Embedding individualized machine learning prediction models for energy efficient VM consolidation within Cloud data centers

Moghaddam, Seyedhamid Mashhadi; O'Sullivan, Michael J.; Walker, Cameron; Piraghaj, Sareh Fotuhi; Unsworth, Charles P.

doi:10.1016/j.future.2020.01.008

Cited by 45 publications

(10 citation statements)

References 24 publications

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“…It applies in different domains and achieves acceptable efficiency. Similarly, many researchers use machine learning algorithms like Moghaddam et al's [26] to embed machine learning techniques like ANN, decision forest, support vector machines, gradient boosting, and linear regression to predict the future load of VMs.…”

Section: Machine-learning Modelsmentioning

confidence: 99%

HBWO-CML: An energy-efficient Hybrid Black Widow Optimisation and a Combined Machine Learning approach for VM Placement

Goyal

Awasthi

2023

Preprint

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Cloud data centers remove the hardware barriers for the world with enormous storage and computing capabilities and provide access from anywhere over the internet with the concept of virtual machines (VM) and physical machines (PM). These cloud centers raise the problem of lack of resource utilisation and energy waste. These drawbacks have led many researchers to propose VM placement techniques that improve resource utilization and energy consumption. The research proposes VM migration/consolidation and host selection techniques to improve the resource utilisation of cloud centers. This research suggests hybrid black widow optimization (HBWO) and a combined machine learning model to mitigate the problem of cloud centers. HBWO optimizes the VM placement sub-process using an evolutionary approach and a combined machine-learning-based model to predict the over and under-utilised hosts. The effectiveness of the proposed approach was analysed and compared with a Power-Aware Best-Fit Decrease (PABFD) approach and a Particle Swarm Optimization (PSO) approach. The proposed approach improves energy utilisation by 30-40\% over PABFD and 20-15\% over PSO. Moreover, the combined approach of HBWO and machine learning improves the overall performance by 40-50\% compared to PABFD and 20-30\% compared to PSO.

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Section: Machine-learning Modelsmentioning

confidence: 99%

HBWO-CML: An energy-efficient Hybrid Black Widow Optimisation and a Combined Machine Learning approach for VM Placement

Goyal

Awasthi

2023

Preprint

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“…One of the latest studies in machine learning in the dynamic consolidation of VMs is conducted by Moghaddam et al 36 They applied cross‐validation to select an appropriate prediction model for each VM, which is a highly time‐consuming process for real‐world cloud data centers. Moreover, they had predicted CPU utilization only for VM prediction and ignored to predicting memory requirement of VMs.…”

Section: Literature Reviewmentioning

confidence: 99%

Confidence interval‐based overload avoidance algorithm for virtual machine placement

et al. 2022

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Virtualization plays an essential role in decreasing energy consumption and optimizing resource utilization by enabling the creation of virtual machines (VM) and their consolidation through live migration. Excessive migrations and a lack of required VMs are two critical factors in QoS degradation. The current consolidation approaches impose an intensive time complexity and cannot be used in large data centers with hundreds of hosts. This article proposes a framework for dynamic consolidation divided into a QoS‐aware algorithm for overload avoidance and a power‐aware algorithm for VM placement. To compute a safe zone criterion for any VM, relations were suggested by applying an interval estimate with a confidence level. By employing this criterion, the offered algorithm could guarantee the quality of service (QoS), particularly for specific VMs, while avoiding overhead. The VM placement algorithm is developed based on the maximum utilization of active hosts. It provides the capability to control the number of active hosts for the data center manager. The simulation results with real workloads revealed that the proposed framework could decline the amount of service level agreement violations by 78% and the number of migrations by 74%, and energy consumption by up to 13% in comparison with the best results of the benchmark algorithms. Hence, the application of this framework upgrades the QoS of data centers and declines their energy costs.

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“…Also, a Power Aware Best Fit Decreasing (PABFD) policy sorts VMs in a descending order based on their current CPU utilization and allocates the VMs to the PMs so that VM allocation incurs minimum power consumption. Moghadam et al [39] proposed a prediction model to detect over-and under-utilized hosts before performing VM migration. Also, a fine tuned machine learning was introduced to predict resource usage of migrating VM and select a proper destination host for the VM destination.…”

Section: Related Workmentioning

confidence: 99%

Design and evaluation of a joint profit and interference-aware VMs consolidation in IaaS cloud datacenter

RahimiZadeh

Dehghani

2021

Cluster Comput

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Cloud service providers employ virtualization technologies in order to deliver energy efficiency and optimize resource utilization in cloud datacenters. In this way, Virtual Machine (VM) consolidation is an efficient technique for reducing energy related costs and environmental concerns. This technique consists of placing as much as possible VMs on as less as possible Physical Machines (PM)s by means of live VM migration. However, VM consolidation approaches bring advantages, they cannot efficiently guarantee performance isolation of co-located VMs resulting in operational interference issues. Therefore, it is necessary to be aware of workload performance degradation issues during VMs consolidation in a datacenter. In this paper, we propose a novel joint profit and interference-aware scheduling scheme (PIAS) to efficiently consolidate VMs that host multi-tier application workloads in Infrastructure-as-a-Service (IaaS) cloud datacenter. The PIAS scheme considers profit and cost of power consumption, operational interference of VMs, resource utilization, and service level agreements (SLA)s as well as amount of transferred memory pages during live VM migration for consolidating VMs. In this regard, a stochastic dynamic programming is introduced to model the operational behavior of VM. Also, an optimization problem is introduced to maximize provider's profit and minimize the overall execution cost of application workloads. We simulate a series of experiments based on real workloads traces for different IaaS cloud datacenter configurations. The PIAS scheme on average provides better results than other competitors and the results demonstrate how our approach at least improves profit, energy efficiency, and service down time by 40%, 29%, and 35%, respectively.

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Embedding individualized machine learning prediction models for energy efficient VM consolidation within Cloud data centers

Cited by 45 publications

References 24 publications

HBWO-CML: An energy-efficient Hybrid Black Widow Optimisation and a Combined Machine Learning approach for VM Placement

HBWO-CML: An energy-efficient Hybrid Black Widow Optimisation and a Combined Machine Learning approach for VM Placement

Confidence interval‐based overload avoidance algorithm for virtual machine placement

Design and evaluation of a joint profit and interference-aware VMs consolidation in IaaS cloud datacenter

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