executing
tasks with minimum execution time. CloudSim is used to compare the
effectiveness of the improved Max-Min algorithm with the traditional one. The
experimented results show that the improved algorithm is efficient and can
produce better makespan than Max-Min and DataAware.
