A Comparative Analysis of Resource Management Policies: A Report

This report provides an analysis of various resource management policies, evaluating their advantages and disadvantages. It examines control theory, machine learning, utility-based, and market-oriented approaches. The report discusses the benefits of control theory in analyzing system functions and optimizing design, while also acknowledging its complexity. It highlights the advantages of machine learning in trend identification and automation, as well as its susceptibility to data acquisition issues. The utility-based approach is praised for increasing profit and optimizing resources, but it is criticized for load imbalance. Finally, the market-oriented theory is noted for its focus on client needs, but also for potential compromises in product quality. References to relevant literature are also included.

Running Head: RESOURCE MANAGEMENT POLICIES
RESOURCE MANAGEMENT POLICIES
Name of the Student
Name of the University
Author Note

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Control Theory
Advantage
The main advantages of using control theory is that this process analyzes the
functioning process of the time variance or the time invariance output system. Analyzing of
the linear, nonlinear, single or multiple input output systems are also performed with the help
of this theory. Confirming the state of system parameters are also performed via the control
theory implementation (Smith-Lovin & Heise 2016). Optimizing the system and making the
system useful for the optimal design is a main advantage of this theory. Inclusion of the data
that were collected while the project was in the initial condition will help in better execution
of the project.
Disadvantage
The main disadvantage of this this theory is that the techniques that is used is very
complex for completion of the project. several stages of computation processes are required.
Machine learning
Advantage
The main advantage of implementation of machine learning is that trend identification
process gets better and hence understanding of the trend and implementing the same in the
process of the same in the execution of the same will be increasing the efficiency of the
business. Machine learning imbibes high end automation in the projects in which they are
implemented. With the help of this aspect each and every steps of the project will be well
defined and hence the execution of the project will get easier (Li et al, 2016). A continuous
improvement in the execution steps are noticed. This helps the managerial body to forecast
the business model that is to be implemented. Proper handling of multi dimensional data has

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also been a reason of machine learning having such high advantages in the business execution
process. the range of application that can implement the technology of machine learning is
vast and hence acceptance of the technology is very high.
Disadvantage
Data acquisition and high error susceptibility are the major disadvantage of the
process.
Utility based
Advantage
The major advantage that is gained due to the implementation of the Utility based
theory includes increase of profit that is being made. Utilization and guarantee providing for
the SLA for performing the multi-tier applications (Witten et al, 2016). The optimization of
the resources is also very vast and hence the convergence process is of high utility. Improving
of global system have been a major advantage of the theory.
Disadvantage
Load imbalance and less concentration on reliability are the main constraints or
disadvantages.
Market oriented
Advantage
The main advantage that is received due to the implementation of the market-oriented
theory is that the strategies that are implemented are due to the understanding of the needs
and demands of the people and the clients (Lam and Gu 2016). The strategies are set as per
the requirement of the external stakeholders and the major revenue earners.

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Disadvantage
The main disadvantage is that due to the excess concentration that is provided in the market
orientation, compromise in the quality of the product is expected. With decrease in quality of
the product, retention of clients gets more difficult.

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References
Lam, J.S.L. and Gu, Y., 2016. A market-oriented approach for intermodal network
optimisation meeting cost, time and environmental requirements. International
Journal of Production Economics, 171, pp.266-274.
Li, H., Xing, X., Zhu, J., Cheng, X., Li, K., Bie, R., & Jing, T. (2016). Utility-based
cooperative spectrum sensing scheduling in cognitive radio networks. IEEE
Transactions on Vehicular Technology, 66(1), 645-655.
Smith-Lovin, L., & Heise, D. R. (2016). Analyzing social interaction: Advances in affect
control theory. Routledge.
Witten, I. H., Frank, E., Hall, M. A., & Pal, C. J. (2016). Data Mining: Practical machine
learning tools and techniques. Morgan Kaufmann.