scholarly journals Cloud to cloud data migration using self sovereign identity for 5G and beyond

2021 ◽  
Author(s):  
M. G. Aruna ◽  
Mohammad Kamrul Hasan ◽  
Shayla Islam ◽  
K. G. Mohan ◽  
Preeta Sharan ◽  
...  
Keyword(s):  
Data Migration ◽  
Cloud 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.

Data Replication Strategies with Load Balancing and Data Migration in Cloud Data Center

2020 ◽  
Vol 17 (5) ◽  
pp. 2024-2029
Author(s):  
E. Bijolin Edwin ◽  
M. Roshni Thanka
Keyword(s):  
Ant Colony Algorithm ◽  
Storage Systems ◽  
Distributed Storage ◽  
Scheduling Algorithm ◽  
Data Migration ◽  
Cloud Data Center ◽  
Cloud Data ◽  
Replicated Data ◽  
Distributed Storage Systems ◽  
Improved Performance

The evolution of Information Systems implies new applications and the need to migrate the data from a previous application to a new one. At the same time, some organizations may need to replicate data from one technology to another one, in order to have backup systems and have a flexible load balanced strategies. The maximal uniform distribution of the load across closer and number of simpler nodes can help managing and providing the big data and large workloads which are more easy to handle. The ultimate goal is to balance the load through cloud and make internet less cloud defendant by having data available closer to the user end. One of the most challenging steps required to deploy an application infrastructure in the cloud involves the physics of moving data into and out of the cloud. Amazon Web Services (AWS) provides a number of services for moving data, and each solution offers various levels of speed, security, cost, and performance. This stems from the fact that almost all the typical distributed storage systems only provide data-amount-oriented balancing mechanisms without considering the different access load of data. To eliminate the system bottlenecks and optimize the resource utilization, there is a demand for such distributed storage systems to employ a workload balancing and adaptive resource management framework. We propose a framework of Enhanced replication scheduling algorithm which balances the replicated data to be balanced and to handle the overload data integration by data migration concept which gives more data efficiency and improved performance during migration of replicated data. For handling of data migration, we propose Ant Colony Algorithm which gives a safe data migration from one end to the other. This will improve the efficiency, Cost and takes less duration for the data to migrated and to be equally balanced.

scholarly journals Mitigating the Threat due to Data Deduplication Attacks in Cloud Migration using User Layer Authentication with Light Weight Cryptography

2020 ◽  
Vol 9 (3) ◽  
pp. 2539-2545
Keyword(s):  
Data Security ◽  
Service Providers ◽  
Data Integrity ◽  
Cloud Service ◽  
Behavioral Pattern ◽  
Data Migration ◽  
Data Deduplication ◽  
Cloud Data ◽  
Template Attacks ◽  
The Impact

The widespread adoption of multi-cloud in enterprises is one of the root causes of cost-effectiveness. Cloud service providers reduce storage costs through advanced data de-duplication, which also provides vulnerabilities for attackers. Traditional approaches to authentication and data security for a single cloud need to be upgraded to be best suitable for cloud-to-cloud data migration security in order to mitigate the impact of dictionary and template attacks on authentication and data integrity, respectively. This paper proposes a scheme of user layer authentication along with lightweight cryptography. The proposed simulates its mathematical model to analyze the behavioral pattern of time-complexity of data security along with user auth protection. The performance pattern validates the model for scalability and reliability against both authentication and data integrity.

scholarly journals A Novel Technique to Improve the Performance of fog Computing using fog Terminal Nodes

2019 ◽  
Vol 8 (12) ◽  
pp. 4010-4014
Keyword(s):  
Fog Computing ◽  
Cloud Services ◽  
Data Migration ◽  
Time Data ◽  
Cloud Data ◽  
Computing Platform ◽  
Processing Power ◽  
Generation Computing ◽  
Real Time Data Processing ◽  
Cloud Servers

Cloud computing is considered to be technological revolution in the past decade, due to its reliability and flexibility in enabling anything-as-a-service to the end users based on the key principle of utility computing. With the advent of IoT and Real-time data processing continuous usage of cloud services have incremented the dependency levels of Cloud Data Centres which in a while required high processing power as well as it will be hazardous to the environment. Addressing this problem several research studies have identified FoG Computing as a next generation computing platform that enhances the performance of the cloud servers by processing the data at the edge devices. This paper presents a novel fog computing framework that enhances the performance of the data migration reducing the effort on cloud servers.

scholarly journals Polar Particle Swarm Algorithm for Solving Cloud Data Migration Optimization Problem

2017 ◽  
Vol 11 (8) ◽  
pp. 98 ◽  
Author(s):  
Rizik M. H. Al-Sayyed ◽  
Hussam N. Fakhouri ◽  
Ali Rodan ◽  
Colin Pattinson
Keyword(s):  
Optimization Problem ◽  
Particle Swarm ◽  
Search Space ◽  
Data Migration ◽  
Grey Wolf Optimizer ◽  
Particle Swarm Algorithm ◽  
Local Minima ◽  
Cloud Data ◽  
Polar Function ◽  
Sine Cosine Algorithm

Particle Swarm Optimization (PSO) has proved to be a common meta-heuristic algorithm for determining the minimum value among a set of values but it is known to suffer from the local minima problem. In this paper, we propose a novel optimization algorithm called POLARPSO that enhances the behavior of PSO and avoids the local minima problem by using a polar function to search for more points in the search space. The algorithm has been tested on 23 well-known benchmark factions and the results are verified by comparing them with state of the art algorithms: Grey Wolf Optimizer (GWO), Sine Cosine Algorithm (SCA), Multi-Verse Optimizer (MVO) as well as PSO. The paper also considers a solution to the cloud data migration problem where data migrates from highly loaded nodes to less loaded nodes in a process aims at achieving a kind of load balancing. The results prove that the proposed algorithm is applicable to solve this challenging problem in cloud environment and is able to find the best node to migrate to quickly and effectively. Our empirical results show that the proposed algorithm has enhanced the PSO behavior in reaching the best solution and outperformed the other algorithms over the tested benchmarked functions.

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