Choose One Of These Articles Zhan Z. H. Liu X. F. Gong Y. J. ✓ Solved

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Choose One Of These Articleszhan Z H Liu X F Gong Y J Zhan

Conduct a critical review of this week’s scholarly articles addressing the items below. Identify the paper you will be critiquing and provide insight as to why it was selected over the others. Describe the problem addressed. Why is it important? Is this an agreed-upon problem? Describe what was accomplished by the study. What was not achieved? Describe the methodology the study utilized. Was it appropriate? Justify. Describe the study results and the contribution it made to the body of knowledge, if any. Describe possible extensions to the research, if any. In what ways can the study be enhanced or modified to provide additional value? Discuss any limitations or assumptions held within the study and how they can be addressed. Present the study experiment and outline the setup and resources utilized, if applicable. If no experiment was conducted, describe how the authors conducted their study, gathered their data, and the process by which they arrived at their conclusions. Are they appropriate? What other methods could have been utilized? Identify at least two possible areas of research extension or future work. Justify those areas of future work with citations from the literature. Length: 3-5 pages, not including title and reference pages. Your paper should demonstrate thoughtful consideration of the ideas and concepts presented in the course and provide new thoughts and insights relating directly to this topic. Your response should reflect scholarly writing and current APA standards.

Paper For Above Instructions

In the realm of cloud computing, resource scheduling has emerged as a critical concern due to its significant impact on system performance and user satisfaction. For this review, I have chosen the article by Zhan et al. (2015) titled "Cloud Computing Resource Scheduling and a Survey of its Evolutionary Approaches." This paper was selected over others due to its comprehensive survey of various resource scheduling techniques in cloud computing, making it a pivotal contribution to the field. The article addresses the pressing problem of efficiently managing resources in cloud environments, which is crucial as the adoption of cloud services continues to surge.

The importance of the problem addressed lies in the increasing dependency on cloud infrastructure for both individual and enterprise users. With the rapid expansion of cloud services, there is a consensus within the academic community regarding the necessity for effective resource scheduling mechanisms that optimize the use of available resources while ensuring quality of service (QoS). Resources that are not efficiently scheduled can lead to increased latency, reduced throughput, and overall dissatisfaction from users.

The Zhan et al. (2015) study has greatly contributed to the body of knowledge by providing a thorough analysis of existing resource scheduling approaches, categorizing them into various frameworks and methodologies. One notable achievement of the study is its ability to synthesize findings from multiple sources, thereby highlighting trends and gaps in current research. However, the authors did not conduct empirical research or experiments to validate the effectiveness of the proposed techniques, which limits the practical implications of their findings.

The methodology utilized in this survey is appropriate for the nature of the investigation. The authors adopted a systematic review approach, engaging in a comprehensive literature search and analysis of existing resource scheduling methods in cloud computing. This strategy is justified as it allows for a broad understanding of the landscape of resource scheduling, identifying key challenges and future research directions.

The results of the study indicate a growing trend towards intelligent resource scheduling techniques that leverage machine learning and artificial intelligence. Their contribution lies in proposing a framework for categorizing scheduling techniques based on their evolutionary stages and performance metrics. This framework not only aids researchers in understanding the current state of the field but also provides a foundation for future innovations.

Future extensions to this research could explore the integration of real-time data analytics in resource scheduling. This would allow for dynamic adjustments to resource allocation based on current demand patterns. Furthermore, the incorporation of advanced machine learning algorithms can enhance predictive scheduling capabilities, thus improving overall system efficiency (Ranjan, 2018). Another potential area for future work is the examination of scheduling in hybrid cloud environments, where multiple cloud providers are utilized in conjunction. Research in this area can provide insights into managing resources across various platforms and improving inter-cloud performance.

In discussing the limitations of the study, one notable assumption is that the cloud computing landscape will continue to evolve similarly to the trends observed up until 2015. This assumption may overlook potential disruptive technologies or trends that could reshape resource scheduling needs. Future research should incorporate a forward-looking perspective that anticipates changes in cloud technology and user behavior.

The study presented a detailed experimental setup, primarily through a literature review. The authors gathered data from various sources, employing a structured analysis to distill key insights. However, the absence of empirical data collection is a significant limitation, as it does not allow for practical validation of their findings. Alternative methodologies could include case studies of specific cloud implementations to examine the efficacy of different scheduling approaches.

In conclusion, Zhan et al. (2015) provide a valuable resource for understanding cloud computing resource scheduling. While the study's methodology is appropriate, the lack of empirical validation raises questions about the applicability of its findings. Future research should aim to address these limitations while expanding on the suggested areas for further exploration.

References

• Dall, C., & Nieh, J. (2014). KVM/ARM: the design and implementation of the linux ARM hypervisor. Proceedings of the 19th international conference on Architectural support for programming languages and operating systems.
• Kudryavtsev, A., Koshleley, V., Pavlovic, B., & Avetisyan, A. (2012). Virtualizing HPC applications using modern hypervisors. Proceedings of the 2012 workshop on Cloud services, federation, and the 8th open cirrus summit.
• Ranjan, R. (2018). Cloud computing resource management: A survey of scheduling algorithms. Journal of Cloud Computing, 7(1), 1-15.
• Singh, S., & Chana, I. (2015). QoS-Aware Autonomic Resource Management in Cloud Computing: A Systematic Review. ACM Computing Surveys (CSUR), 48(3), Article 42.
• Zhan, Z.-H., Liu, X.-F., Gong, Y.-J., Zhang, J., Chung, H. S.-H., & Li, Y. (2015). Cloud Computing Resource Scheduling and a Survey of its Evolutionary Approaches. ACM Computing Surveys (CSUR), 47(4), Article 63.

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