scholarly journals Improving security for data migration in cloud computing using randomized encryption technique

2013 ◽  
Vol 11 (6) ◽  
pp. 39-42 ◽  
Author(s):  
Rashmi Rao Rashmi Rao
Keyword(s):  
Cloud Computing ◽  
Data Migration ◽  
Randomized Encryption

scholarly journals A Secure and Efficient Data Migration Over Cloud Computing

2021 ◽  
Vol 1099 (1) ◽  
pp. 012082
Author(s):  
G. Madhukar Rao ◽  
K. Srinivas ◽  
Sayyad Samee ◽  
K Venkatesh ◽  
Pankaj Dadheech ◽  
...  
Keyword(s):  
Cloud Computing ◽  
Data Migration ◽  
Efficient Data

scholarly journals Opportunities and Challenges of Data Migration in Cloud

2021 ◽  
Vol 9 (1) ◽  
pp. 41-50
Author(s):  
Ruhul Amin ◽  
Siddhartha Vadlamudi
Keyword(s):  
Cloud Computing ◽  
Impact Analysis ◽  
Academic Research ◽  
Security Requirements ◽  
Data Migration ◽  
Cloud Provider ◽  
Computing Environment ◽  
Cloud Computing Environment ◽  
Cloud Data ◽  
Cloud Migration

Cloud data migration is the process of moving data, localhost applications, services, and data to the distributed cloud processing framework. The success of this data migration measure is relying upon a few viewpoints like planning and impact analysis of existing enterprise systems. Quite possibly the most widely recognized process is moving locally stored data in a public cloud computing environment. Cloud migration comes along with both challenges and advantages, so there are different academic research and technical applications on data migration to the cloud that will be discussed throughout this paper. By breaking down the research achievement and application status, we divide the existing migration techniques into three strategies as indicated by the cloud service models essentially. Various processes should be considered for different migration techniques, and various tasks will be included accordingly. The similarities and differences between the migration strategies are examined, and the challenges and future work about data migration to the cloud are proposed. This paper, through a research survey, recognizes the key benefits and challenges of migrating data into the cloud. There are different cloud migration procedures and models recommended to assess the presentation, identifying security requirements, choosing a cloud provider, calculating the expense, and making any essential organizational changes. The results of this research paper can give a roadmap for data migration and can help decision-makers towards a secure and productive migration to a cloud computing environment.

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.

Data migration in cloud computing using honey encryption

2018 ◽  
Vol 7 (2.8) ◽  
pp. 230
Author(s):  
K Ravindranadh ◽  
Mallarapu Sai Kiran ◽  
B Durga Sai Pavan Kumar ◽  
D Priyanka
Keyword(s):  
Cloud Computing ◽  
Load Balancing ◽  
Privacy Protection ◽  
Security And Privacy ◽  
Data Migration ◽  
Data Confidentiality

Cloudcomputing provides variouskindsof servicesfor storingdata, load balancing betweencloudsandprovides an infrastructure for developing applicationsandmanaging them.Duetovariousattractive services provided by various cloudservice providers, users migrating their data from  their storage systemtocloudserviceprovider.Whilemigratingdatatoa cloudservice provider there will be  security  and privacy  protection concerns arises.Byconsideringthese concerns we are  proposing a secure privacy protection  migration usinghoney-encryptioncryptographicalgorithmfordata whichis outsourceddatatocloudandweareusingmigrationprotocolwhile migratingdatafromexisting serverstoragesystemtocloud server  storage systemwhichensures dataintegrityand data confidentiality.

Data Control in Public Cloud Computing: Issues and Challenges

2019 ◽  
Vol 12 ◽  
Author(s):  
Pranay Jha ◽  
Sartaj Singh ◽  
Ashok Sharma
Keyword(s):  
Cloud Computing ◽  
Data Security ◽  
Business Models ◽  
Data Migration ◽  
Security Mechanism ◽  
Public Cloud ◽  
It Infrastructure ◽  
Cloud Models ◽  
Data Control ◽  
In Transit

Background: : The Advancement in the Hardware and Progress in IoT based devices has led to significant transformation in digitalization and globalization of business models in the IT World. In fact Cloud Computing has attracted many companies to expand their business by providing IT infrastructure with very less budget in pay per use model. The Expansion and Migration of Companies to Cloud Computing facilities has really brought many pros and cons and opened new area of Research. The Management of IT infrastructure as per business requirement is a great challenge for the IT Infrastructure managers because of complex business models which needs to be updated with market trends and it requires huge and updated infrastructure to accelerate their business requirements. No doubt there are many benefits of moving to Cloud but several vulnerabilities and potential threats related to security is a major concern for any business sensitive data. These security challenges place restrictions on moving on-premises workloads to the Cloud. This paper has discussed key differences in cloud models and existing various Cloud Security Architectures and challenges in cloud computing related to Data Security at Rest and in Transit. Also data controlling mechanism need to be adopted by IT Industry along with end to end security mechanism has been explained. Objective:: The main objective of this paper is to discuss about the prevailing issues in cloud in terms of data security which is discouraging the Industry and organizations to move their data into public cloud and also to discuss how to enhance security mechanism in cloud during data migration and multitenant environment. Methods:: Based on different reports and analysis, it has been pointed that data breach and data security are most challenging and concerning factor for any customer when someone think to migrate the workloads from On-Premises datacenter to Cloud Computing. It needs more attention in every consideration. All criteria and considerations to secure and protect the customer’s information and data have been classified and discussed. Data-at-rest and Data-in-transit are transmission method for storing and moving the data from one source to destination. Different encryption methods for protecting and security data-at-rest and data-in-transit have been identity. However, there are still more areas need to work in for filling gaps for Cloud data control and security which is still a serious concern and on top of attackers every day. Conclusion:: Since, cyber-attacks occurring very frequently and causing a huge amount of investment on re-establishing the environment, it needs more control with effective usages of technology. All those concerns related to security are very reasonable concerns that needs to be addressed.

Cloud technology in healthcare: a qualitative study exploring key challenges and opportunities (Preprint)

2021 ◽  
Author(s):  
Kathrin Cresswell ◽  
Andrés Domínguez Hernández ◽  
Robin Williams ◽  
Aziz Sheikh
Keyword(s):  
Cloud Computing ◽  
Data Driven ◽  
Data Migration ◽  
User Engagement ◽  
Structured Interviews ◽  
Cloud Technology ◽  
Healthcare Settings ◽  
Cloud Providers ◽  
Challenges And Opportunities ◽  
Returns On Investment

BACKGROUND The use of cloud computing (involving storage and processing of data on the internet) in healthcare has increasingly been highlighted as having great potential in facilitating data-driven innovations. Whilst some provider organizations are reaping the benefits of using cloud providers to store and process their data, others are lagging behind. OBJECTIVE We aimed at exploring and understanding existing challenges and barriers to the use of cloud computing in healthcare settings and investigate how perceived risks can be addressed. METHODS We conducted a qualitative case study of cloud computing in healthcare settings, interviewing a range of individuals with diverse perspectives on supply, implementation, adoption and integration of cloud technology. Data were collected through a series of in-depth semi-structured interviews exploring current applications, implementation approaches, challenges encountered and visions for the future. Interviews were transcribed and thematically analyzed with the help of NVivo 12 software. We coded the data based on a sociotechnical coding framework developed in related work. RESULTS We interviewed 23 people between September and November 2020 with professionals working across major cloud providers, healthcare provider organizations, innovators, small and medium-sized software vendors, and academic institutions. Participants were united by a common vision of a cloud-enabled ecosystem of applications and by drivers surrounding data-driven innovation. Identified barriers to progress included cost of data migration and skills gaps to implement cloud technologies within provider organizations, the cultural shift required to move to externally hosted services, a lack of user pull as many benefits were not visible to those providing frontline care, and a lack of interoperability standards and central regulations. CONCLUSIONS To facilitate the implementation and exploitation of cloud-based infrastructures and to maximize returns on investment, implementations need to be viewed as digitally-enabled transformation of services, driven by skills development, organizational change management and user engagement.

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