Background:
In recent years, the computational memory and energy conservation have
become a major problem in cloud computing environment due to the increase in data size and
computing resources. Since, most of the different cloud providers offer different cloud services and
resources use limited number of user’s applications.
Objective:
The main objective of this work is to design and implement a cloud resource allocation
and resources scheduling model in the cloud environment.
Methods:
In the proposed model, a novel cloud server to resource management technique is proposed
on real-time cloud environment to minimize the cost and time. In this model different types
of cloud resources and its services are scheduled using multi-level objective constraint programming.
Proposed cloud server-based resource allocation model is based on optimization functions to
minimize the resource allocation time and cost.
Results:
Experimental results proved that the proposed model has high computational resource allocation
time and cost compared to the existing resource allocation models.
Conclusion:
This cloud service and resource optimization model is efficiently implemented and
tested in real-time cloud instances with different types of services and resource sets.
Multi-Level Queue based Resource Allocation in Downlink of OFDMA Wireless Cellular Networks
