scholarly journals COMPARATIVE ANALYSIS OF VM PLACEMENT AND MIGRATION ALGORITHMS IN VM CONSOLIDATION

2021 ◽  
Vol 9 (1) ◽  
pp. 479-485
Author(s):  
Rashmi Sindhu, Vikas Siwach, Harkesh Sehrawat
Keyword(s):  
Comparative Analysis ◽  
Data Center ◽  
Data Centers ◽  
Vm Placement ◽  
Major Aspect ◽  
Iot Devices ◽  
Reduction Of Energy Consumption ◽  
And Migration ◽  
Cloud Infrastructures ◽  
Vm Consolidation

With the increasing number of Internet of Things (IoT) devices, data centers are experiencing immense augmentation in the hardware devices with an increase in the traffic to the cloud infrastructures. To handle this growth and to satisfy users demand, data centers require more energy. The IoT devices produce vast data which needs to be handled properly by the data centers which in turn is responsible for increase in the power consumption at the data centers Management and reduction of this energy is quite a challenging task for the managers and the designers of the data centers as increasing cost of data centers is posing a major hindrance.. One major aspect that needs to be taken into consideration is the sharing of the data center resources which is fundamentally achieved by the consolidation of the resources. The analysis done will conclude that consolidation plays an important role in the reduction of energy consumption of a data center.                     

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.

Minimization of Energy Using Heuristic Resource Allocation and Migration for Cloud Computing

2021 ◽  
Vol 12 (1) ◽  
pp. 74-83
Author(s):  
Manjunatha S. ◽  
Suresh L.
Keyword(s):  
Cloud Computing ◽  
Data Center ◽  
Large Scale ◽  
Data Centers ◽  
Service Providers ◽  
Cost Effective ◽  
Cooling Systems ◽  
Operational Costs ◽  
Cloud Computing Service ◽  
And Migration

Data center is a cost-effective infrastructure for storing large volumes of data and hosting large-scale service applications. Cloud computing service providers are rapidly deploying data centers across the world with a huge number of servers and switches. These data centers consume significant amounts of energy, contributing to high operational costs. Thus, optimizing the energy consumption of servers and networks in data centers can reduce operational costs. In a data center, power consumption is mainly due to servers, networking devices, and cooling systems, and an effective energy-saving strategy is to consolidate the computation and communication into a smaller number of servers and network devices and then power off as many unneeded servers and network devices as possible.

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.

scholarly journals CPU-Memory Aware VM Consolidation for Cloud Data Centers

2020 ◽  
Vol 21 (2) ◽  
pp. 159-172
Author(s):  
Nithiya Baskaran ◽  
Eswari R
Keyword(s):  
Energy Consumption ◽  
Data Centers ◽  
Vm Placement ◽  
Cloud Data ◽  
Cloud Data Centers ◽  
Placement Algorithm ◽  
Violation Rate ◽  
Vm Consolidation ◽  
Memory Utilization ◽  
Better Than

The unbalanced usage of resources in cloud data centers cause an enormous amount of power consumption. The Virtual Machine (VM) consolidation shuts the underutilized hosts and makes the overloaded hosts as normally loaded hosts by selecting appropriate VMs from the hosts and migrates them to other hosts in such a way to reduce the energy consumption and to improve physical resource utilization. Efficient method is needed for VM selection and destination hosts selection (VM placement). In this paper, a CPU-Memory aware VM placement algorithm is proposed for selecting suitable destination host for migration. The VMs are selected using Fuzzy Soft Set (FSS) method VM selection algorithm. The proposed placement algorithm considers both CPU, Memory, and combination of CPU-Memory utilization of VMs on the source host. The proposed method is experimentally compared with several existing selection and placement algorithms and the results show that the proposed consolidation method performs better than existing algorithms in terms of energy efficiency, energy consumption, SLA violation rate, and number of VM migrations.

scholarly journals Host Overloading Detection pada Dynamic VM Consolidation Menggunakan Fuzzy Mamdani

2018 ◽  
Vol 4 (2) ◽  
pp. 94
Author(s):  
Chaerul Umam ◽  
Guruh Fajar Shidik
Keyword(s):  
Cloud Computing ◽  
Fuzzy Logic ◽  
Data Center ◽  
Vm Placement ◽  
Vm Consolidation

Perkembangan Cloud Computing telah mengakibatkan pembangunan data center skala besar di seluruh dunia yang berisi ribuan node. Data Center Cloud mengkonsumsi energi listrik yang besar yang tentunya mengakibatkan biaya operasi yang tinggi. Konsumsi energi di Data Center akan terus tumbuh pesat kecuali dengan mengembangkan dan menerapkan manajemen resource yang hemat energi. Dynamic VM consolidation bisa menjadi strategi efektif untuk mengatasi masalah pemborosan energi pada data center cloud. Strategi ini dapat diuraikan ke dalam empat tugas pengambilan keputusan, yaitu Host overloading detection (memutuskan kapan host harus dianggap sebagai kelebihan beban), Host underloading detection (memutuskan kapan host harus dianggap underloaded / kekurangan beban), VM selection (memutuskan VMs mana yang harus pindah dari host yang kelebihan beban), dan VM placement (memutuskan tentang host mana yang harus dipilih untuk menerima migrasi VM). Penelitian ini mengusulkan metode fuzzy logic dalam proses host overloading detection. Dataset untuk menguji metode menggunakan data workload dari PlanetLab. Hasil dari pengujian metode yang diusulkan menunjukkan hasil yang menjanjikan dengan peningkatan efisiensi energi 2,24%.

scholarly journals A survey on Approaches Used for Efficient Workload Management in Geo-Distributed Data Centres

2018 ◽  
Vol 7 (3.34) ◽  
pp. 141
Author(s):  
D Ramya ◽  
J Deepa ◽  
P N.Karthikayan
Keyword(s):  
Operating Systems ◽  
Data Center ◽  
Data Centers ◽  
Distributed Data ◽  
Cloud Infrastructure ◽  
Workload Management ◽  
Software Applications ◽  
Geographically Distributed ◽  
Cloud Infrastructures ◽  
Data Centres

A geographically distributed Data center assures Globalization of data and also security for the organizations. The principles for Disaster recovery is also taken into consideration. The above aspects drive business opportunities to companies that own many sites and Cloud Infrastructures with multiple owners.  The data centers store very critical and confidential documents that multiple organizations share in the cloud infrastructure. Previously different servers with different Operating systems and software applications were used. As it was difficult to maintain, Servers are consolidated which allows sharing of resources at low of cost maintenance [7]. The availability of documents should be increased and down time should be reduced. Thus workload management becomes a challenging among the data centers distributed geographically. In this paper we focus on different approaches used for workload management in Geo-distributed data centers. The algorithms used and also the challenges involved in different approaches are discussed 

Network-Aware Virtual Machine Placement and Migration in Cloud Data Centers

2015 ◽  
pp. 42-91 ◽  
Author(s):  
Md Hasanul Ferdaus ◽  
Manzur Murshed ◽  
Rodrigo N. Calheiros ◽  
Rajkumar Buyya
Keyword(s):  
Virtual Machine ◽  
Large Scale ◽  
Data Centers ◽  
Background Information ◽  
Future Research ◽  
Vm Placement ◽  
Network Resource ◽  
Cloud Data ◽  
Large Scale Data ◽  
And Migration

With the pragmatic realization of computing as a utility, Cloud Computing has recently emerged as a highly successful alternative IT paradigm. Cloud providers are deploying large-scale data centers across the globe to meet the Cloud customers' compute, storage, and network resource demands. Efficiency and scalability of these data centers, as well as the performance of the hosted applications' highly depend on the allocations of the data center resources. Very recently, network-aware Virtual Machine (VM) placement and migration is developing as a very promising technique for the optimization of compute-network resource utilization, energy consumption, and network traffic minimization. This chapter presents the relevant background information and a detailed taxonomy that characterizes and classifies the various components of VM placement and migration techniques, as well as an elaborate survey and comparative analysis of the state of the art techniques. Besides highlighting the various aspects and insights of the network-aware VM placement and migration strategies and algorithms proposed by the research community, the survey further identifies the benefits and limitations of the existing techniques and discusses on the future research directions.

Data Center Equipment Reliability Concerns—Contamination Issues, Standards Actions, and Case Studies

2013 ◽  
Author(s):  
Chris Muller ◽  
Chuck Arent ◽  
Henry Yu
Keyword(s):  
Case Studies ◽  
Data Center ◽  
Data Centers ◽  
Electronic Equipment ◽  
Circuit Board ◽  
Lead Free ◽  
Reliable Operation ◽  
Contamination Assessment ◽  
Equipment Reliability ◽  
Equipment Failures

Abstract Lead-free manufacturing regulations, reduction in circuit board feature sizes and the miniaturization of components to improve hardware performance have combined to make data center IT equipment more prone to attack by corrosive contaminants. Manufacturers are under pressure to control contamination in the data center environment and maintaining acceptable limits is now critical to the continued reliable operation of datacom and IT equipment. This paper will discuss ongoing reliability issues with electronic equipment in data centers and will present updates on ongoing contamination concerns, standards activities, and case studies from several different locations illustrating the successful application of contamination assessment, control, and monitoring programs to eliminate electronic equipment failures.

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