Energy and QoS-aware virtual machine placement approach for IaaS cloud datacenter

Neural Computing and Applications, Год журнала: 2025, Номер unknown

Опубликована: Янв. 7, 2025

Abstract Virtualization technology enables cloud providers to abstract, hide, and manage the underlying physical resources of data centers in a flexible scalable manner. It allows placing multiple independent virtual machines (VMs) on single server order improve resource utilization energy efficiency. However, determining optimal VM placement is crucial as it directly impacts load balancing, consumption, performance degradation within center. Furthermore, deciding based factor usually insufficient center because many factors must be considered, ignoring them may too expensive. This paper improves new multi-objective (MVMP) algorithm using quantum particle swarm optimization (QPSO) technique. We call QPSO-MOVMP, its objective find Pareto solution for problem by balancing different goals. generates solutions that save power minimizing number running machines, avoid maintaining service level agreement (SLA), keeping loads at utilization. The experimental results show QPSO-MOVMP had superior terms consumption compared three other algorithms conventional single-objective algorithms. Simulation proposed achieves 2.4 × 10 4 watts power. outperformed others, achieving minimum 12% SLA breaches while experiencing significant surge requests from VMs. Moreover, model generated better distribution than those derived comparative method.

Язык: Английский

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Quality-aware web service composition using a hybrid summarization

The Journal of Supercomputing, Год журнала: 2025, Номер 81(5)

Опубликована: Март 18, 2025

Язык: Английский

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Reserve policy-aware VM positioning based on prediction in multi-cloud environment

The Journal of Supercomputing, Год журнала: 2024, Номер 80(16), С. 23736 - 23766

Опубликована: Июль 22, 2024

Язык: Английский

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