Utilizing multi-population ant colony system and exponential grey prediction model for multi-objective virtual machine consolidation in Cloud Data Centers DOI

Nenyasha Madyavanhu,

Vaneet Kumar

International Journal of Information Technology, Journal Year: 2024, Volume and Issue: unknown

Published: June 14, 2024

Language: Английский

Using light weight container a mesh based dynamic allocation task scheduling algorithm for cloud with IoT network DOI
Santosh Shakya, Priyanka Tripathi

International Journal of Information Technology, Journal Year: 2024, Volume and Issue: 16(5), P. 2847 - 2861

Published: March 5, 2024

Language: Английский

Citations

6

EBWO‐GE: An innovative approach to dynamic VM consolidation for cloud data centers DOI
Sahul Goyal, Lalit Kumar Awasthi

Concurrency and Computation Practice and Experience, Journal Year: 2024, Volume and Issue: unknown

Published: Sept. 30, 2024

Summary Cloud data centers (CDCs) have revolutionized global computing by offering extensive storage and processing capabilities. Nevertheless, the environmental impact of these processes, including their substantial energy consumption carbon emissions, calls for implementing more efficient techniques. Efficient virtual machine (VM) consolidation is crucial in optimizing resource utilization reducing consumption. Current methods enhancing efficiency often lead to issues such as service level agreements (SLAs) violations quality services (QoS) degradation. This study presents a novel approach host selection using grey‐extreme (GE) learning model, which accurately predicts over underutilized hosts. In addition, VM placement technique called enhanced black widow optimization (EBWO) utilizes heuristic techniques differential evolutionary optimize placement. The proposed dynamic optimizes while meeting strict SLA requirements QoS metrics CDCs. Extensive analyses were conducted Cloudsim toolkit validate approach's effectiveness. These encompassed conditions random workloads heterogeneous environments. simulation results showed that GE‐EBWO outperforms other improves 12%–15%. it significantly decreases migrations 11%–14% compared advanced methods. validates practicality moving towards environmentally friendly

Language: Английский

Citations

0

Multi-resource predictive workload consolidation approach in virtualized environments DOI
Mirna Awad,

Aris Leivadeas,

Abir Awad

et al.

Computer Networks, Journal Year: 2023, Volume and Issue: 237, P. 110088 - 110088

Published: Nov. 5, 2023

Language: Английский

Citations

1

Utilizing multi-population ant colony system and exponential grey prediction model for multi-objective virtual machine consolidation in Cloud Data Centers DOI

Nenyasha Madyavanhu,

Vaneet Kumar

International Journal of Information Technology, Journal Year: 2024, Volume and Issue: unknown

Published: June 14, 2024

Language: Английский

Citations

0