Performance and Energy-based Cost Prediction of Virtual Machines Live Migration in Clouds

Aldossary, Mohammad; Djemame, Karim

doi:10.5220/0006682803840391

Cited by 15 publications

(25 citation statements)

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“…Further, an experimental study was carried out to investigate the effect of the resource usage (CPU, RAM, disk and network) on the power consumption. The findings [13,14,15] show that the CPU utilisation correlates well with the power consumption, which is supported in other work, for example [11,16,17]. Thus, the work introduced in this paper follows the same approach and takes into account the CPU utilisation only when modelling and identifying the energy consumption for the VMs.…”

Section: Energy-aware Virtual Machine Modelsupporting

confidence: 77%

Energy-aware cost prediction and pricing of virtual machines in cloud computing environments

Aldossary

Djemame

Alzamil

et al. 2019

Future Generation Computer Systems

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Section: Energy-aware Virtual Machine Modelsupporting

confidence: 77%

Energy-aware cost prediction and pricing of virtual machines in cloud computing environments

Aldossary

Djemame

Alzamil

et al. 2019

Future Generation Computer Systems

Self Cite

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“…To contrast: works like [70,[84][85][86][87][88] are not included within this category -notwithstanding their development of models for prediction of host system power consumption. In these works, host system models were developed as part of the scope of the challenge of modeling virtualized entities.…”

Section: P3mentioning

confidence: 99%

“…(b) Nonetheless, this power consumption must be accounted for and different approaches have been followed. For example: the physical machine's idle power is attributed to individual VEs in fractions equal to the ratio of each VE's virtual CPUs (vCPUs) count to the total complement of vCPUs active on the physical machine [70,[84][85][86].…”

Section: P7: Loading the Ve's Resources And Measuring Resource Usementioning

confidence: 99%

A Categorization of Power Measurement and Power Consumption Models in the Telco Cloud

Depasquale

Salam

Davoli

2021

Preprint

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This article surveys the literature, over the period 2010-2020, on measurement of power consumption and relevant power models of virtual entities as they apply to the telco cloud. Hardware power meters are incapable of measuring power consumption of individual virtual entities co-hosted on a physical machine. Thus, software power meters are inevitable, yet their development is difficult. Indeed, there is no direct approach to measurement and, therefore, modeling through proxies of power consumption must be used. In this survey, we present trends, fallacies and pitfalls. Notably, we identify limitations of the widely-used linear models and the progression towards Artificial Intelligence / Machine Learning techniques as a means of dealing with the seven major dimensions of variability: workload type; computer virtualization agents; system architecture and resources; concurrent, co-hosted virtualized entities; approaches towards attribution of power consumption to virtual entities; frequency; and temperature.

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“…The authors of [8] proposed the performance and energy-based cost prediction framework that dynamically supports VMs auto-scaling decision and demonstrates the trade-off between cost, power consumption and performance. This framework allows to estimate auto-scaling total cost, which is essential when using consolidation and horizontal auto-scaling approaches.…”

Section: Electricity Usage Of Data Centres 2010-2030 Estimationmentioning

confidence: 99%

Intelligent Workload Scheduling in Distributed Computing Environment for Balance between Energy Efficiency and Performance

Globa¹,

Stryzhak²,

Gvozdetska³

et al. 2020

Scheduling Problems - New Applications and Trends

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Global digital transformation requires more productive large-scale distributed systems. Such systems should meet lots of requirements, such as high availability, low latency and reliability. However, new challenges become more and more important nowadays. One of them is energy efficiency of large-scale computing systems. Many service providers prefer to use cheap commodity servers in their distributed infrastructure, which makes the problem of energy efficiency even harder because of hardware inhomogeneity. In this chapter an approach to finding balance between performance and energy efficiency requirements within inhomogeneous distributed computing environment is proposed. The main idea of the proposed approach is to use each node's individual energy consumption models in order to generate distributed system scaling patterns based on the statistical daily workload and then adjust these patterns to match the current workload while using energy-aware Power Consumption and Performance Balance (PCPB) scheduling algorithm. An approach is tested using Matlab modeling. As a result of applying the proposed approach, large-scale distributed computing systems save energy while maintaining a fairly high level of performance and meeting the requirements of the service-level agreement (SLA).

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Performance and Energy-based Cost Prediction of Virtual Machines Live Migration in Clouds

Cited by 15 publications

References 0 publications

Energy-aware cost prediction and pricing of virtual machines in cloud computing environments

Energy-aware cost prediction and pricing of virtual machines in cloud computing environments

A Categorization of Power Measurement and Power Consumption Models in the Telco Cloud

Intelligent Workload Scheduling in Distributed Computing Environment for Balance between Energy Efficiency and Performance

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