scholarly journals Serverless Computing Platform for Big Data Storage

2019 ◽  
Vol 8 (3) ◽  
pp. 6592-6595
Keyword(s):  
Big Data ◽  
Data Storage ◽  
Cloud Storage ◽  
Optimal Solution ◽  
Cloud Provider ◽  
Random Load ◽  
Data Movement ◽  
Computing Platform ◽  
Big Data Applications ◽  
Load Increase

This paper describes various challenges faced by the Big Data cloud providers and the challenges encountered by its users. This foreshadows that the Serverless computing as the feasible platform for Big Data application’s data storages. The literature research undertaken focuses on various Serverless computing architectural designs, computational methodologies, performance, data movement and functions. The framework for Serverless cloud computing is discussed and its performance is tested for the metric of scaling in the Serverless cloud storage for Big Data applications. The results of the analyses and its outcome are also discussed. Thus suggesting that the scaling of Serverless cloud storage for data storage during random load increase as the optimal solution for cloud provider and Big Data application user.

Towards Federation and Interoperability of Cloud Storage Systems

2013 ◽  
pp. 60-71 ◽  
Author(s):  
Sebastian Dippl ◽  
Michael C. Jaeger ◽  
Achim Luhn ◽  
Alexandra Shulman-Peleg ◽  
Gil Vernik
Keyword(s):  
Big Data ◽  
Cloud Storage ◽  
Storage Systems ◽  
Network Throughput ◽  
Use Cases ◽  
Single System ◽  
Cloud Systems ◽  
Logical Conclusion ◽  
Big Data Applications ◽  
Distributed Cloud

While it is common to use storage in a cloud-based manner, the question of true interoperability is rarely fully addressed. This question becomes even more relevant since the steadily growing amount of data that needs to be stored will supersede the capacity of a single system in terms of resources, availability, and network throughput quite soon. The logical conclusion is that a network of systems needs to be created that is able to cope with the requirements of big data applications and data deluge scenarios. This chapter shows how federation and interoperability will fit into a cloud storage scenario. The authors take a look at the challenges that federation imposes on autonomous, heterogeneous, and distributed cloud systems, and present approaches that help deal with the special requirements introduced by the VISION Cloud use cases from healthcare, media, telecommunications, and enterprise domains. Finally, the authors give an overview on how VISION Cloud addresses these requirements in its research scenarios and architecture.

Cloud-Centric Blockchain Public Key Infrastructure for Big Data Applications

2020 ◽  
pp. 125-140
Author(s):  
Brian Tuan Khieu ◽  
Melody Moh
Keyword(s):  
Big Data ◽  
Data Storage ◽  
Public Key Infrastructure ◽  
Public Key ◽  
Cloud Provider ◽  
Smart Contracts ◽  
Security Measures ◽  
Certificate Authority ◽  
Blockchain Technology ◽  
Attack Surface

A cloud-based public key infrastructure (PKI) utilizing blockchain technology is proposed. Big data ecosystems have scalable and resilient needs that current PKI cannot satisfy. Enhancements include using blockchains to establish persistent access to certificate data and certificate revocation lists, decoupling of data from certificate authority, and hosting it on a cloud provider to tap into its traffic security measures. Instead of holding data within the transaction data fields, certificate data and status were embedded into smart contracts. The tests revealed a significant performance increase over that of both traditional and the version that stored data within blocks. The proposed method reduced the mining data size, and lowered the mining time to 6.6% of the time used for the block data storage method. Also, the mining gas cost per certificate was consequently cut by 87%. In summary, completely decoupling the certificate authority portion of a PKI and storing certificate data inside smart contracts yields a sizable performance boost while decreasing the attack surface.

Architecture for Big Data Storage in Different Cloud Deployment Models

2021 ◽  
pp. 178-208
Author(s):  
Chandu Thota ◽  
Gunasekaran Manogaran ◽  
Daphne Lopez ◽  
Revathi Sundarasekar
Keyword(s):  
Cloud Computing ◽  
Big Data ◽  
Data Storage ◽  
High Performance ◽  
Data Services ◽  
Big Data Applications ◽  
Nosql Database ◽  
Amazon Web Services ◽  
Product Domains ◽  
Scalable Database

Cloud Computing is a new computing model that distributes the computation on a resource pool. The need for a scalable database capable of expanding to accommodate growth has increased with the growing data in web world. More familiar Cloud Computing vendors such as Amazon Web Services, Microsoft, Google, IBM and Rackspace offer cloud based Hadoop and NoSQL database platforms to process Big Data applications. Variety of services are available that run on top of cloud platforms freeing users from the need to deploy their own systems. Nowadays, integrating Big Data and various cloud deployment models is major concern for Internet companies especially software and data services vendors that are just getting started themselves. This chapter proposes an efficient architecture for integration with comprehensive capabilities including real time and bulk data movement, bi-directional replication, metadata management, high performance transformation, data services and data quality for customer and product domains.

scholarly journals Performance Analysis of NoSQL and Relational Databases with CouchDB and MySQL for Application’s Data Storage

2020 ◽  
Vol 10 (23) ◽  
pp. 8524
Author(s):  
Cornelia A. Győrödi ◽  
Diana V. Dumşe-Burescu ◽  
Doina R. Zmaranda ◽  
Robert Ş. Győrödi ◽  
Gianina A. Gabor ◽  
...  
Keyword(s):  
Big Data ◽  
Data Storage ◽  
Relational Databases ◽  
Database Systems ◽  
Application Performance ◽  
Big Data Applications ◽  
Database Technology ◽  
Important Challenge ◽  
Big Data Application ◽  
The Impact

In the current context of emerging several types of database systems (relational and non-relational), choosing the type and database system for storing large amounts of data in today’s big data applications has become an important challenge. In this paper, we aimed to provide a comparative evaluation of two popular open-source database management systems (DBMSs): MySQL as a relational DBMS and, more recently, as a non-relational DBMS, and CouchDB as a non-relational DBMS. This comparison was based on performance evaluation of CRUD (CREATE, READ, UPDATE, DELETE) operations for different amounts of data to show how these two databases could be modeled and used in an application and highlight the differences in the response time and complexity. The main objective of the paper was to make a comparative analysis of the impact that each specific DBMS has on application performance when carrying out CRUD requests. To perform the analysis and to ensure the consistency of tests, two similar applications were developed in Java, one using MySQL and the other one using CouchDB database; these applications were further used to evaluate the time responses for each database technology on the same CRUD operations on the database. Finally, a comprehensive discussion based on the results of the analysis was performed that centered on the results obtained and several conclusions were revealed. Advantages and drawbacks for each DBMS are outlined to support a decision for choosing a specific type of DBMS that could be used in a big data application.

scholarly journals Blockchain-based Secure Big Data Storage on Cloud

2020 ◽  
Vol 9 (4) ◽  
pp. 37-45
Keyword(s):  
Big Data ◽  
Comparative Analysis ◽  
Data Storage ◽  
Data Security ◽  
Cloud Storage ◽  
Information Technologies ◽  
Mobile Cloud ◽  
Blockchain Technology ◽  
Data Authentication ◽  
Big Data Storage

In the cryptocurrency era, Blockchain is one of the expeditiously growing information technologies that help in providing security to the data. Data tampering and authentication problems generally occur in centralized servers while sharing and storing the data. Blockchain provides the platform for big data and cloud storage in enhancing the security by evading from pernicious users. In this paper, we have discussed the exhaustive description of blockchain and its need, features and applications. Analysis of blockchain is done for different domains such as big data, cloud, internet of things and mobile cloud where the differences V’s are compared with big data and blockchain. SWOT (Strength Weakness Opportunities Threats) analysis is performed to address the merits and limitations in blockchain technology. The survey in aspects of data security, data storage, data sharing and data authentication through blockchain technology is done and the challenges are discussed to overcome the problem that leads in big data and cloud storage. The detailed comparative analysis proves that the blockchain technology overcomes the problems in big data storage and data security in cloud.

scholarly journals Reflections on the Innovation of University Scientific Research Management in the Era of Big Data

2022 ◽  
Vol 2022 ◽  
pp. 1-8
Author(s):  
Yiming Li
Keyword(s):  
Big Data ◽  
Scientific Research ◽  
Research Management ◽  
Support Vector ◽  
Optimization Strategy ◽  
Web Database ◽  
Computing Platform ◽  
Research And Innovation ◽  
Software Configuration ◽  
Big Data Applications

In China, universities are important centers for SR (scientific research) and innovation, and the quality of SR management has a significant impact on university innovation. The informatization of SR management is a critical component of university development in the big data environment. As a result, it is crucial to figure out how to improve SR management. As a result, this paper builds a four-tier B/W/D/C (Browser/Web/Database/Client) university SR management innovation information system based on big data technology and thoroughly examines the system’s hardware and software configuration. The SVM-WNB (Support Vector Machine-Weighted NB) classification algorithm is proposed, and the improved algorithm runs in parallel on the Hadoop cloud computing platform, allowing the algorithm to process large amounts of data efficiently. The optimization strategy proposed in this paper can effectively optimize the execution of scientific big data applications according to a large number of simulation experiments and real-world multidata center environment experiments.

A Review of Security Challenges in Cloud Storage of Big Data

2019 ◽  
pp. 1440-1459
Author(s):  
Sara Usmani ◽  
Faiza Rehman ◽  
Sajid Umair ◽  
Safdar Abbas Khan
Keyword(s):  
Information Technology ◽  
Cloud Computing ◽  
Big Data ◽  
Data Storage ◽  
Cloud Storage ◽  
Personal Data ◽  
Security And Privacy ◽  
Privacy And Security ◽  
Security Issues ◽  
The People

The novel advances in the field of Information Technology presented the people pleasure, luxuries and ease. One of the latest expansions in the Information Technology (IT) industry is Cloud Computing, a technology that uses the internet for storage and access of data. It is also known as on-demand computing. The end user can access personal data and applications anywhere any time with a device having internet. Cloud Computing has gained an enormous attention but it results in the issues of data security and privacy as the data is scattered on different machines in different places across the globe which is a serious threat to the technology. It has many advantages like flexibility, efficiency and scalability but many of the companies are hesitant to invest in it due to privacy concerns. In this chapter, the objective is to review the privacy and security issues in cloud storage of Big Data and to enhance the security in cloud environment so that end users can enjoy a trustworthy and reliable data storage and access.

Database Systems for Big Data Storage and Retrieval

2018 ◽  
pp. 76-100 ◽  
Author(s):  
Venkat Gudivada ◽  
Amy Apon ◽  
Dhana L. Rao
Keyword(s):  
Big Data ◽  
Data Storage ◽  
Database Systems ◽  
Query Languages ◽  
Research Issues ◽  
Storage And Retrieval ◽  
Emerging Trends ◽  
Big Data Applications ◽  
Data Application ◽  
Big Data Application

Special needs of Big Data applications have ushered in several new classes of systems for data storage and retrieval. Each class targets the needs of a category of Big Data application. These systems differ greatly in their data models and system architecture, approaches used for high availability and scalability, query languages and client interfaces provided. This chapter begins with a description of the emergence of Big Data and data management requirements of Big Data applications. Several new classes of database management systems have emerged recently to address the needs of Big Data applications. NoSQL is an umbrella term used to refer to these systems. Next, a taxonomy for NoSQL systems is developed and several NoSQL systems are classified under this taxonomy. Characteristics of representative systems in each class are also discussed. The chapter concludes by indicating the emerging trends of NoSQL systems and research issues.

scholarly journals Research on Big data Security privacy Protection based on cloud Computing

2021 ◽  
Vol 2083 (4) ◽  
pp. 042077
Author(s):  
Tongtong Xu ◽  
Lei Shi
Keyword(s):  
Cloud Computing ◽  
Big Data ◽  
Data Storage ◽  
Data Security ◽  
Privacy Protection ◽  
Data Privacy ◽  
Cloud Services ◽  
Security Mechanism ◽  
Computing Platform ◽  
Cloud Servers

Abstract Cloud computing is a new way of computing and storage. Users do not need to master professional skills, but can enjoy convenient network services as long as they pay according to their own needs. When we use cloud services, we need to upload data to cloud servers. As the cloud is an open environment, it is easy for attackers to use cloud computing to conduct excessive computational analysis on big data, which is bound to infringe on others’ privacy. In this process, we inevitably face the challenge of data security. How to ensure data privacy security in the cloud environment has become an urgent problem to be solved. This paper studies the big data security privacy protection based on cloud computing platform. This paper starts from two aspects: implicit security mechanism and display security mechanism (encryption mechanism), so as to protect the security privacy of cloud big data platform in data storage and data computing processing.

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