Supporting Extensible Performance SLAs for Cloud Databases

A Survey on Database in Cloud Computing with Reference to the Traditional Database

International Journal of Advanced Research in Computer Science and Software Engineering ◽

10.23956/ijarcsse.v7i11.477 ◽

2017 ◽

Vol 7 (11) ◽

pp. 150

Author(s):

Eter Basar ◽

Ankur Pan Saikia ◽

L. P. Saikia

Keyword(s):

Information Technology ◽

Cloud Computing ◽

The Internet ◽

Data Organization ◽

Technology Industry ◽

Business Applications ◽

Business Information ◽

Points Of Interest ◽

Cloud Databases ◽

Online Reservation

Data Technology industry has been utilizing the customary social databases for around 40 years. Be that as it may, in the latest years, there was a generous transformation in the IT business as far as business applications. Remain solitary applications have been supplanted with electronic applications, conferred servers with different proper servers and committed stockpiling with framework stockpiling. Lower expense, adaptability, the model of pay-as-you-go are the fundamental reasons, which caused the conveyed processing are transformed into reality. This is a standout amongst the hugest upsets in Information Technology, after the development of the Internet. Cloud databases, Big Table, Sherpa, and SimpleDB are getting the opportunity to be more natural to groups. They featured the hindrances of current social databases as far as convenience, adaptability, and provisioning. Cloud databases are basically utilized for data raised applications, for example, stockpiling and mining of gigantic information or business information. These applications are adaptable and multipurpose in nature. Various esteem based data organization applications, such as managing an account, online reservation, e-exchange and stock organization, and so on are delivered. Databases with the help of these sorts of uses need to incorporate four essential highlights: Atomicity, Consistency, Isolation, and Durability (ACID), in spite of the fact that utilizing these databases isn't basic for utilizing as a part of the cloud. The objective of this paper is to discover the points of interest and disservices of databases generally utilized in cloud frameworks and to survey the difficulties in creating cloud databases

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Problems of Network Data Storage and Cloud Databases

4th International Symposium on Innovative Approaches in Engineering and Natural Sciences Proceedings ◽

10.36287/setsci.4.6.008 ◽

2019 ◽

Author(s):

S.Mubariz Khalilov ◽

Irada B. Dadashova

Keyword(s):

Data Storage ◽

Network Data ◽

Cloud Databases

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State Management for Cloud-Native Applications

Electronics ◽

10.3390/electronics10040423 ◽

2021 ◽

Vol 10 (4) ◽

pp. 423

Author(s):

Márk Szalay ◽

Péter Mátray ◽

László Toka

Keyword(s):

Large Scale ◽

Distributed Databases ◽

Access Time ◽

Replica Placement ◽

State Management ◽

Placement Decisions ◽

Dynamic Replication ◽

Cloud Databases ◽

Placement Algorithm ◽

The One

The stateless cloud-native design improves the elasticity and reliability of applications running in the cloud. The design decouples the life-cycle of application states from that of application instances; states are written to and read from cloud databases, and deployed close to the application code to ensure low latency bounds on state access. However, the scalability of applications brings the well-known limitations of distributed databases, in which the states are stored. In this paper, we propose a full-fledged state layer that supports the stateless cloud application design. In order to minimize the inter-host communication due to state externalization, we propose, on the one hand, a system design jointly with a data placement algorithm that places functions’ states across the hosts of a data center. On the other hand, we design a dynamic replication module that decides the proper number of copies for each state to ensure a sweet spot in short state-access time and low network traffic. We evaluate the proposed methods across realistic scenarios. We show that our solution yields state-access delays close to the optimal, and ensures fast replica placement decisions in large-scale settings.

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