Towards Quantifiable Eventual Consistency

Francisco Maia; Miguel Matos; Fábio Coelho

Research.Publish.Connect.

*Please fill out at least one Field. *Value must be an number!

Title:
ISBN:
Year:
Acronym:
Subject:

Advanced Search Proceedings Search

If you're looking for an exact phrase use quotation marks on text fields.

*Please fill out at least one Field.

Title:
Author:
Affiliation:
Subject:

Advanced Search Papers Search

If you're looking for an exact phrase use quotation marks on text fields.

*Please fill out at least one Field.

Name:
Affiliation:
Country:
Conference:
Subject:

Advanced Search Authors Search

If you're looking for an exact phrase use quotation marks on text fields.

*Please fill out at least one Field.

Name:
Country:
Subject:

Advanced Search Affiliations Search

If you're looking for an exact phrase use quotation marks on text fields.

Proceedings

Proceedings Search *Please fill out at least one Field. *Value must be an number!

Title:
ISBN:
Year:
Acronym:
Subject:

Advanced Search Proceedings Search

If you're looking for an exact phrase use quotation marks on text fields.

Papers

Papers Search *Please fill out at least one Field.

Title:
Author:
Affiliation:
Subject:

Advanced Search Papers Search

If you're looking for an exact phrase use quotation marks on text fields.

Authors

Authors Search *Please fill out at least one Field.

Name:
Affiliation:
Country:
Conference:
Subject:

Advanced Search Authors Search

If you're looking for an exact phrase use quotation marks on text fields.

Advanced Search

Paper

Towards Quantifiable Eventual Consistency

In Proceedings of the 6th International Conference on Cloud Computing and Services Science - Volume 1: DataDiversityConvergence, 368-370, 2016 , Rome, Italy

Authors: Francisco Maia ; Miguel Matos and Fábio Coelho

Affiliation: INESC TEC & U. Minho, Portugal

Keyword(s): Large Scale, Data Stores, Epidemic Protocols.

Abstract: In the pursuit of highly available systems, storage systems began offering eventually consistent data models. These models are suitable for a number of applications but not applicable for all. In this paper we discuss a system that can offer a eventually consistent data model but can also, when needed, offer a strong consistent one.

CC BY-NC-ND 4.0

Guest: Register as new SciTePress user now for free.

SciTePress user: please login.

My Papers

You are not signed in, therefore limits apply to your IP address 8.217.144.104

In the current month:

Recent papers: 100 available of 100 total

2⁺ years older papers: 200 available of 200 total

Paper citation in several formats:

Maia, F., Matos, M. and Coelho, F. (2016). Towards Quantifiable Eventual Consistency. In Proceedings of the 6th International Conference on Cloud Computing and Services Science (CLOSER 2016) - Volume 1: DataDiversityConvergence; ISBN 978-989-758-182-3; ISSN 2184-5042, SciTePress, pages 368-370. DOI: 10.5220/0005929103680370

@conference{datadiversityconvergence16,
author={Francisco Maia and Miguel Matos and Fábio Coelho},
title={Towards Quantifiable Eventual Consistency},
booktitle={Proceedings of the 6th International Conference on Cloud Computing and Services Science (CLOSER 2016) - Volume 1: DataDiversityConvergence},
year={2016},
pages={368-370},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005929103680370},
isbn={978-989-758-182-3},
issn={2184-5042},
}

TY - CONF

JO - Proceedings of the 6th International Conference on Cloud Computing and Services Science (CLOSER 2016) - Volume 1: DataDiversityConvergence
TI - Towards Quantifiable Eventual Consistency
SN - 978-989-758-182-3
IS - 2184-5042
AU - Maia, F.
AU - Matos, M.
AU - Coelho, F.
PY - 2016
SP - 368
EP - 370
DO - 10.5220/0005929103680370
PB - SciTePress