Fine Tuning of Rank Based VM Placement and Scheduling Strategies on Opennebula Based Cloud
Shreesudha Kembhavi1, Shalini Nigam2
1Shreesudha Kembhavi*, Information Technology, Genba SopanRao College of Engineering, Pune, India.
2Shalini Nigam, Information Technology, Genba SopanRao College of Engineering, Pune, India.
Manuscript received on January 02, 2020. | Revised Manuscript received on January 15, 2020. | Manuscript published on January 30, 2020. | PP: 1833-1835 | Volume-8 Issue-5, January 2020. | Retrieval Number: E4836018520/2020©BEIESP | DOI: 10.35940/ijrte.E4836.018520
Open Access | Ethics and Policies | Cite | Mendeley
© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: The HPC Clouds are good option over deploying actual physical infrastructure. This helps in running applications efficiently and in cost effective manner as applications leverage resources in pay as you go manner. But HPC clouds suffer performance issues due to Hypervisor layer. This work addresses the issue by coming up with a VM placement strategy considering the intensity of the applications and also resources available in the host machines and avoid performance degradation of parallel running applications. This placement strategy identifies the and ranks the available host machines and places the maximum possible VMs in highest ranking nodes. This avoids communication over the network since the VMs use shared memory for communication.
Keywords: Application Intensity, Cloud Computing, Heterogeneous resources, High Performance Computing, Hypervisor layer.
Scope of the Article: Cloud Computing.