scholarly journals EeadSelfCloud: Energy Efficient Adaptive Depth Self Cloud Mechanism for VM Migration in Data Centers

2019 ◽  
Vol 8 (10) ◽  
pp. 2236-2244
Keyword(s):  
Cloud Computing ◽  
Virtual Machine ◽  
Data Center ◽  
Data Centers ◽  
Virtual Machines ◽  
Power Saving ◽  
Human Beings ◽  
Hop Count ◽  
Vm Migration ◽  
Self Organizing

Cloud computing, with its great potential in low cost and demanding services, is a good computing platform. Modern data centers for cloud computing are facing the difficulty of consistently increasing complexity because of the expanding quantity of clients and their enlarging resource demands. A great deal of efforts are currently focused on giving the cloud framework with autonomic behavior , so it can take decision about virtual machine (VM) management over the datacenter without intervention of human beings. Most of the self-organizing solutions results in eager migration, which attempts to diminish the amount of working servers virtual machines. These self-organizing resolution produce needless migration due to unpredictable workload. So also it consume huge amounts of electrical energy during unnecessary migration process. To overcome this issue, this project develop one novel VM migration scheme called eeadSelfCloud. The proposed schema is used to change the virtual machine in a cloud center that requires a lot of factors, such as basic requirements for resources during virtual machine setup, dynamic resource allocation, top software loading, software execution, and power saving at the Data Center. Data Center Utilization, Average Node Utilization, Request Rejection Ration, Number of Hop Count and Power Consumption are taken as constraint for measuring the proposed approach. The analysis report depicted that the proposed approach performs best than the other existing approaches.

Data Center Enhancement by Server Resources Utilization

2021 ◽  
Vol 39 (1B) ◽  
pp. 203-208
Author(s):  
Haider A. Ghanem ◽  
Rana F. Ghani ◽  
Maha J. Abbas
Keyword(s):  
Cloud Computing ◽  
Power Consumption ◽  
Resource Utilization ◽  
Virtual Machine ◽  
Data Center ◽  
Data Centers ◽  
Virtual Machines ◽  
The Internet ◽  
Memory Resources

Data centers are the main nerve of the Internet because of its hosting, storage, cloud computing and other services. All these services require a lot of work and resources, such as energy and cooling. The main problem is how to improve the work of data centers through increased resource utilization by using virtual host simulations and exploiting all server resources. In this paper, we have considered memory resources, where Virtual machines were distributed to hosts after comparing the virtual machines with the host from where the memory and putting the virtual machine on the appropriate host, this will reduce the host machines in the data centers and this will improve the performance of the data centers, in terms of power consumption and the number of servers used and cost.

scholarly journals Eeadoselfcloud: Energy Efficient Adaptive Depth Optimized Self Cloud Mechanism for VM Migration in Data Centers

2019 ◽  
Vol 8 (6) ◽  
pp. 475-482
Keyword(s):  
Power Consumption ◽  
Data Center ◽  
Data Centers ◽  
Virtual Machines ◽  
Algorithm Analysis ◽  
Virtual Machine Migration ◽  
Hop Count ◽  
Vm Migration ◽  
Bee Colony ◽  
Data Centres

Cloud computing is a paradigm where all resources like software, hardware and information are accessed over internet by using highly sophisticated virtual data centres. The cloud has a data center with a host of many features. Each machine is shared by many users, and virtual machines are used to use these machines. With a large number of data centers and data centers with a large number of physical hosts. Two important issues in cloud environment are Load balancing and power consumption which solved by virtual machine migration. In earlier learnings, Artificial Bee Colony (ABC)'s policy could lead to a compromise between productivity and energy consumption. There are, however, two ways in the ABC-based Abstract based approach: (1) How to find effective solutions across the globe. (2) how to reduce the time to decide to distribute BM.To overcome this issue, this project develop one novel VM migration scheme called eeadoSelfCloud. This proposed method introduces Bee Lion Optimization (BLO) for VM allocation. Data Center Utilization, Average Node Utilization, Request Rejection Ration, Number of Hop Count and Power Consumption are employed as parameters for the proposed algorithm analysis. The experimental results indicate that the proposed algorithm does better than the other available methods.

scholarly journals Power consumption prediction in cloud data center using machine learning

2020 ◽  
Vol 10 (2) ◽  
pp. 1524 ◽  
Author(s):  
Deepika T. ◽  
Prakash P.
Keyword(s):  
Machine Learning ◽  
Cloud Computing ◽  
Power Consumption ◽  
Data Center ◽  
Data Centers ◽  
Virtual Machines ◽  
Service Providers ◽  
Business World ◽  
Cloud Data ◽  
Cloud Data Centers

The flourishing development of the cloud computing paradigm provides several services in the industrial business world. Power consumption by cloud data centers is one of the crucial issues for service providers in the domain of cloud computing. Pursuant to the rapid technology enhancements in cloud environments and data centers augmentations, power utilization in data centers is expected to grow unabated. A diverse set of numerous connected devices, engaged with the ubiquitous cloud, results in unprecedented power utilization by the data centers, accompanied by increased carbon footprints. Nearly a million physical machines (PM) are running all over the data centers, along with (5 – 6) million virtual machines (VM). In the next five years, the power needs of this domain are expected to spiral up to 5% of global power production. The virtual machine power consumption reduction impacts the diminishing of the PM’s power, however further changing in power consumption of data center year by year, to aid the cloud vendors using prediction methods. The sudden fluctuation in power utilization will cause power outage in the cloud data centers. This paper aims to forecast the VM power consumption with the help of regressive predictive analysis, one of the Machine Learning (ML) techniques. The potency of this approach to make better predictions of future value, using Multi-layer Perceptron (MLP) regressor which provides 91% of accuracy during the prediction process.

scholarly journals Proposing a load balancing algorithm with an integrative approach to reduce response time and service process time in data centers

2019 ◽  
Vol 16 (4) ◽  
pp. 627-637
Author(s):  
Sanaz Hosseinzadeh Sabeti ◽  
Maryam Mollabgher
Keyword(s):  
Load Balancing ◽  
Data Processing ◽  
Response Time ◽  
Virtual Machine ◽  
Data Center ◽  
Processing Time ◽  
Data Centers ◽  
Virtual Machines ◽  
Overall Response ◽  
Load Balancing Algorithm

Goal: Load balancing policies often map workloads on virtual machines, and are being sought to achieve their goals by creating an almost equal level of workload on any virtual machine. In this research, a hybrid load balancing algorithm is proposed with the aim of reducing response time and processing time. Design / Methodology / Approach: The proposed algorithm performs load balancing using a table including the status indicators of virtual machines and the task list allocated to each virtual machine. The evaluation results of response time and processing time in data centers from four algorithms, ESCE, Throttled, Round Robin and the proposed algorithm is done. Results: The overall response time and data processing time in the proposed algorithm data center are shorter than other algorithms and improve the response time and data processing time in the data center. The results of the overall response time for all algorithms show that the response time of the proposed algorithm is 12.28%, compared to the Round Robin algorithm, 9.1% compared to the Throttled algorithm, and 4.86% of the ESCE algorithm. Limitations of the investigation: Due to time and technical limitations, load balancing has not been achieved with more goals, such as lowering costs and increasing productivity. Practical implications: The implementation of a hybrid load factor policy can improve the response time and processing time. The use of load balancing will cause the traffic load between virtual machines to be properly distributed and prevent bottlenecks. This will be effective in increasing customer responsiveness. And finally, improving response time increases the satisfaction of cloud users and increases the productivity of computing resources. Originality/Value: This research can be effective in optimizing the existing algorithms and will take a step towards further research in this regard.

scholarly journals A Decentralized Virtual Machine Migration Approach of Data Centers for Cloud Computing

2013 ◽  
Vol 2013 ◽  
pp. 1-10 ◽  
Author(s):  
Xiaoying Wang ◽  
Xiaojing Liu ◽  
Lihua Fan ◽  
Xuhan Jia
Keyword(s):  
Cloud Computing ◽  
Virtual Machine ◽  
Large Scale ◽  
Data Centers ◽  
Virtual Machines ◽  
Quality Of Services ◽  
Virtual Machine Migration ◽  
Power Models ◽  
Key Steps

As cloud computing offers services to lots of users worldwide, pervasive applications from customers are hosted by large-scale data centers. Upon such platforms, virtualization technology is employed to multiplex the underlying physical resources. Since the incoming loads of different application vary significantly, it is important and critical to manage the placement and resource allocation schemes of the virtual machines (VMs) in order to guarantee the quality of services. In this paper, we propose a decentralized virtual machine migration approach inside the data centers for cloud computing environments. The system models and power models are defined and described first. Then, we present the key steps of the decentralized mechanism, including the establishment of load vectors, load information collection, VM selection, and destination determination. A two-threshold decentralized migration algorithm is implemented to further save the energy consumption as well as keeping the quality of services. By examining the effect of our approach by performance evaluation experiments, the thresholds and other factors are analyzed and discussed. The results illustrate that the proposed approach can efficiently balance the loads across different physical nodes and also can lead to less power consumption of the entire system holistically.

scholarly journals A Location Selection Policy of Live Virtual Machine Migration for Power Saving and Load Balancing

2013 ◽  
Vol 2013 ◽  
pp. 1-16 ◽  
Author(s):  
Jia Zhao ◽  
Yan Ding ◽  
Gaochao Xu ◽  
Liang Hu ◽  
Yushuang Dong ◽  
...  
Keyword(s):  
Cloud Computing ◽  
Load Balancing ◽  
Virtual Machine ◽  
Power Saving ◽  
Genetic Operators ◽  
Virtual Machine Migration ◽  
Cloud Data Center ◽  
Vm Migration ◽  
Location Selection ◽  
Cloud Data

Green cloud data center has become a research hotspot of virtualized cloud computing architecture. And load balancing has also been one of the most important goals in cloud data centers. Since live virtual machine (VM) migration technology is widely used and studied in cloud computing, we have focused on location selection (migration policy) of live VM migration for power saving and load balancing. We propose a novel approach MOGA-LS, which is a heuristic and self-adaptive multiobjective optimization algorithm based on the improved genetic algorithm (GA). This paper has presented the specific design and implementation of MOGA-LS such as the design of the genetic operators, fitness values, and elitism. We have introduced the Pareto dominance theory and the simulated annealing (SA) idea into MOGA-LS and have presented the specific process to get the final solution, and thus, the whole approach achieves a long-term efficient optimization for power saving and load balancing. The experimental results demonstrate that MOGA-LS evidently reduces the total incremental power consumption and better protects the performance of VM migration and achieves the balancing of system load compared with the existing research. It makes the result of live VM migration more high-effective and meaningful.

scholarly journals Randomized routing of virtual machines in IaaS data centers

2019 ◽  
Vol 5 ◽  
pp. e211
Author(s):  
Hadi Khani ◽  
Hamed Khanmirza
Keyword(s):  
Cloud Computing ◽  
Quality Of Service ◽  
Power Consumption ◽  
Analytical Model ◽  
Data Center ◽  
Large Scale ◽  
Data Centers ◽  
Virtual Machines ◽  
General Distribution

Cloud computing technology has been a game changer in recent years. Cloud computing providers promise cost-effective and on-demand resource computing for their users. Cloud computing providers are running the workloads of users as virtual machines (VMs) in a large-scale data center consisting a few thousands physical servers. Cloud data centers face highly dynamic workloads varying over time and many short tasks that demand quick resource management decisions. These data centers are large scale and the behavior of workload is unpredictable. The incoming VM must be assigned onto the proper physical machine (PM) in order to keep a balance between power consumption and quality of service. The scale and agility of cloud computing data centers are unprecedented so the previous approaches are fruitless. We suggest an analytical model for cloud computing data centers when the number of PMs in the data center is large. In particular, we focus on the assignment of VM onto PMs regardless of their current load. For exponential VM arrival with general distribution sojourn time, the mean power consumption is calculated. Then, we show the minimum power consumption under quality of service constraint will be achieved with randomize assignment of incoming VMs onto PMs. Extensive simulation supports the validity of our analytical model.

scholarly journals A Novel Artificial Bee Colony Approach of Live Virtual Machine Migration Policy Using Bayes Theorem

2013 ◽  
Vol 2013 ◽  
pp. 1-13 ◽  
Author(s):  
Gaochao Xu ◽  
Yan Ding ◽  
Jia Zhao ◽  
Liang Hu ◽  
Xiaodong Fu
Keyword(s):  
Cloud Computing ◽  
Virtual Machine ◽  
Artificial Bee Colony ◽  
Power Saving ◽  
Bayes Theorem ◽  
Virtual Machine Migration ◽  
Cloud Data Center ◽  
Vm Migration ◽  
Cloud Data ◽  
Bee Colony

Green cloud data center has become a research hotspot of virtualized cloud computing architecture. Since live virtual machine (VM) migration technology is widely used and studied in cloud computing, we have focused on the VM placement selection of live migration for power saving. We present a novel heuristic approach which is called PS-ABC. Its algorithm includes two parts. One is that it combines the artificial bee colony (ABC) idea with the uniform random initialization idea, the binary search idea, and Boltzmann selection policy to achieve an improved ABC-based approach with better global exploration’s ability and local exploitation’s ability. The other one is that it uses the Bayes theorem to further optimize the improved ABC-based process to faster get the final optimal solution. As a result, the whole approach achieves a longer-term efficient optimization for power saving. The experimental results demonstrate that PS-ABC evidently reduces the total incremental power consumption and better protects the performance of VM running and migrating compared with the existing research. It makes the result of live VM migration more high-effective and meaningful.

scholarly journals Efficient Virtual Machine Memory Transfer in Datacenter with optimal downtime

2019 ◽  
Author(s):  
Girish L
Keyword(s):  
Cloud Computing ◽  
Virtual Machine ◽  
Virtual Machines ◽  
Flow Chart ◽  
Key Factors ◽  
Virtual Machine Migration ◽  
Memory Transfer ◽  
Vm Migration ◽  
Host Machine ◽  
Machine Memory

Cloud computing is a technology which relies onsharing various computing resources instead of having localservers to handle applications. Cloud computing is driven byvirtualization technology. Virtual machines need migration fromone host to anther due to the presence of error or over loading orslowness in the current running host machine. Live Virtualmachine migration is the transfer of running virtual machinefrom one host to another without stopping the current runningtask. During this live virtual machine migration Downtime is oneof the key factors that have to be considered and assessed.Here we present detailed survey on what are the importance oflive virtual machine migration in cloud computing technologyand various techniques to reduce the downtime during livevirtual machine migration. The flow chart showing the steps usedin Pre copy approach for VM migration. And also we presentthe result of the comparison between the two virtual machinemigration environments, VMWare and Xen Server.

scholarly journals Strategi Efisiensi Energi dan Penyeimbangan Beban Kerja Layanan Cloud Computing Melalui Konsolidasi Mesin Virtual Dinamis

2020 ◽  
Vol 3 (1) ◽  
pp. 1-12
Author(s):  
Abdullah Fadil
Keyword(s):  
Cloud Computing ◽  
Virtual Machine ◽  
Data Center ◽  
Vm Migration ◽  
Physical Machine

Arsitektur data center di dalam cloud computing merupakan lingkungan yang heterogen dan terdistribusi, tersusun atas gugusan jaringan physical machine (PM) atau server dengan berbagai kapasitas sumber daya komputasi yang berbeda-beda di dalam PMnya. Kondisi permintaan (demand) dan ketersediaan (supply) pada layanan cloud yang fluktuatif tersebut membuat data center cloud harus dibuat elastis. Virtual Machine (VM) merupakan representasi dari ketersediaan sumber daya komputasi dinamis yang dapat dialokasikan dan direlokasikan sesuai dengan permintaan. VM yang berada di dalam data center cloud dapat dipindahkan dari satu PM ke PM lainnya menggunakan migrasi VM secara langsung (live VM migration) ataupun tidak langsung (off-line VM migration). lingkungan cloud computing yang dinamis dan terdistribusi mengharuskan strategi pengambilan keputusan di dalam konsolidasi VM harus dibuat sedinamis mungkin atau bahkan adaptif dengan mempertimbangkan heterogenitas sumber daya virtual sesuai dengan layanan cloud computing yang disajikan. Sehingga, dalam penelitian ini diusulkan efisiensi energi sekaligus menjaga kinerja layanan cloud computing melalui penyeimbangan beban kerja dengan teknik migrasi VM yang terdapat pada prosedur konsolidasi VM dinamis. Strategi pengambilan keputusan pada prosedur konsolidasi virtual machine dinamis yang diusulkan, dapat meningkatkan kinerja layanan cloud computing sekaligus beban kerja physical machine menjadi seimbang karena keputusan pemilihan VM dan penempatan VM pada physical machine dipilih secara optimal melalui MADM. Konsumsi energi dari physical machine juga dapat di hemat dengan mematikannya karena statusnya idle.

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