A global-energy-aware virtual machine placement strategy for cloud data centers

Feng, Hao; Deng, Yuhui; Li, Jie

doi:10.1016/j.sysarc.2021.102048

Cited by 54 publications

(16 citation statements)

References 29 publications

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“…Ilager et al [20] formulated the energy minimization problem as an optimization problem with thermal constraints and proposed a thermal-aware scheduling algorithm (ETAS) that was able to control data center temperature and dynamically integrate virtual machines so that the total energy consumption is minimized. Similarly, Feng et al [21] proposed a global energy-aware VMP strategy, but different from the literature [20], the energy consumption model established by the authors includes computing systems, cooling systems and network equipment. Arroba et al [22] designed a meta-heuristic optimization strategy that relies on a simulated annealing algorithm (SA) to achieve joint optimization of IT and cooling energy consumption.…”

Section: Related Workmentioning

confidence: 99%

“…In addition, the hotspots problem further increases the load and energy consumption of air conditioners. To this end, many scholars used the thermal-aware scheduling method to optimize workload distribution for thermal management to control data center temperature [20][21][22][23] . Thermal-aware scheduling balances the temperature of hosts through resource scheduling and integration to reduce thermal gradients and hotspots within the data center.…”

Section: Introductionmentioning

confidence: 99%

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A Resource Scheduling Method for Cloud Data Centers Based on Thermal Management

Mao

Chen

Cheng

et al. 2023

Preprint

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With the continuous growth of cloud computing services, the high energy consumption of cloud data centers has become an urgent problem to be solved. Virtual machine consolidation (VMC) is an important way to optimize energy consumption, however excessive consolidation may lead to local hotspots and increase the risk of equipment failure. Thermal-aware scheduling can solve this problem, but it is difficult to strike a balance between SLA and energy consumption. To solve the above problems, we propose a method for scheduling cloud data center resources based on thermal management (TM-VMC), which optimizes total energy consumption and proactively prevents hotspots from a global perspective. It includes four phases of the VM consolidation process, dynamically schedules VMs by detecting server temperature and utilization status in real time, and finds suitable target hosts based on an improved ant colony algorithm (UACO) for the VMs. We compare the TM-VMC approach with several existing mainstream VM consolidation algorithms under workloads from real-world data centers. Simulation experimental results show that the TM-VMC approach can proactively avoid data center hotspots and significantly reduce energy consumption while maintaining low SLA violation rates.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Resource Scheduling Method for Cloud Data Centers Based on Thermal Management

Mao

Chen

Cheng

et al. 2023

Preprint

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“…The system proposed by authors is also profit improvement system based on the energy parameters as they save energy up to 24% (Zhang, X. et al, 2019). Feng, H. et al, suggest a global-power-conscious virtual-machine placement VMP method to lessen, from more than one aspects, the entire power intake of statistics facilities (Feng, H. et al, 2021).…”

Section: Energy Consumption Modelingmentioning

confidence: 99%

Energy-Aware VM Scheduler

Shukla¹,

Chaturvedi²

2022

International Journal of Information System Modeling and Design

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Cost is the backbone of any business model to sustain and growth in the competitive business market. Considering this, higher energy consumption may affect the operational cost of any industry. Cloud computing is one of the most popular IT business model for service provider and its users. It is observed in many research, that the average cost of the services are highly dependent upon the run time and power consumption. More power consumption effect the environmental things as well. In this review, more than 100 research articles are considered to evaluate the energy consumption methods, issues and challenges from 2009 to 2021. Based on the study, the energy-aware load balancing methods are classified into four major clusters. The most cited methods are compared based on method used, platform where it is deployed, different evaluation parameters like cost, run time, Virtual Machine utilization, Service-level-agreement violation rate, and Quality-of-service QoS. The result of this review is presented as open research issues and challenges for forthcoming research.

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“…Feng et al [29] have developed a two-step SAG algorithm to minimize the energy consumption in cloud data centers. In the first step, Simulated Annealing (SA) algorithm was utilized to minimize the server and cooling energy consumption.…”

Section: Related Workmentioning

confidence: 99%

Intelligent SLA-Aware VM Allocation and Energy Minimization Approach with EPO Algorithm for Cloud Computing Environment

Samriya

Patel

Khurana

et al. 2021

Mathematical Problems in Engineering

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Cloud computing is the most prominent established framework; it offers access to resources and services based on large-scale distributed processing. An intensive management system is required for the cloud environment, and it should gather information about all phases of task processing and ensuring fair resource provisioning through the levels of Quality of Service (QoS). Virtual machine allocation is a major issue in the cloud environment that contributes to energy consumption and asset utilization in distributed cloud computing. Subsequently, in this paper, a multiobjective Emperor Penguin Optimization (EPO) algorithm is proposed to allocate the virtual machines with power utilization in a heterogeneous cloud environment. The proposed method is analyzed to make it suitable for virtual machines in the data center through Binary Gravity Search Algorithm (BGSA), Ant Colony Optimization (ACO), and Particle Swarm Optimization (PSO). To compare with other strategies, EPO is energy-efficient and there are significant differences. The results of the proposed system have been evaluated through the JAVA simulation platform. The exploratory outcome presents that the proposed EPO-based system is very effective in limiting energy consumption, SLA violation (SLAV), and enlarging QoS requirements for giving capable cloud service.

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A global-energy-aware virtual machine placement strategy for cloud data centers

Cited by 54 publications

References 29 publications

A Resource Scheduling Method for Cloud Data Centers Based on Thermal Management

A Resource Scheduling Method for Cloud Data Centers Based on Thermal Management

Energy-Aware VM Scheduler

Intelligent SLA-Aware VM Allocation and Energy Minimization Approach with EPO Algorithm for Cloud Computing Environment

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