Power Management of Virtualized Cloud Computing Platform

Ye, Kejiang; Wu, Zhaohui; Jiang, Xiaohong; He, Qinming

doi:10.3724/sp.j.1016.2012.01262

Cited by 41 publications

(10 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Linear power model is widely used in researches including Ye et al (2012), Basmadjian et al (2011), Kansal et al (2010 and Lee and Zomaya (2012). In addition to that, linear model is also popular in evaluating resource scheduling algorithms (Sampaio and Barbosa 2014;Beloglazov et al 2012) and virtual machine placement strategies (Lin et al 2015a).…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

An intelligent power consumption model for virtual machines under CPU-intensive workload in cloud environment

Lin

Peng

2016

Soft Comput

View full text Add to dashboard Cite

Cloud computing has gained enormous popularity by providing high availability and scalability as well as on-demand services. However, with the continuous rise of energy consumption cost, the virtualized environment of cloud data centers poses a challenge to today's power monitoring system. Software-based power monitoring is gaining prevalence since power models can work precisely by exploiting soft computing methodologies like genetic programming and swarm intelligence for model optimization. However, traditional power models barely consider virtualization and have drawbacks like high error rate, low feasibility as well as insufficient scalability. In this paper, we first analyze the power signatures of virtual machines in different configurations through experiments. Then we propose a virtual machine (VM) power model, named CAM, which is able to adapt to the reconfiguration of VMs and provide accurate power estimating under CPU-intensive workload. We also propose two training methodologies corresponding to two typical situations for model training. CAM can estimate the power of a single VM as well as a physical server hosting several heterogeneous VMs. We exploited public Linux benchmarks to evaluate CAM .The experimental results show that CAM produced very small errors in power estimating for both VMs (4.26 % on average) and the host server (0.88 % on average).

show abstract

Section: Introductionmentioning

confidence: 99%

“…Related workThe core of software-based power monitoring is power model(Ye et al 2012). Generally, power models can be divided…”

mentioning

confidence: 99%

An intelligent power consumption model for virtual machines under CPU-intensive workload in cloud environment

Lin

Peng

2016

Soft Comput

View full text Add to dashboard Cite

show abstract

“…Workload consolidation [5] is a common method to reduce computing energy and its main objectives is minimizing the number of active servers, assigning higher scheduling priority to servers with greater energy-efficiency, and avoidance of resource fragmentation [36]. Workload consolidation is commonly modelled as a bin packing problem which has been proved to be NP-complete [15].…”

Section: Software-level Methodsmentioning

confidence: 99%

“…This becomes particularly important as the development of virtualization technology which allows for live migrations of VMs. Workload co nsolidation via VM migration can be modelled with linear integer programming formulation [36]. Beloglazov and Buyya [25] presented various of algorithms to deal with key problems in migrations such as selecting VMs to migrate out from overloaded server and selecting new placement for migrated VMs.…”

Section: Software-level Methodsmentioning

confidence: 99%

Holistic Virtual Machine Scheduling in Cloud Datacenters towards Minimizing Total Energy

Garraghan

Jiang

et al. 2018

IEEE Trans. Parallel Distrib. Syst.

View full text Add to dashboard Cite

Abstract-Energy consumed by Cloud datacenters has dramatically increased, driven by rapid uptake of applications and services globally provisioned through virtualization. By applying energy-aware virtual machine scheduling, Cloud providers are able to achieve enhanced energy efficiency and reduced operation cost. Energy consumption of datacenters consists of computing energy and cooling energy. However, due to the complexity of energy and thermal modeling of realistic Cloud datacenter operation, traditional approaches are unable to provide a comprehensive in-depth solution for virtual machine scheduling which encompasses both computing and cooling energy. This paper addresses this challenge by presenting an elaborate thermal model that analyzes the temperature distribution of airflow and server CPU. We propose GRANITE -a holistic virtual machine scheduling algorithm capable of minimizing total datacenter energy consumption. The algorithm is evaluated against other existing workload scheduling algorithms MaxUtil, TASA, IQR and Random using real Cloud workload characteristics extracted from Google datacenter tracelog. Results demonstrate that GRANITE consumes 4.3% -43.6% less total energy in comparison to the state-of-the-art, and reduces the probability of critical temperature violation by 99.2% with 0.17% SLA violation rate as the performance penalty.

show abstract

“…A large number of data centers use virtualization technology and cloud computing technology. The integration of traditional power management technology and virtualization technology provides new solutions to solve the power management issue of cloud data center [3][4][5]. Operation and power management in virtualization are major challenges in cloud platform.…”

Section: Optimization Of Energy Consumption In Cloud Datamentioning

confidence: 99%

Ant Colony Optimization Algorithm to Dynamic Energy Management in Cloud Data Center

Pang

Zhang

et al. 2017

Mathematical Problems in Engineering

View full text Add to dashboard Cite

With the wide deployment of cloud computing data centers, the problems of power consumption have become increasingly prominent. The dynamic energy management problem in pursuit of energy-efficiency in cloud data centers is investigated. Specifically, a dynamic energy management system model for cloud data centers is built, and this system is composed of DVS Management Module, Load Balancing Module, and Task Scheduling Module. According to Task Scheduling Module, the scheduling process is analyzed by Stochastic Petri Net, and a task-oriented resource allocation method (LET-ACO) is proposed, which optimizes the running time of the system and the energy consumption by scheduling tasks. Simulation studies confirm the effectiveness of the proposed system model. And the simulation results also show that, compared to ACO, Min-Min, and RR scheduling strategy, the proposed LET-ACO method can save up to 28%, 31%, and 40% energy consumption while meeting performance constraints.

show abstract

Power Management of Virtualized Cloud Computing Platform

Cited by 41 publications

References 0 publications

An intelligent power consumption model for virtual machines under CPU-intensive workload in cloud environment

An intelligent power consumption model for virtual machines under CPU-intensive workload in cloud environment

Holistic Virtual Machine Scheduling in Cloud Datacenters towards Minimizing Total Energy

Ant Colony Optimization Algorithm to Dynamic Energy Management in Cloud Data Center

Contact Info

Product

Resources

About