Hybrid VM allocation and placement policy for VM consolidation process in cloud data centres

Thakor, Devendra; Dabhi, Dipak

doi:10.1504/ijguc.2022.10049114

Cited by 1 publication

(2 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The SRVM_LB is compared with MFPED (Medium-Fit Power Efficient Decreasing) [29] and HVMAP (Hybrid VM Allocation and Placement) [30] regarding EC, SLA violation and ESV.…”

Section: Resultsmentioning

confidence: 99%

“…In comparison to many other policies, like, worst fit, best fit, and first fit, the new policy yields the best outcomes. Nevertheless, a policy is required to keep the ratio of power consumption to Modified Genetic algorithm EC Provide infeasible solutions [23] ML-based approach VM migration, SLA violation, and EC Does not consider the resource constraints [24] User needs-based approach EC, CPU utilization, and SLA violations High computational complexity [29] Energy-aware VM placement Energy, overload time fraction, VM migration, SL A violation Neglects traffic effects and network devices in its assessment [30] Hybrid VM allocation and placement EC, SLA Violation, VM migration Emphasize VM placement and host underload detection for performance enhancement…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Cloud Computing by Implementing State and Random-Based Virtual Machine Load Balancing Model

2024

IJIES

View full text Add to dashboard Cite

The resilient concept of cloud computing (CC) enables individuals and businesses to access the necessary services following their requirements. The methodology presents various functionalities like storage solutions, deployment platforms, convenient admittance to online services, and more. A significant issue in CC is load balancing (LB), which reduces the performance and effectiveness of the resources. The LB can be attained through (Task / Virtual Machine) scheduling and allocation. This research considers virtual machine (VM) allocation, which allocates VM to a suitable physical machine (PM). Implementing an efficient VM allocation approach is essential for mitigating energy consumption (EC) and service level agreement (SLA) breaches. This study presents an effectual LB model for CC based on state and randomization. Based on host utilization, the proposed approach first identifies the PM's current state (high, medium, and low). Next, select the suitable PM for VM allocation using a randomization approach. The Cloudsim toolkit is employed for simulating the suggested technique, and the PlanetLab workload is used to evaluate the performance regarding EC and SLA violations. The proposed approach is compared with MFPED (Medium-Fit Power Efficient Decreasing) and HVMAP (Hybrid VM Allocation and Placement). The experimental outcome shows that the proposed technique significantly lowers EC by 40.81% and 39.76% and SLA violation by 96.81% and 95.58% for MFPED and HVMAP methods.

show abstract

“…The SRVM_LB is compared with MFPED (Medium-Fit Power Efficient Decreasing) [29] and HVMAP (Hybrid VM Allocation and Placement) [30] regarding EC, SLA violation and ESV.…”

Section: Resultsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Cloud Computing by Implementing State and Random-Based Virtual Machine Load Balancing Model

2024

IJIES

View full text Add to dashboard Cite

show abstract

Hybrid VM allocation and placement policy for VM consolidation process in cloud data centres

Cited by 1 publication

References 0 publications

Cloud Computing by Implementing State and Random-Based Virtual Machine Load Balancing Model

Cloud Computing by Implementing State and Random-Based Virtual Machine Load Balancing Model

Contact Info

Product

Resources

About