Articles | Open Access |

OPTIMIZING WORKLOAD ORCHESTRATION: PROACTIVE MANAGEMENT STRATEGIES FOR DYNAMIC VIRTUALIZED ENVIRONMENTS

Mohmed Salam , The Faculty of Computers and Informatics, Suez Canal University, Ismailia, Egypt

Abstract

This paper explores proactive management strategies for optimizing workload orchestration in dynamic virtualized environments. In today's rapidly evolving computing landscape, the ability to efficiently allocate and manage resources is crucial for ensuring system performance, cost-effectiveness, and user satisfaction. This study investigates proactive approaches to workload orchestration, emphasizing predictive resource allocation, workload balancing, and adaptive scaling. By harnessing these strategies, organizations can enhance their ability to respond to dynamic workloads and maintain optimal performance in virtualized environments. The findings presented in this paper provide valuable insights for IT professionals and researchers seeking to achieve proactive workload management in the context of dynamic virtualization.

Keywords

Workload Orchestration, Proactive Management, Dynamic Virtualized Environments

References

C. Clark, K. Fraser, S. Hand, J.G. Hansen, E. Jul, C. Limpach, I. Pratt, A. Warfield, Live migration of virtual machines, in: Proceedings of the 2nd Conference on Symposium on Networked Systems Design & Implementation, NSDI’05, USENIX Association, Berkeley, Boston, 2005, pp. 273–286.

Q. Zhang, L. Cheng, R. Boutaba, Cloud computing: state-of-the-art and research challenges, J. Internet Serv. Appl. 1 (1) (2010) 7–18.

X. Qin, H. Jiang, A. Manzanares, X. Ruan, S. Yin, Dynamic load balancing for I/O-intensive applications on clusters, ACM Transactions on Storage 5 (3) (2009) 1–38, article 9.

L. He, D. Zou, Z. Zhang, C. Chen, H. Jin, S.A. Jarvis, Developing resource consolidation frameworks for moldable virtual machines in clouds, Future Gener. Comput. Syst. 32 (2014) 69–81.

J. Levon, P. Elie, Oprofile: a system profiler for linux, http://oprofile.sf.net/, 2004.

Menon, J.R. Santos, Y. Turner, G. Janakiraman, W. Zwaenepoel, Diagnosing performance overheads in the Xen virtual machine environment, in: Proceedings of the 1st ACM/USENIX International Conference on Virtual Execution Environments, VEE ’05, ACM, New York, Boston, 2005, pp. 13–23.

D. Gupta, R. Gardner, L. Cherkasova, Xenmon: Qos monitoring and performance profiling tool, Hewlett–Packard Labs, 2005.

Openvz monitoring tools, http://wiki.openvz.org/Category:Monitoring, 2008.

S. Ibrahim, J. Hai, L. Lu, H. Bingsheng, W. Song, Adaptive disk I/O scheduling for mapreduce in virtualized environment, in: 2011 International Conference on Parallel Processing, ICPP, IEEE Computer Society, Washington, Taipei, 2011, pp. 335–344.

H. Kang, Y. Chen, J.L. Wong, R. Sion, J. Wu, Enhancement of Xen’s scheduler for mapreduce workloads, in: Proceedings of the 20th International Symposium on High Performance Distributed Computing, HPDC ’11, ACM, New York, San Jose, California, 2011, pp. 251–262.

Article Statistics

Downloads

Download data is not yet available.

Copyright License

Download Citations

How to Cite

Mohmed Salam. (2023). OPTIMIZING WORKLOAD ORCHESTRATION: PROACTIVE MANAGEMENT STRATEGIES FOR DYNAMIC VIRTUALIZED ENVIRONMENTS. International Journal of Computer Science & Information System, 8(10), 01–05. Retrieved from https://scientiamreearch.org/index.php/ijcsis/article/view/69