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, Год журнала: 2024, Номер unknown

Опубликована: Июнь 14, 2024

Язык: 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, Год журнала: 2024, Номер 16(5), С. 2847 - 2861

Опубликована: Март 5, 2024

Язык: Английский

Процитировано

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, Год журнала: 2024, Номер unknown

Опубликована: Сен. 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

Язык: Английский

Процитировано

0

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

Aris Leivadeas,

Abir Awad

и другие.

Computer Networks, Год журнала: 2023, Номер 237, С. 110088 - 110088

Опубликована: Ноя. 5, 2023

Язык: Английский

Процитировано

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, Год журнала: 2024, Номер unknown

Опубликована: Июнь 14, 2024

Язык: Английский

Процитировано

0