Harmonic Migration Algorithm for Virtual Machine Migration and Switching Strategy in Cloud Computing DOI
Vahini Siruvoru, S Aparna

Concurrency and Computation Practice and Experience, Год журнала: 2024, Номер unknown

Опубликована: Окт. 29, 2024

ABSTRACT Nowadays, cloud computing (CC) has been utilized broadly owing to the services it provides which can be received from any location at time on basis of customer's requirements. A huge amount data transmission is made both user host as well customer in environment, but here placing virtual machine (VM) a suitable and transferring challenging task. In this research, harmonic migration algorithm (HMA) developed by combining (MA) analysis (HA) for migrating VM an overloaded under‐loaded physical (PM) enabling or disabling through switching strategies CC. The tasks are allocated corresponding round‐robin (RR) manner subsequently, load predicted gated recurrent unit (GRU). HMA technique migrates when higher than value threshold also, enables disables necessary. Thus, performance improved over other previous schemes 100, 200, 300, 400 varying iterations. Therefore, load, makespan, resource utilization 0.148, 0.327 s, 0.482% task 100 iteration 100.

Язык: Английский

Applying Optimized Machine Learning Models for Predicting Unconfined Compressive Strength in Fine-Grained Soil DOI
Ishwor Thapa, Sufyan Ghani

Transportation Infrastructure Geotechnology, Год журнала: 2024, Номер 11(4), С. 2235 - 2269

Опубликована: Фев. 8, 2024

Язык: Английский

Процитировано

11

Advancing earth science in geotechnical engineering: A data-driven soft computing technique for unconfined compressive strength prediction in soft soil DOI
Ishwor Thapa, Sufyan Ghani

Journal of Earth System Science, Год журнала: 2024, Номер 133(3)

Опубликована: Авг. 17, 2024

Язык: Английский

Процитировано

6

Heuristic Models for Optimal Host Selection DOI
Sakshi Patni, Deepika Saxena, Ashutosh K. Singh

и другие.

Опубликована: Янв. 1, 2025

Язык: Английский

Процитировано

0

Efficient resource allocation in cloud environment using SHO-ANN-based hybrid approach DOI Creative Commons
Sanjeev Sharma,

Pradeep Singh Rawat

Sustainable Operations and Computers, Год журнала: 2024, Номер 5, С. 141 - 155

Опубликована: Янв. 1, 2024

The cloud computing paradigm provides services to users in an on-demand fashion using high-speed Internet. This Internet-based resources on a rent basis without any fault. Virtual machine resource allocation is one of the challenging concerns environment. existing static, dynamic, and Meta-Heuristic approaches provide solution virtual problem. These techniques stuck with local optimal solution. slow convergence rate leads locally fails Globally. manuscript proposes hybrid Spotted Hyena optimizer artificial neural network, named SHO-ANN technique, assignment presented technique evaluated analyzed performance metrics "Energy Consumption (Kwh) (8.54%), Host Utilization (24.8%), Average Execution Time(ms) (26.33%), SLA Violations (1.33%), Number Migrations (Counts) (19.73%)". spotted hyena used vast data set ANN model for better accuracy. approach globally high convergence. experimental results exhibit that outperforms IqMc, SHO, Genetic real workload scenarios fabricated scenarios.

Язык: Английский

Процитировано

1

Harmonic Migration Algorithm for Virtual Machine Migration and Switching Strategy in Cloud Computing DOI
Vahini Siruvoru, S Aparna

Concurrency and Computation Practice and Experience, Год журнала: 2024, Номер unknown

Опубликована: Окт. 29, 2024

ABSTRACT Nowadays, cloud computing (CC) has been utilized broadly owing to the services it provides which can be received from any location at time on basis of customer's requirements. A huge amount data transmission is made both user host as well customer in environment, but here placing virtual machine (VM) a suitable and transferring challenging task. In this research, harmonic migration algorithm (HMA) developed by combining (MA) analysis (HA) for migrating VM an overloaded under‐loaded physical (PM) enabling or disabling through switching strategies CC. The tasks are allocated corresponding round‐robin (RR) manner subsequently, load predicted gated recurrent unit (GRU). HMA technique migrates when higher than value threshold also, enables disables necessary. Thus, performance improved over other previous schemes 100, 200, 300, 400 varying iterations. Therefore, load, makespan, resource utilization 0.148, 0.327 s, 0.482% task 100 iteration 100.

Язык: Английский

Процитировано

0