Improving Cost for Data Migration in Cloud Computing Using Genetic Algorithm

2020 ◽  
Vol 8 (3) ◽  
pp. 69-81
Author(s):  
Nitin Chawla ◽  
Deepak Kumar ◽  
Dinesh Kumar Sharma
Keyword(s):  
Genetic Algorithm ◽  
Cloud Computing ◽  
Data Center ◽  
Data Centers ◽  
Software As A Service ◽  
Data Migration ◽  
Platform As A Service ◽  
Challenging Tasks ◽  
Enterprise Applications ◽  
Client Data

Cloud computing is gradually increasing its popularity in enterprise-wide organizations. Information technology organizations e.g., IBM, Microsoft, and Amazon have already shifted towards Cloud computing. Cloud-based offerings such as Software as a Service, Platform as a Service and Infrastructure as a Service (IAAS) are the most famous offerings. Most of the existing enterprise applications are deployed using an on-premise model. Organizations are looking for Cloud based offerings to deploy or upgrade their existing applications. SAP, Microsoft Dynamics, and Oracle are the most famous ERP or CRM application OEMs. These enterprise applications generate lots of data are hosted in an organization or on client data centers. Moving data from one data center to the Cloud is always a challenging tasks which cost a lot and takes much effort. This study proposes an efficient approach to optimize cost for data migration in cloud computing. This study also proposes the approach to optimize cost for data collection from multiple locations which can be processed centrally and then migrate to Cloud Computing.

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.

Energy-Efficiency in Cloud Data Centers

2013 ◽  
pp. 241-263
Author(s):  
Burak Kantarci ◽  
Hussein T. Mouftah
Keyword(s):  
Cloud Computing ◽  
Energy Consumption ◽  
Data Center ◽  
Energy Efficient ◽  
Large Scale ◽  
Data Centers ◽  
High Energy ◽  
It Services ◽  
Data Center Network ◽  
Cloud Data

Cloud computing aims to migrate IT services to distant data centers in order to reduce the dependency of the services on the limited local resources. Cloud computing provides access to distant computing resources via Web services while the end user is not aware of how the IT infrastructure is managed. Besides the novelties and advantages of cloud computing, deployment of a large number of servers and data centers introduces the challenge of high energy consumption. Additionally, transportation of IT services over the Internet backbone accumulates the energy consumption problem of the backbone infrastructure. In this chapter, the authors cover energy-efficient cloud computing studies in the data center involving various aspects such as: reduction of processing, storage, and data center network-related power consumption. They first provide a brief overview of the existing approaches on cool data centers that can be mainly grouped as studies on virtualization techniques, energy-efficient data center network design schemes, and studies that monitor the data center thermal activity by Wireless Sensor Networks (WSNs). The authors also present solutions that aim to reduce energy consumption in data centers by considering the communications aspects over the backbone of large-scale cloud systems.

Fault Tolerant Cloud Systems

2019 ◽  
pp. 171-190
Author(s):  
Sathish Kumar ◽  
Balamurugan B
Keyword(s):  
Cloud Computing ◽  
Fault Tolerant ◽  
Software As A Service ◽  
Arrival Process ◽  
Cloud Provider ◽  
Infrastructure As A Service ◽  
Batch Mode ◽  
Web Based ◽  
Platform As A Service ◽  
Cloud Systems

Cloud computing refers to a model for accessing computing resource like networks, servers, storage, applications, and services remotely. Cloud computing offers these resources as a service, namely infrastructure-as-a-service, platform-as-a-service, and software-as-a-service. To use these services, two roles involved: the cloud provider offers the service and the cloud customer consumes the service. These resources are efficiently shared and utilized by customers and it is called workload. The requirement of workload depends on customer demands that vary from higher to lower. Based on the customer demand, cloud provider makes the resource available efficiently. In the context of cloud, the workload is based on web-based service or jobs processed in batch mode. The arrival process of jobs in the cloud is not often deterministic. The irregular increase or decrease in workload has a vital impact on resource provision. Monitoring the resources helps in measuring the performance of the cloud so that the resource can be provisioned to customers efficiently.

Mobile Cloud Gaming and Today's World

2018 ◽  
pp. 282-295
Author(s):  
Hallah Shahid Butt ◽  
Sadaf Jalil ◽  
Sajid Umair ◽  
Safdar Abbas Khan
Keyword(s):  
Cloud Computing ◽  
Mobile Cloud Computing ◽  
Research Work ◽  
Cloud Services ◽  
Software As A Service ◽  
Mobile Cloud ◽  
Infrastructure As A Service ◽  
Platform As A Service ◽  
Research Areas ◽  
Generic Architecture

Mobile cloud computing is the emerging field. Along-with different services being provided by the cloud like Platform as a Service, Infrastructure as a Service, Software as a Service; Game as a Service is new terminology for the cloud services. In this paper, we generally discussed the concept of mobile cloud gaming, the companies that provide the services as GaaS, the generic architecture, and the research work that has been done in this field. Furthermore, we highlighted the research areas in this field.

Analysis and Research of Cloud Computing Data Center

2013 ◽  
Vol 427-429 ◽  
pp. 2184-2187
Author(s):  
Le Jiang Guo ◽  
Feng Zheng ◽  
Ya Hui Hu ◽  
Lei Xiao ◽  
Liang Liu
Keyword(s):  
Cloud Computing ◽  
Data Center ◽  
Data Centers ◽  
Computing Technologies ◽  
Data Center Management ◽  
Computing Centers ◽  
The Relationship

Cloud computing data centers can be called cloud computing centers. It has put forward newer and higher demands for data centers with the development of cloud computing technologies. This paper will discuss what are cloud computing data centers, cloud computing data center construction, cloud computing data center architecture, cloud computing data center management and maintenance, and the relationship between cloud computing data centers and clouds.

scholarly journals Performance Evaluation of SUMEGHA Cloud Computing Environment: Methodologies & Tool

2019 ◽  
Vol 8 (12) ◽  
pp. 875-880
Keyword(s):  
Cloud Computing ◽  
Performance Evaluation ◽  
Software As A Service ◽  
Automated Testing ◽  
Cloud Environment ◽  
Computing Environment ◽  
Cloud Computing Environment ◽  
Platform As A Service ◽  
Testing Tools ◽  
Manual Testing

Cloud ecosystem basically offers Platform as a Service (PaaS), Infrastructure as a Service (IaaS) and Software as a Service (SaaS). This paper describes the testing process employed for testing the C-DAC cloud SuMegha. Though new tools for the testing cloud are emerging into the market, there are aspects which are suited for manual testing and some which can be speeded up using automated testing tools. This paper brings out the techniques best suited to test different features of Cloud computing environment. It offers a comparison of several tools which enhance the testing process at each level. The authors also try to bring out (recommend) broad guidelines to follow while setting up a cloud environment to reduce the number of bugs in the system

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.

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