Modeling Heterogeneous Virtual Machines on IaaS Data Centers

2015 ◽  
Vol 19 (4) ◽  
pp. 537-540 ◽  
Author(s):  
Bin Wang ◽  
Xiaolin Chang ◽  
Jiqiang Liu
Keyword(s):  
Data Centers ◽  
Virtual Machines

An Energy-Efficient Consolidation of Virtual Machines in Cloud Data Centers

2017 ◽  
Vol 26 (1) ◽  
pp. 113-128
Author(s):  
Gamal Eldin I. Selim ◽  
Mohamed A. El-Rashidy ◽  
Nawal A. El-Fishawy
Keyword(s):  
Energy Efficient ◽  
Data Centers ◽  
Virtual Machines ◽  
Cloud Data ◽  
Cloud Data Centers

scholarly journals Virtual Machine Placement via Bin Packing in Cloud Data Centers

Electronics ◽  
2018 ◽  
Vol 7 (12) ◽  
pp. 389 ◽  
Author(s):  
Aisha Fatima ◽  
Nadeem Javaid ◽  
Tanzeela Sultana ◽  
Waqar Hussain ◽  
Muhammad Bilal ◽  
...  
Keyword(s):  
Particle Swarm Optimization ◽  
Data Centers ◽  
Bin Packing ◽  
Virtual Machines ◽  
Particle Swarm ◽  
Vm Placement ◽  
Swarm Optimization ◽  
Cloud Data ◽  
Cloud Data Centers ◽  
Vm Consolidation

With the increasing size of cloud data centers, the number of users and virtual machines (VMs) increases rapidly. The requests of users are entertained by VMs residing on physical servers. The dramatic growth of internet services results in unbalanced network resources. Resource management is an important factor for the performance of a cloud. Various techniques are used to manage the resources of a cloud efficiently. VM-consolidation is an intelligent and efficient strategy to balance the load of cloud data centers. VM-placement is an important subproblem of the VM-consolidation problem that needs to be resolved. The basic objective of VM-placement is to minimize the utilization rate of physical machines (PMs). VM-placement is used to save energy and cost. An enhanced levy-based particle swarm optimization algorithm with variable sized bin packing (PSOLBP) is proposed for solving the VM-placement problem. Moreover, the best-fit strategy is also used with the variable sized bin packing problem (VSBPP). Simulations are done to authenticate the adaptivity of the proposed algorithm. Three algorithms are implemented in Matlab. The given algorithm is compared with simple particle swarm optimization (PSO) and a hybrid of levy flight and particle swarm optimization (LFPSO). The proposed algorithm efficiently minimized the number of running PMs. VM-consolidation is an NP-hard problem, however, the proposed algorithm outperformed the other two algorithms.

Multidimensional Resource Scheduling in Cloud Data Centers

2014 ◽  
Vol 1008-1009 ◽  
pp. 1513-1516
Author(s):  
Hai Na Song ◽  
Xiao Qing Zhang ◽  
Zhong Tang He
Keyword(s):  
Cloud Computing ◽  
Data Centers ◽  
Bin Packing ◽  
Virtual Machines ◽  
Three Dimensional ◽  
Resource Scheduling ◽  
Computing Environment ◽  
Cloud Computing Environment ◽  
Cloud Data ◽  
Cloud Data Centers

Cloud computing environment is regarded as a kind of multi-tenant computing mode. With virtulization as a support technology, cloud computing realizes the integration of multiple workloads in one server through the package and seperation of virtual machines. Aiming at the contradiction between the heterogeneous applications and uniform shared resource pool, using the idea of bin packing, the multidimensional resource scheduling problem is analyzed in this paper. We carry out some example analysis in one-dimensional resource scheduling, two-dimensional resource schduling and three-dimensional resource scheduling. The results shows that the resource utilization of cloud data centers will be improved greatly when the resource sheduling is conducted after reorganizing rationally the heterogeneous demands.

Effective Utilization of Resources Through Optimal Allocation and Opportunistic Migration of Virtual Machines in Cloud Environment

2021 ◽  
Vol 11 (3) ◽  
pp. 72-91
Author(s):  
Priyanka H. ◽  
Mary Cherian
Keyword(s):  
Data Center ◽  
Execution Time ◽  
Data Centers ◽  
Optimal Allocation ◽  
Virtual Machines ◽  
Large Data ◽  
Migration Time ◽  
Resource Usage ◽  
Total Execution Time ◽  
Effective Utilization

Cloud computing has become more prominent, and it is used in large data centers. Distribution of well-organized resources (bandwidth, CPU, and memory) is the major problem in the data centers. The genetically enhanced shuffling frog leaping algorithm (GESFLA) framework is proposed to select the optimal virtual machines to schedule the tasks and allocate them in physical machines (PMs). The proposed GESFLA-based resource allocation technique is useful in minimizing the wastage of resource usage and also minimizes the power consumption of the data center. The proposed GESFL algorithm is compared with task-based particle swarm optimization (TBPSO) for efficiency. The experimental results show the excellence of GESFLA over TBPSO in terms of resource usage ratio, migration time, and total execution time. The proposed GESFLA framework reduces the energy consumption of data center up to 79%, migration time by 67%, and CPU utilization is improved by 9% for Planet Lab workload traces. For the random workload, the execution time is minimized by 71%, transfer time is reduced up to 99%, and the CPU consumption is improved by 17% when compared to TBPSO.

Energy and SLA Efficient Virtual Machine Placement in Cloud Environment Using Non-Dominated Sorting Genetic Algorithm

2021 ◽  
pp. 773-789
Author(s):  
Oshin Sharma ◽  
Hemraj Saini
Keyword(s):  
Genetic Algorithm ◽  
Energy Consumption ◽  
Data Centers ◽  
Virtual Machines ◽  
Cloud Environment ◽  
Vm Placement ◽  
Energy Consumption Reduction ◽  
Consumption Reduction ◽  
The World ◽  
Placement Algorithm

To increase the availability of the resources and simultaneously to reduce the energy consumption of data centers by providing a good level of the service are one of the major challenges in the cloud environment. With the increasing data centers and their size around the world, the focus of the current research is to save the consumption of energy inside data centers. Thus, this article presents an energy-efficient VM placement algorithm for the mapping of virtual machines over physical machines. The idea of the mapping of virtual machines over physical machines is to lessen the count of physical machines used inside the data center. In the proposed algorithm, the problem of VM placement is formulated using a non-dominated sorting genetic algorithm based multi-objective optimization. The objectives are: optimization of the energy consumption, reduction of the level of SLA violation and the minimization of the migration count.

scholarly journals Dependency Analysis based Approach for Virtual Machine Placement in Software-Defined Data Center

2019 ◽  
Vol 9 (16) ◽  
pp. 3223
Author(s):  
Jargalsaikhan Narantuya ◽  
Taejin Ha ◽  
Jaewon Bae ◽  
Hyuk Lim
Keyword(s):  
Data Center ◽  
Data Centers ◽  
Virtual Machines ◽  
Principal Component ◽  
Service Performance ◽  
Data Traffic ◽  
Vm Placement ◽  
Matching Method ◽  
Placement Method ◽  
Underlying Network

In data centers, cloud-based services are usually deployed among multiple virtual machines (VMs), and these VMs have data traffic dependencies on each other. However, traffic dependency between VMs has not been fully considered when the services running in the data center are expanded by creating additional VMs. If highly dependent VMs are placed in different physical machines (PMs), the data traffic increases in the underlying physical network of the data center. To reduce the amount of data traffic in the underlying network and improve the service performance, we propose a traffic-dependency-based strategy for VM placement in software-defined data center (SDDC). The traffic dependencies between the VMs are analyzed by principal component analysis, and highly dependent VMs are grouped by gravity-based clustering. Each group of highly dependent VMs is placed within an appropriate PM based on the Hungarian matching method. This strategy of dependency-based VM placement facilitates reducing data traffic volume of the data center, since the highly dependent VMs are placed within the same PM. The results of the performance evaluation in SDDC testbed indicate that the proposed VM placement method efficiently reduces the amount of data traffic in the underlying network and improves the data center performance.

Bandwidth allocation for communicating virtual machines in cloud data centers

2020 ◽  
Vol 76 (9) ◽  
pp. 7268-7289
Author(s):  
Kamalesh Karmakar ◽  
Rajib K. Das ◽  
Sunirmal Khatua
Keyword(s):  
Bandwidth Allocation ◽  
Data Centers ◽  
Virtual Machines ◽  
Cloud Data ◽  
Cloud Data Centers

scholarly journals Reducing the Operational Cost of Cloud Data Centers through Renewable Energy

Algorithms ◽  
2018 ◽  
Vol 11 (10) ◽  
pp. 145 ◽  
Author(s):  
Demetrio Laganà ◽  
Carlo Mastroianni ◽  
Michela Meo ◽  
Daniela Renga
Keyword(s):  
Renewable Energy ◽  
Data Centers ◽  
Virtual Machines ◽  
Renewable Energy Sources ◽  
Management Strategies ◽  
Production Performance ◽  
Load Management ◽  
Cloud Data ◽  
Operational Costs ◽  
Allocation Process

The success of cloud computing services has led to big computing infrastructures that are complex to manage and very costly to operate. In particular, power supply dominates the operational costs of big infrastructures, and several solutions have to be put in place to alleviate these operational costs and make the whole infrastructure more sustainable. In this paper, we investigate the case of a complex infrastructure composed of data centers (DCs) located in different geographical areas in which renewable energy generators are installed, co-located with the data centers, to reduce the amount of energy that must be purchased by the power grid. Since renewable energy generators are intermittent, the load management strategies of the infrastructure have to be adapted to the intermittent nature of the sources. In particular, we consider EcoMultiCloud , a load management strategy already proposed in the literature for multi-objective load management strategies, and we adapt it to the presence of renewable energy sources. Hence, cost reduction is achieved in the load allocation process, when virtual machines (VMs) are assigned to a data center of the considered infrastructure, by considering both energy cost variations and the presence of renewable energy production. Performance is analyzed for a specific infrastructure composed of four data centers. Results show that, despite being intermittent and highly variable, renewable energy can be effectively exploited in geographical data centers when a smart load allocation strategy is implemented. In addition, the results confirm that EcoMultiCloud is very flexible and is suited to the considered scenario.

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%.

Energy-Efficient Virtual Machines Scheduling in Multi-Tenant Data Centers

2016 ◽  
Vol 4 (2) ◽  
pp. 210-221 ◽  
Author(s):  
Xiangming Dai ◽  
Jason Min Wang ◽  
Brahim Bensaou
Keyword(s):  
Energy Efficient ◽  
Data Centers ◽  
Virtual Machines
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