Power management in virtualized data centers: state of the art

Al-Dulaimy, Auday; Itani, Wassim; Zekri, Ahmed; Zantout, Rached

doi:10.1186/s13677-016-0055-y

Cited by 21 publications

(9 citation statements)

References 34 publications

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“…Extra VM migration may affect energy efficiency, leading to further power consumption. So, VM migration is a very critical process that should be optimally done to avoid unnecessary VM migration [9], and attempt to balances energy saving energy resulting from turning off PMs not in use with energy use required for the migration process. -Failures due to power outages or network component are called correlated failures.…”

Section: Discussionmentioning

confidence: 99%

Proactive dynamic virtual-machine consolidation for energy conservation in cloud data centres

2018

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Data center power consumption is among the largest commodity expenditures for many organizations. Reduction of power used in cloud data centres with heterogeneous physical resources can be achieved through Virtual-Machine (VM) consolidation which reduces the number of Physical Machines (PMs) used, subject to Quality of Service (QoS) constraints. This paper provides an in-depth survey of the most recent techniques and algorithms used in proactive dynamic VM consolidation focused on energy consumption. We present a general framework that can be used on multiple phases of a complete consolidation process.

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Section: Discussionmentioning

confidence: 99%

Proactive dynamic virtual-machine consolidation for energy conservation in cloud data centres

2018

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“…Also, reviewing the power efficiency techniques in virtualized cloud data centers have been done by Al‐Dulaimy et al They provided the power management techniques taxonomy into two main parts, namely, the static power management and the dynamic power management, and also highlighted some current challenges and future directions concerning power management studies in cloud computing. Their work summarized the power efficiency studies from many perspectives including the single or multiple data centers, the homogenous or heterogeneous environment, the online or offline scheduling, and the resource type involved in enhancing the energy efficiency.…”

Section: Related Workmentioning

confidence: 99%

Multiobjective virtual machine placement mechanisms using nature‐inspired metaheuristic algorithms in cloud environments: A comprehensive review

Vahed

Ghobaei‐Arani

Souri

2019

Int J Communication

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Cloud computing introduced a new paradigm in IT industry by providing ondemand, elastic, ubiquitous computing resources for users. In a virtualized cloud data center, there are a large number of physical machines (PMs) hosting different types of virtual machines (VMs). Unfortunately, the cloud data centers do not fully utilize their computing resources and cause a considerable amount of energy waste that has a great operational cost and dramatic impact on the environment. Server consolidation is one of the techniques that provide efficient use of physical resources by reducing the number of active servers. Since VM placement plays an important role in server consolidation, one of the main challenges in cloud data centers is an efficient mapping of VMs to PMs. Multiobjective VM placement is generating considerable interest among researchers and academia. This paper aims to represent a detailed review of the recent state-of-the-art multiobjective VM placement mechanisms using nature-inspired metaheuristic algorithms in cloud environments. Also, it gives special attention to the parameters and approaches used for placing VMs into PMs. In the end, we will discuss and explore further works that can be done in this area of research.KEYWORDS cloud computing, multiobjective optimization, nature-inspired metaheuristic algorithms, server consolidation, virtual machine placement | INTRODUCTIONCloud computing is an emerging paradigm in IT industry, which is mainly based on two important concepts including abstraction and virtualization. 1 As NIST 2 defined, this model provides on-demand, ubiquitous, rapid, and elastic access to shared resources, which is based on the pay-as-go model. Cloud services can appear into two distinct models: deployment models and service models. Cloud deployment models are based on where the cloud's infrastructure has deployed, and they can be classified into four categories: public clouds, private clouds, hybrid clouds, and community clouds. [3][4][5] On the other hand, the cloud service models are based on the services they provide. Generally, there are three common basic cloud services including Infrastructure as a Service (IaaS), Platform as a Service (PaaS), and Software as a Service (SaaS).

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“…There have been several surveys available in the literature. Al-Dulaimy et al [6] surveyed previous studies and researches that aimed to improve power efficiency of virtualized data centers. Beloglazov et al [7] discussed causes and problems of high power/energy consumption, and presented a taxonomy of energy efficient design of computing systems, covering the hardware, operating system, virtualization, and data center levels.…”

Section: B Related Workmentioning

confidence: 99%

Optimal Power and Performance Management for Heterogeneous and Arbitrary Cloud Servers

Li¹

2019

IEEE Access

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We investigate optimal power and performance management for heterogeneous and arbitrary cloud servers in a data center. In particular, we study the problems of power-constrained performance optimization and performance-constrained power optimization in a data center with multiple heterogeneous and arbitrary servers. These problems are essential to find optimal server speeds, such that: 1) the average task response time is minimized, and that the total power consumption does not exceed certain power constraint or 2) the total power consumption is minimized, and that the average task response time does not exceed certain performance constraint. Each server is treated as a G/G/1 queuing system, whose task interarrival times and task execution requirements can have arbitrary probability distributions. Furthermore, these servers are entirely heterogeneous in terms of task interarrival time, task execution requirement, and power consumption models. The main contributions of this paper are summarized as follows: 1) we formulate the average task response time as well as the total power consumption in a data center with multiple heterogeneous and arbitrary servers as the functions of server speeds; 2) we define our optimization problems by finding optimal server speeds, since the server speeds determine both the average task response time and total power consumption; 3) we develop algorithms to find the optimal solutions and demonstrate numerical data; and 4) we also develop several closed-form heuristic solutions and compare their quality with that of the optimal solution. Our approach provides an analytical way of studying the power-performance tradeoff at the data center level. INDEX TERMS Arbitrary cloud server, average task response time, data center, heterogeneous server, power consumption.

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Power management in virtualized data centers: state of the art

Cited by 21 publications

References 34 publications

Proactive dynamic virtual-machine consolidation for energy conservation in cloud data centres

Proactive dynamic virtual-machine consolidation for energy conservation in cloud data centres

Multiobjective virtual machine placement mechanisms using nature‐inspired metaheuristic algorithms in cloud environments: A comprehensive review

Optimal Power and Performance Management for Heterogeneous and Arbitrary Cloud Servers

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