scholarly journals Towards Distributed Data Management in Fog Computing

2018 ◽  
Vol 2018 ◽  
pp. 1-14 ◽  
Author(s):  
Vasileios Moysiadis ◽  
Panagiotis Sarigiannidis ◽  
Ioannis Moscholios
Keyword(s):  
Cloud Computing ◽  
Data Storage ◽  
Large Scale ◽  
Fog Computing ◽  
Cloud Services ◽  
Smart Devices ◽  
Distributed Data ◽  
Storage Allocation ◽  
Huge Amount ◽  
Distributed Data Storage

In the emerging area of the Internet of Things (IoT), the exponential growth of the number of smart devices leads to a growing need for efficient data storage mechanisms. Cloud Computing was an efficient solution so far to store and manipulate such huge amount of data. However, in the next years it is expected that Cloud Computing will be unable to handle the huge amount of the IoT devices efficiently due to bandwidth limitations. An arising technology which promises to overwhelm many drawbacks in large-scale networks in IoT is Fog Computing. Fog Computing provides high-quality Cloud services in the physical proximity of mobile users. Computational power and storage capacity could be offered from the Fog, with low latency and high bandwidth. This survey discusses the main features of Fog Computing, introduces representative simulators and tools, highlights the benefits of Fog Computing in line with the applications of large-scale IoT networks, and identifies various aspects of issues we may encounter when designing and implementing social IoT systems in the context of the Fog Computing paradigm. The rationale behind this work lies in the data storage discussion which is performed by taking into account the importance of storage capabilities in modern Fog Computing systems. In addition, we provide a comprehensive comparison among previously developed distributed data storage systems which consist of a promising solution for data storage allocation in Fog Computing.

Research on Digital Library Based on Cloud Computing

2014 ◽  
Vol 1078 ◽  
pp. 448-451
Author(s):  
Xiao Wen Chen
Keyword(s):  
Cloud Computing ◽  
Data Storage ◽  
Digital Library ◽  
Digital Libraries ◽  
Information Service ◽  
Information Services ◽  
Cloud Services ◽  
Distributed Data ◽  
Distributed Data Storage ◽  
Massive Data Processing

In recent years, the development of digital libraries also encountered a lot of problems, the cloud computing was applied to digital libraries can solve the problem of information on demand and optimal scheduling of resources and transaction processing capabilities effectively, So as to achieve the purpose of improving the efficiency of resource of digital library and information security. From the digital library of cloud computing and personalized information services status in this paper, the study and found a problem about the digital library information services at this stage, let the virtualization cloud computing, the key technology of distributed data storage, massive data processing and cloud platforms used in building digital library of personalized information service with cloud platform, and deployment cloud services on the platform.

Grid Data Handling

2013 ◽  
pp. 294-321
Author(s):  
Alexandru Costan
Keyword(s):  
Fault Tolerance ◽  
Data Storage ◽  
Large Scale ◽  
File Systems ◽  
Future Research ◽  
Distributed Data ◽  
Data Handling ◽  
Grid Data ◽  
Distributed Data Storage ◽  
Grid Environments

To accommodate the needs of large-scale distributed systems, scalable data storage and management strategies are required, allowing applications to efficiently cope with continuously growing, highly distributed data. This chapter addresses the key issues of data handling in grid environments focusing on storing, accessing, managing and processing data. We start by providing the background for the data storage issue in grid environments. We outline the main challenges addressed by distributed storage systems: high availability which translates into high resilience and consistency, corruption handling regarding arbitrary faults, fault tolerance, asynchrony, fairness, access control and transparency. The core part of the chapter presents how existing solutions cope with these high requirements. The most important research results are organized along several themes: grid data storage, distributed file systems, data transfer and retrieval and data management. Important characteristics such as performance, efficient use of resources, fault tolerance, security, and others are strongly determined by the adopted system architectures and the technologies behind them. For each topic, we shortly present previous work, describe the most recent achievements, highlight their advantages and limitations, and indicate future research trends in distributed data storage and management.

Novel Taxonomy to Select Fog Products and Challenges Faced in Fog Environments

2021 ◽  
pp. 2558-2571
Author(s):  
Akashdeep Bhardwaj
Keyword(s):  
Cloud Computing ◽  
Distributed Computing ◽  
High Speed ◽  
Low Cost ◽  
Fog Computing ◽  
Research Work ◽  
Cloud Services ◽  
Smart Devices ◽  
Viable Solution ◽  
Last Mile

This article describes how the rise of fog computing to improve cloud computing performance and the acceptance of smart devices is slowly but surely changing our future and shaping the computing environment around us. IoT integrated with advances in low cost computing, storage and power, along with high speed networks and big data, supports distributed computing. However, much like cloud computing, which are under constant security attacks and issues, distributed computing also faces similar challenges and security threats. This can be mitigated to a great extent using fog computing, which extends the limits of Cloud services to the last mile edge near to the nodes and networks, thereby increasing the performance and security levels. Fog computing also helps increase the reach and comes across as a viable solution for distributed computing. This article presents a review of the academic literature research work on the Fog Computing. The authors discuss the challenges in Fog environment and propose a new taxonomy.

scholarly journals Enhancing Distributed Data Storage Security for Cloud Computing using AES algorithm

IJARCCE ◽  
2017 ◽  
Vol 6 (3) ◽  
pp. 752-755 ◽  
Author(s):  
Vasant S. Kakade ◽  
Akshay Kirve ◽  
Ankush Bhoir ◽  
Sagar Kadam
Keyword(s):  
Cloud Computing ◽  
Data Storage ◽  
Distributed Data ◽  
Distributed Data Storage ◽  
Storage Security ◽  
Aes Algorithm

Novel Taxonomy to Select Fog Products and Challenges Faced in Fog Environments

2018 ◽  
Vol 1 (1) ◽  
pp. 35-49 ◽  
Author(s):  
Akashdeep Bhardwaj
Keyword(s):  
Cloud Computing ◽  
Distributed Computing ◽  
High Speed ◽  
Low Cost ◽  
Fog Computing ◽  
Research Work ◽  
Cloud Services ◽  
Smart Devices ◽  
Viable Solution ◽  
Last Mile

This article describes how the rise of fog computing to improve cloud computing performance and the acceptance of smart devices is slowly but surely changing our future and shaping the computing environment around us. IoT integrated with advances in low cost computing, storage and power, along with high speed networks and big data, supports distributed computing. However, much like cloud computing, which are under constant security attacks and issues, distributed computing also faces similar challenges and security threats. This can be mitigated to a great extent using fog computing, which extends the limits of Cloud services to the last mile edge near to the nodes and networks, thereby increasing the performance and security levels. Fog computing also helps increase the reach and comes across as a viable solution for distributed computing. This article presents a review of the academic literature research work on the Fog Computing. The authors discuss the challenges in Fog environment and propose a new taxonomy.

scholarly journals Robust Security with Strong Authentication in Mobile Cloud Computing based on Trefoil Congruity framework

2021 ◽  
Vol 33 (6) ◽  
pp. 0-0
Keyword(s):  
Cloud Computing ◽  
Data Storage ◽  
Large Scale ◽  
Mobile Cloud Computing ◽  
Data Transfer ◽  
Mobile Cloud ◽  
Distributed Data ◽  
Large Scale Data ◽  
Backup And Recovery ◽  
Secured Data

In the mobile cloud computing era, the sharing of secured large-scale data which have major challenges. From an existing quantum based security mechanism randomly chosen the photon detector which creates small length of qubits so it cannot provide much security in MCC also data storage in the cloud server doesn’t guarantees the lossless back up and data recovery as well attains more computation complex during secure access of stored data. Therefore to solve those issues a unique combination of the Trefoil Congruity framework is proposed which consist Quantum Key Fibo Privacy Approach (QKFPA) performing the quantum key generation for encrypt and decrypt the data with the aid of Fibonacci chain-slanting matrix. Based on that quantum key data is uploaded, then secured data should be stored, ultra-widely distributed data transfer mechanism does the scrambling with sorting the stored data by implementing novel HS-DRT technique that improves the lossless backup and recovery of data storage.

Sign in / Sign up

Export Citation Format

Share Document