scholarly journals Energy-Aware and Proactive Host Load Detection in Virtual Machine Consolidation

2021 ◽  
Vol 50 (2) ◽  
pp. 332-341
Author(s):  
Seyed Yahya Zahedi Fard ◽  
Mohammad Karim Sohrabi ◽  
Vahid Ghods
Keyword(s):  
Energy Consumption ◽  
Virtual Machine ◽  
Power Efficiency ◽  
Data Centers ◽  
Service Providers ◽  
Energy Utilization ◽  
Energy Aware ◽  
Cloud Data ◽  
Server Utilization ◽  
Vm Consolidation

With the expansion and enhancement of cloud data centers in recent years, increasing the energy consumptionand the costs of the users have become the major concerns in the cloud research area. Service quality parametersshould be guaranteed to meet the demands of the users of the cloud, to support cloud service providers,and to reduce the energy consumption of the data centers. Therefore, the data center's resources must be managedefficiently to improve energy utilization. Using the virtual machine (VM) consolidation technique is animportant approach to enhance energy utilization in cloud computing. Since users generally do not use all thepower of a VM, the VM consolidation technique on the physical server improves the energy consumption andresource efficiency of the physical server, and thus improves the quality of service (QoS). In this article, a serverthreshold prediction method is proposed that focuses on the server overload and server underload detectionto improve server utilization and to reduce the number of VM migrations, which consequently improves theVM's QoS. Since the VM integration problem is very complex, the exponential smoothing technique is utilizedfor predicting server utilization. The results of the experiments show that the proposed method goes beyondexisting methods in terms of power efficiency and the number of VM migrations.

scholarly journals Energy-Aware Virtual Machine Clustering for Consolidation in Multi-tenant IaaS Public Clouds

2019 ◽  
pp. 1123-1136
Author(s):  
Kenga Mosoti Derdus ◽  
Vincent Oteke Omwenga ◽  
Patrick Job Ogao
Keyword(s):  
Energy Consumption ◽  
Virtual Machine ◽  
Service Providers ◽  
Cloud Service ◽  
Resource Usage ◽  
Energy Aware ◽  
Allocation Policy ◽  
Cloud Data ◽  
Vm Allocation ◽  
Vm Consolidation

Cloud computing has gained a lot of interest from both small and big academic and commercial organizations because of its success in delivering service on a pay-as-you-go basis. Moreover, many users (organizations) can share server computing resources, which is made possible by virtualization. However, the amount of energy consumed by cloud data centres is a major concern. One of the major causes of energy wastage is the inefficient utilization of resources. For instance, in IaaS public clouds, users select Virtual Machine (VM) sizes set beforehand by the Cloud Service Providers (CSPs) without the knowledge of the kind of workloads to be executed in the VM. More often, the users overprovision the resources, which go to waste. Additionally, the CSPs do not have control over the types of applications that are executed and thus VM consolidation is performed blindly. There have been efforts to address the problem of energy consumption by efficient resource utilization through VM allocation and migration. However, these techniques lack collection and analysis of active real cloud traces from the IaaS cloud. This paper proposes an architecture for VM consolidation through VM profiling and analysis of VM resource usage and resource usage patterns, and a VM allocation policy. We have implemented our policy on CloudSim Plus cloud simulator and results show that it outperforms Worst Fit, Best Fit and First Fit VM allocation algorithms. Energy consumption is reduced through efficient consolidation that is informed by VM resource consumption.

scholarly journals Energy aware dynamic virtual machine consolidation in cloud data centers

2018 ◽  
Vol 7 (2.8) ◽  
pp. 550 ◽  
Author(s):  
G Anusha ◽  
P Supraja
Keyword(s):  
Energy Consumption ◽  
Virtual Machine ◽  
Data Centers ◽  
Energy Aware ◽  
Large Power ◽  
Complex Tasks ◽  
Cloud Data ◽  
Network Bandwidth ◽  
Virtual Machine Consolidation ◽  
Cloud Data Centers

Cloud computing is a growing technology now-a-days, which provides various resources to perform complex tasks. These complex tasks can be performed with the help of datacenters. Data centers helps the incoming tasks by providing various resources like CPU, storage, network, bandwidth and memory, which has resulted in the increase of the total number of datacenters in the world. These data centers consume large volume of energy for performing the operations and which leads to high operation costs. Resources are the key cause for the power consumption in data centers along with the air and cooling systems. Energy consumption in data centers is comparative to the resource usage. Excessive amount of energy consumption by datacenters falls out in large power bills. There is a necessity to increase the energy efficiency of such data centers. We have proposed an Energy aware dynamic virtual machine consolidation (EADVMC) model which focuses on pm selection, vm selection, vm placement phases, which results in the reduced energy consumption and the Quality of service (QoS) to a considerable level.

scholarly journals Energy-aware load balancing in distributed data centers

2020 ◽  
Author(s):  
Rodrigo A. C. Da Silva ◽  
Nelson L. S. Da Fonseca
Keyword(s):  
Energy Consumption ◽  
Load Balancing ◽  
Virtual Machine ◽  
Data Center ◽  
Data Centers ◽  
Energy Savings ◽  
Virtual Machines ◽  
Distributed Data ◽  
Energy Aware ◽  
Cloud Data

This paper summarizes the dissertation ”Energy-aware load balancing in distributed data centers”, which proposed two new algorithms for minimizing energy consumption in cloud data centers. Both algorithms consider hierarchical data center network topologies and requests for the allocation of groups of virtual machines (VMs). The Topology-aware Virtual Machine Placement (TAVMP) algorithm deals with the placement of virtual machines in a single data center. It reduces the blocking of requests and yet maintains acceptable levels of energy consumption. The Topology-aware Virtual Machine Selection (TAVMS) algorithm chooses sets of VM groups for migration between different data centers. Its employment leads to relevant overall energy savings.

Advances in Dynamic Virtual Machine Management for Cloud Data Centers

2017 ◽  
pp. 91-114
Author(s):  
Rashmi Rai ◽  
G. Sahoo
Keyword(s):  
Energy Consumption ◽  
Virtual Machine ◽  
Large Scale ◽  
Data Centers ◽  
Economic Modeling ◽  
User Preferences ◽  
Virtual Machine Migration ◽  
Carbon Footprints ◽  
Cloud Data ◽  
Computing Services

The ever-rising demand for computing services and the humongous amount of data generated everyday has led to the mushrooming of power craving data centers across the globe. These large-scale data centers consume huge amount of power and emit considerable amount of CO2.There have been significant work towards reducing energy consumption and carbon footprints using several heuristics for dynamic virtual machine consolidation problem. Here we have tried to solve this problem a bit differently by making use of utility functions, which are widely used in economic modeling for representing user preferences. Our approach also uses Meta heuristic genetic algorithm and the fitness is evaluated with the utility function to consolidate virtual machine migration within cloud environment. The initial results as compared with existing state of art shows marginal but significant improvement in energy consumption as well as overall SLA violations.

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 Server Consolidation Based on Culture Multiple-Ant-Colony Algorithm in Cloud Computing

Sensors ◽  
2019 ◽  
Vol 19 (12) ◽  
pp. 2724 ◽  
Author(s):  
Yuan ◽  
Sun
Keyword(s):  
Energy Consumption ◽  
Virtual Machine ◽  
Ant Colony Algorithm ◽  
Data Centers ◽  
Virtual Machines ◽  
Ant Colony ◽  
Virtual Machine Migration ◽  
Server Consolidation ◽  
Cloud Data ◽  
Cloud Data Centers

High-energy consumption in data centers has become a critical issue. The dynamic server consolidation has significant effects on saving energy of a data center. An effective way to consolidate virtual machines is to migrate virtual machines in real time so that some light load physical machines can be turned off or switched to low-power mode. The present challenge is to reduce the energy consumption of cloud data centers. In this paper, for the first time, a server consolidation algorithm based on the culture multiple-ant-colony algorithm was proposed for dynamic execution of virtual machine migration, thus reducing the energy consumption of cloud data centers. The server consolidation algorithm based on the culture multiple-ant-colony algorithm (CMACA) finds an approximate optimal solution through a specific target function. The simulation results show that the proposed algorithm not only reduces the energy consumption but also reduces the number of virtual machine migration.

Energy-aware virtual machine allocation and selection in cloud data centers

2017 ◽  
Vol 23 (6) ◽  
pp. 1917-1932 ◽  
Author(s):  
V. Dinesh Reddy ◽  
G. R. Gangadharan ◽  
G. Subrahmanya V. R. K. Rao
Keyword(s):  
Virtual Machine ◽  
Data Centers ◽  
Energy Aware ◽  
Cloud Data ◽  
Cloud Data Centers ◽  
Machine Allocation ◽  
Virtual Machine Allocation

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.

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