4.5 Article

An efficient proactive VM consolidation technique with improved LSTM network in a cloud environment

Journal

COMPUTING
Volume -, Issue -, Pages -

Publisher

SPRINGER WIEN
DOI: 10.1007/s00607-023-01214-5

Keywords

Cloud computing; VM consolidation; Resource management; Proactive; Prediction methods; SLA violations

Ask authors/readers for more resources

The power consumption of datacenters is increasing rapidly, and it is estimated to reach approximately 8000 TWh by 2030 if cloud resources are not utilized effectively. VM consolidation technique is a prominent solution to effectively manage cloud resources, improve server performance, and reduce power consumption. However, unnecessary actions of VM consolidation can lead to degraded resource management, poor QoS, and SLA violations. This paper proposes a proactive VM consolidation technique using an improved LSTM network to effectively manage resources, reduce power consumption, and avoid SLA violations.
The power consumption of datacenters is multiplying, and several survey reports stated that the power consumption of datacenters will reach approximately 8000 TWh by 2030 if do not utilize cloud resources effectively. To use allocated cloud resources effectively, one of the prominent solutions is the VM consolidation technique. VM consolidation technique manages cloud resources effectively while simultaneously satisfying the objectives of cloud users and providers. Additionally, it helps to increase servers' performance while reducing the high power consumption of datacenters. However, unnecessary actions of VM consolidation technique cause unsuitable VM selection and inappropriate VM placement, which degrades resource management performance, poor QoS, and SLA violations. To overcome this issue, this paper proposed a resource, SLA, power-aware proactive VM consolidation technique by using an improved LSTM network to manage the allocated resources effectively. The proposed proactive VM consolidation technique helps reduce the high power consumption of datacenters while maximizing resource management performance and avoiding SLA violations. Finally, the authors measure the proposed methodology effectiveness by considering the benchmark dataset of NASA servers, and experimental results proved that an improved LSTM network can able to achieve an average accuracy rate of up to 94% with minimum prediction error rate. Proactive VM consolidation technique minimized nearly 30% of the power consumption of datacenters compared with conventional VM consolidation technique.

Authors

I am an author on this paper
Click your name to claim this paper and add it to your profile.

Reviews

Primary Rating

4.5
Not enough ratings

Secondary Ratings

Novelty
-
Significance
-
Scientific rigor
-
Rate this paper

Recommended

No Data Available
No Data Available