scholarly journals Efficient virtual machine placement algorithms for consolidation in cloud data centers

2020 ◽  
Vol 17 (1) ◽  
pp. 29-50
Author(s):  
Loiy Alsbatin ◽  
Gürcü Öz ◽  
Ali Ulusoy
Keyword(s):  
Virtual Machine ◽  
Service Level Agreement ◽  
Energy Performance ◽  
Service Level ◽  
Vm Placement ◽  
Cloud Data ◽  
Successful Approach ◽  
Cloud Data Centers ◽  
Placement Algorithms ◽  
Best Fit

Dynamic Virtual Machine (VM) consolidation is a successful approach to improve the energy efficiency and the resource utilization in cloud environments. Consequently, optimizing the online energy-performance tradeoff directly influences quality of service. In this study, algorithms named as CPU Priority based Best-Fit Decreasing (CPBFD) and Dynamic CPU Priority based Best-Fit Decreasing (DCPBFD) are proposed for VM placement. A number of VM placement algorithms are implemented and compared with the proposed algorithms. The algorithms are evaluated through simulations with real-world workload traces and it is shown that the proposed algorithms outperform the known algorithms. The simulation results clearly show that CPBFD and DCPBFD provide the least service level agreement violations, least VM migrations, and efficient energy consumption.

Energy Efficient VM Live Migration and Allocation at Cloud Data Centers

2014 ◽  
Vol 4 (4) ◽  
pp. 55-63 ◽  
Author(s):  
Djouhra Dad ◽  
Djamel Eddine Yagoubi ◽  
Ghalem Belalem
Keyword(s):  
Data Center ◽  
Energy Efficient ◽  
Virtual Machines ◽  
Service Level Agreement ◽  
Service Level ◽  
Live Migration ◽  
Cloud Data ◽  
Cloud Data Centers ◽  
High Utilization ◽  
Best Fit

Aiming at data center virtual machines Migration, allocating resource dynamically in order to reduce energy is a significant problem in cloud. This energy doesn't cause only the decrease of cloud provider's profit but also emit a large amount of carbon dioxide. This paper studies the resource allocation and live migration of Virtual Machines (VMs). It proposes a Double Threshold Migration (DTM) algorithm which takes into consideration an upper and a lower threshold of CPU utilization. These Thresholds let one select a number of VMs to do the migration. The live migration of the VMs reduces the high utilization of the servers and set on off state the unused physical machines (PMs). To solve the problem of the VM placement, the work applies a modification of the Best Fit Decreasing (MBFD) algorithm. Experiment results show that the proposed approach improve resource utilization, reduce the energy consumption and maintain the SLA (Service Level Agreement) violations with the energy constraint.

scholarly journals Virtual Machine Consolidation with Minimization of Migration Thrashing for Cloud Data Centers

2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Xialin Liu ◽  
Junsheng Wu ◽  
Gang Sha ◽  
Shuqin Liu
Keyword(s):  
Virtual Machine ◽  
Carbon Dioxide Emissions ◽  
Data Centers ◽  
Virtual Machines ◽  
Service Level Agreement ◽  
High Capacity ◽  
Electrical Energy ◽  
Cloud Data ◽  
Cloud Data Centers ◽  
Vm Consolidation

Cloud data centers consume huge amount of electrical energy bringing about in high operating costs and carbon dioxide emissions. Virtual machine (VM) consolidation utilizes live migration of virtual machines (VMs) to transfer a VM among physical servers in order to improve the utilization of resources and energy efficiency in cloud data centers. Most of the current VM consolidation approaches tend to aggressive-migrate for some types of applications such as large capacity application such as speech recognition, image processing, and decision support systems. These approaches generate a high migration thrashing because VMs are consolidated to servers according to VM’s instant resource usage without considering their overall and long-term utilization. The proposed approach, dynamic consolidation with minimization of migration thrashing (DCMMT) which prioritizes VM with high capacity, significantly reduces migration thrashing and the number of migrations to ensure service-level agreement (SLA) since it keeps VMs likely to suffer from migration thrashing in the same physical servers instead of migrating. We have performed experiments using real workload traces compared to existing aggressive-migration-based solutions; through simulations, we show that our approach improves migration thrashing metric by about 28%, number of migrations metric by about 21%, and SLAV metric by about 19%.

scholarly journals An Distributed Virtual Machine Placement Algorithm for Balanced Resource Utilization and Low Energy Consumption

2018 ◽  
Vol 173 ◽  
pp. 03092
Author(s):  
Bo Li ◽  
Yun Wang
Keyword(s):  
Virtual Machine ◽  
Data Centers ◽  
Virtual Machine Placement ◽  
Vm Placement ◽  
Energy Aware ◽  
Cloud Data ◽  
Low Energy Consumption ◽  
Cloud Data Centers ◽  
Placement Algorithm ◽  
Heterogeneous Cloud

Virtual machine placement is the process of selecting the most suitable server in large cloud data centers to deploy newly-created VMs. Traditional load balancing or energy-aware VM placement approaches either allocate VMs to PMs in centralized manner or ignore PM’s cost-capacity ratio to implement energy-aware VM placement. We address these two issues by introducing a distributed VM placement approach. A auction-based VM placement algorithm is devised for help VM to find the most suitable server in large heterogeneous cloud data centers. Our algorithm is evaluated by simulation. Experimental results show two major improvements over the existing approaches for VM placement. First, our algorithm efficiently balances the utilization of multiple types of resource by minimizing the amount of physical servers used. Second, it reduces system cost compared with existing approaches in heterogeneous environment.

Optimizing the Migration of Virtual Machines in Cloud Data Centers

2022 ◽  
Vol 13 (1) ◽  
pp. 0-0
Keyword(s):  
Data Centers ◽  
Bin Packing ◽  
Virtual Machines ◽  
Service Level Agreement ◽  
Service Level ◽  
Virtual Machine Migration ◽  
Cloud Data ◽  
Time Simulation ◽  
Cloud Data Centers ◽  
Dynamic Resource

Dynamic resource allocation of cloud data centers is implemented with the use of virtual machine migration. Selected virtual machines (VM) should be migrated on appropriate destination servers. This is a critical step and should be performed according to several criteria. It is proposed to use the criteria of minimum resource wastage and service level agreement violation. The optimization problem of the VM placement according to two criteria is formulated, which is equivalent to the well-known main assignment problem in terms of the structure, necessary conditions, and the nature of variables. It is suggested to use the Hungarian method or to reduce the problem to a closed transport problem. This allows the exact solution to be obtained in real time. Simulation has shown that the proposed approach outperforms widely used bin-packing heuristics in both criteria.

Service level agreement based energy-efficient resource management in cloud data centers

2014 ◽  
Vol 40 (5) ◽  
pp. 1621-1633 ◽  
Author(s):  
Yongqiang Gao ◽  
Haibing Guan ◽  
Zhengwei Qi ◽  
Tao Song ◽  
Fei Huan ◽  
...  
Keyword(s):  
Resource Management ◽  
Energy Efficient ◽  
Data Centers ◽  
Service Level Agreement ◽  
Service Level ◽  
Cloud Data ◽  
Efficient Resource ◽  
Cloud Data Centers

scholarly journals Growth Potential Aware Virtual Machine Consolidation Framework

2020 ◽  
Author(s):  
Swasthi Shetty ◽  
Annappa B
Keyword(s):  
Virtual Machine ◽  
Data Centers ◽  
Virtual Machines ◽  
Service Level Agreement ◽  
Detection Algorithm ◽  
Growth Potential ◽  
Vm Placement ◽  
Trade Off ◽  
Cloud Data ◽  
Virtual Machine Consolidation

<div> <div> <div> <p>Virtual machine consolidation techniques provide ways to save energy and cost in cloud data centers. However, aggressive packing of virtual machines can cause performance degradation. Therefore, it is essential to strike a trade-off between energy and performance in data centers. Achieving this trade-off has been an active research area in recent years. In this paper, a host underload detection algorithm and a new VM selection and VM placement techniques are proposed to consolidate Virtual machines based on the growth potential of VMs. Growth potential is calculated based on the utilization history of VMs. The interdependence of VM selection and VM placement techniques are also studied in the proposed model. The proposed algorithms are evaluated on real- world PlanetLab workload on Cloudsim. The experimental evaluation shows that our proposed technique reduces Service Level Agreement Violation (SLAV) and energy consumption compared to the existing algorithms. </p> </div> </div> </div>

Resource and Energy Efficient Virtual Machine Migration in Cloud Data Centers

2017 ◽  
pp. 210-238
Author(s):  
Subrat Kumar Dhal ◽  
Harshit Verma ◽  
Sourav Kanti Addya
Keyword(s):  
Cloud Computing ◽  
Data Centers ◽  
Cloud Model ◽  
Service Level Agreement ◽  
Optimal Solution ◽  
Service Level ◽  
Virtual Machine Migration ◽  
Cloud Data ◽  
Cloud Data Centers ◽  
Selection Algorithms

Cloud computing service has been on the rise over the past few decades, which has led to an increase in the number of data centers, thus consuming more amount of energy for their operation. Moreover, the energy consumption in the cloud is proportional to the resource utilization. Thus consolidation schemes for the cloud model need to be devised to minimize energy by decreasing the operating costs. The consolidation problem is NP-complete, which requires heuristic techniques to get a sub-optimal solution. The authors have proposed a new consolidation scheme for the virtual machines (VMs) by improving the host overload detection phase. The resulting scheme is effective in reducing the energy and the level of Service Level Agreement (SLA) violations both, to a considerable extent. For testing the performance of implementation, a simulation environment is needed that can provide an environment of the actual cloud computing components. The authors have used CloudSim 3.0.3 simulation toolkit that allows testing and analyzing Allocation and Selection algorithms.

scholarly journals A Survey on the Use of Preferences for Virtual Machine Placement in Cloud Data Centers

2021 ◽  
Vol 54 (5) ◽  
pp. 1-39
Author(s):  
Abdulaziz Alashaikh ◽  
Eisa Alanazi ◽  
Ala Al-Fuqaha
Keyword(s):  
Virtual Machine ◽  
Data Centers ◽  
Rapid Development ◽  
Cost Effective ◽  
Vm Placement ◽  
Placement Problem ◽  
Cloud Data ◽  
Cloud Data Centers ◽  
Design Factors ◽  
The Given

With the rapid development of virtualization techniques, cloud data centers allow for cost-effective, flexible, and customizable deployments of applications on virtualized infrastructure. Virtual machine (VM) placement aims to assign each virtual machine to a server in the cloud environment. VM Placement is of paramount importance to the design of cloud data centers. Typically, VM placement involves complex relations and multiple design factors as well as local policies that govern the assignment decisions. It also involves different constituents including cloud administrators and customers that might have disparate preferences while opting for a placement solution. Thus, it is often valuable to return not only an optimized solution to the VM placement problem but also a solution that reflects the given preferences of the constituents. In this article, we provide a detailed review on the role of preferences in the recent literature on VM placement. We examine different preference representations found in the literature, explain their existing usage, and explain the adopted solving approaches. We further discuss key challenges and identify possible research opportunities to better incorporate preferences within the context of VM placement.

scholarly journals A Novel Physical Machine Overload Detection Algorithm Combined with Quiescing for Dynamic Virtual Machine Consolidation in Cloud Data Centers

2019 ◽  
Vol 17 (3) ◽  
pp. 358-366
Author(s):  
Loiy Alsbatin ◽  
Gürcü Öz ◽  
Ali Ulusoy
Keyword(s):  
Virtual Machine ◽  
Data Centers ◽  
Virtual Machines ◽  
Energy Performance ◽  
Detection Algorithm ◽  
Vm Migration ◽  
Cloud Data ◽  
Detection Algorithms ◽  
Physical Machine ◽  
Cloud Data Centers

Further growth of computing performance has been started to be limited due to increasing energy consumption of cloud data centers. Therefore, it is important to pay attention to the resource management. Dynamic virtual machines consolidation is a successful approach to improve the utilization of resources and energy efficiency in cloud environments. Consequently, optimizing the online energy-performance trade off directly influences Quality of Service (QoS). In this paper, a novel approach known as Percentage of Overload Time Fraction Threshold (POTFT) is proposed that decides to migrate a Virtual Machine (VM) if the current Overload Time Fraction (OTF) value of Physical Machine (PM) exceeds the defined percentage of maximum allowed OTF value to avoid exceeding the maximum allowed resulting OTF value after a decision of VM migration or during VM migration. The proposed POTFT algorithm is also combined with VM quiescing to maximize the time until migration, while meeting QoS goal. A number of benchmark PM overload detection algorithms is implemented using different parameters to compare with POTFT with and without VM quiescing. We evaluate the algorithms through simulations with real world workload traces and results show that the proposed approaches outperform the benchmark PM overload detection algorithms. The results also show that proposed approaches lead to better time until migration by keeping average resulting OTF values less than allowed values. Moreover, POTFT algorithm with VM quiescing is able to minimize number of migrations according to QoS requirements and meet OTF constraint with a few quiescings.

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