A REVIEW ON DATA MIGRATION IN PUBLIC CLOUD

2018

Cloud computing is a dynamic paradigm that is influencing activities in virtually all facets of the IT world. It has become quite easy to deploy applications on the cloud. Storage is also available based on user’s needs and can be scaled up or down as required by the user. Computing resources have also been made available on virtual machines. Furthermore, applications are available to users supplied by cloud providers. The activities on the cloud has made migration to the cloud desirable to most organizations and enterprises. Adopting the cloud is expected to reduce cost and the need for investment in computing infrastructure. However, most organizations are still concerned about the likely challenges of migrating to the cloud. The goal of this paper is to provide an insight into cloud computing with respect to migration issues. The paper discusses cloud computing and the benefits of migration. It also examines the challenges of migration. Furthermore, present issues of migration as...

international conference on cloud computing, 2020

SharePoint Online (SPO) is a Microsoft cloud-based business collaboration platform that is very robust and dynamic. Organizations can deploy and manage SharePoint Server on-Premises or can use SharePoint Online with an Office 365 Enterprise subscription. The platform has been in the market for almost two decades and last year SharePoint hit 100 million active monthly users in the cloud. The platform has become larger in scale, richer in features, and is improving consistently. Thus, SharePoint migration has become even more important, especially migrating into its online version. The SharePoint support cycle changes when a new version is released, which affects also the support for various features. Namely, newly added features and functionalities somehow enforce one to upgrade/migrate to the new SharePoint version. This paper seeks to show the best practices on how to do the migration of the SharePoint platform from one version to another. Five SharePoint migration projects have been described to serve as a case study. Engaging users during the migration process resulted in easier adoption of the new environment by the users and more efficient work from developers' perspective. Moreover, the study identifies 'must have actions' and 'nice to have ones' within each phase in order to do the migration properly. In particular, content owners should be given a date when to finish the cleanup of old/unused data; if they do not do that properly or at all, then at least it should be requested from them to clean-up workflows, solutions, and pages which are not in use, in order to save the time while developing/recreating them.

American Scientific Publishers, 2018

Legacy systems refer to the applications designed for a particular client that have been in use for a long period of time and developed using obsolete technologies. They are often business-critical systems, therefore any changes here inevitably will affect the other parts of the system. Although these systems are considered to be outdated but are too costly and risky for an organization to replace it. Cloud computing provides a new platform for organization that promises flexible scalability, business agility, high availability and reduction in costs. Considering these benefits, migration of legacy systems to cloud is a lucrative option for many organizations. However the architecture of these legacy applications require a tested, foolproof and risk free approach for migration. Limited migration models or frameworks have been proposed which caters issues of legacy systems migration to cloud platform. Many of these cloud adoption techniques and models emphasize on the generic phases and procedures on migrating the applications and data to cloud. However some of these models are more flexible, and provide better approaches compared to others. This research explores the issues associated with the legacy applications with regards to their migration on cloud and reviews the existing techniques and models that have been proposed in this context.

Multi-tiered storage systems today are integrating Solid State Disks (SSD) on top of traditional rotational hard disks for performance enhancement due to the significant IO improvements in SSD technology. It is widely recognized that automated data migration between SSD and HDD plays a critical role in effective integration of SSD into multi-tiered storage systems. Furthermore, effective data migration has to take into account of application specific workload characteristics, deadlines, and IO profiles. An important and interesting challenge for automated data migration in multi-tiered storage systems is how to fully release the power of data migration while guaranteeing the migration deadline is critical to maximizing the performance of SSD-enabled multi-tiered storage system. In this paper, we present an adatpive lookahead data migration model that can incorporate application specific characteristics and I/O profiles as well as workload deadlines. Our adaptive data migration model has three unique features. First, it incorporates a set of key factors that may impact on the performance of lookahead migration efficiency in our formal model develop. Second, our data migration model can adaptively determine the optimal lookahead window size, based on several parameters, to optimize the effectiveness of lookahead migration. Third, we formally and experimentally show that the adaptive data migration model can improve overall system performance and resource utilization while meeting workload deadlines. Through our trace driven experimental study, we compare the adaptive lookahead migration approach with the basic migration model and show that the adaptive migration model is effective and efficient for continuously improving and tuning of the performance and scalability of multi-tier storage systems.

International Journal of Advanced Computer Science and Applications, 2016

Along with the significant advantages of cloud computing paradigm, the number of enterprises, which expect to move a legacy system towards a cloud, is steadily increasing. Unfortunately, this move is not straightforward. There are many challenges to take up. The applications are often written with the outdated technologies. While some enterprises redevelop applications with a specific Cloud provider in mind, others try to move the legacy systems, either because the organization wants to keep the past investments, or because the legacy systems hold important data. Migrating the legacy systems to the Cloud introduces technical and business challenges. This paper aims to study deeply and to compare existing Cloud migration methods, based on Model Driven Engineering (MDE) approach to highlight the strengths and weaknesses of each one. Finally, we have proposed a Cloud legacy system Migration Method relied on Architecture Driven Modernization (ADM), and explained its working process.

IAEME PUBLICATION, 2024

This article presents a comprehensive analysis of the challenges and solutions associated with automated data migration in cloud environments, addressing the critical needs of modern enterprise digital transformation. Through extensive examination of industry practices, emerging technologies, and case studies, we identify key challenges including data integrity preservation, downtime minimization, and business continuity maintenance. While many organizations are adopting cloud-first strategies, successful migration remains a significant challenge, with 40% of projects exceeding planned downtime and budget allocations.

Porting applications to Clouds is one of the key challenges in software industry. The available approaches to perform this task are basically either services derived from alliances of major software vendors and Cloud providers focusing on their own products, or small platform providers focusing on the most popular software stacks. For migrating other types of software, the options are limited to Infrastructure-as-a-Service (IaaS) solutions which require a lot of programming effort for adapting the software to a Cloud provider’s API. Moreover, if it must be deployed in different providers, new integration procedures must be designed and implemented which could be a nightmare. This paper presents a solution for facilitating the migration of any application to the cloud, inferring the most suitable deployment model for the application and automatically deploying it in the available Cloud providers.

An important development in information technology, cloud computing allows users to share Internetbased access to pre-con Figured systems and services. While there are many benefits, such cost efficiency and scalability, security is still a big worry for everyone involved. The current practices in authentication have been found to be wanting in providing for the principles of CIA triad; confidentiality, integrity and availability. Data transfer to the cloud is also known as data migration, which takes data from on-premises databases together with other cloud services and which is normally associated with many problems such as data integrity and minimize down time. Additional barriers stem from the continuously maturing cloud environments and different levels of compatibility with the given database structures. This paper focuses on the processes that are involved in data migration and different catalogs of migration including, database migration, data center migration, application migration, business process migration and so on, stressing the significance of planning and implementing these migrations efficiently. The main issues that demand shifting to the cloud are outlined as well as the main approaches that large cloud suppliers such as AWS, M icrosoft Azure, and Google Cloud offer. Additionally, potential risks and challenges, such as vendor selection, security concerns, and resource management, are explored. This comprehensive overview highlights the significance of strategic planning and vendor solutions in ensuring successful cloud data migration, while addressing the inherent risks associated with transitioning to cloud-based infrastructures.

Cloud computing is one of the chief requirements of modern IT trade. Today's cloud industry progressively dependent on it, which lead mutually abundant solutions and challenges. Among the numerous challenges of Cloud computing, cloud migration is one of the major concern, and it is necessity to design optimize solutions to advance it with time. Data migration researchers attempt to move data concerning varying geographical locations, which contain huge data volumes, compact time limit and problematical architectures. Researchers aim to transfer data with minimal transmission cost and used various efficient scheduling methods and other techniques to achieve this objective. In former research struggles, numerous solutions have proposed. In our proposed work, we have explore the contextual factors to accomplish shorter transmission time. Entity Framework Core technology is utilize for conceptual modelling, mapping and storage modelling. Meant for minimum transmission cost Object Related Mapping is designated. Desired objective to achieve time efficiency during data migration has been accomplished. Results obtained when data transmission occur among azure and gearhost cloud with implementation of proposed framework with some size limitations.

Background-By leveraging cloud services, organizations can deploy their software systems over a pool of resources. However, organizations heavily depend on their business-critical systems, which have been developed over long periods. These legacy applications are usually deployed on-premise. In recent years, research in cloud migration has been carried out. However, there is no secondary study to consolidate this research. Objective-This paper aims to identify, taxonomically classify, and systematically compare existing research on cloud migration. Method-We conducted a systematic literature review (SLR) of 23 selected studies, published from 2010 to 2013. We classified and compared the selected studies based on a characterization framework that we also introduce in this paper. Results-The research synthesis results in a knowledge base of current solutions for legacy-to-cloud migration. This review also identifies research gaps and directions for future research. Conclusion-This review reveals that cloud migration research is still in early stages of maturity, but is advancing. It identifies the needs for a migration framework to help improving the maturity level and consequently trust into cloud migration. This review shows a lack of tool support to automate migration tasks. This study also identifies needs for architectural adaptation and self-adaptive cloud-enabled systems.