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Data Migration: Cloudsim Extension

Published: 21 January 2020 Publication History

Abstract

With the constantly growing volume of data in the context of cloud applications deployed in geographically distributed systems, the issue of data management has turned to be an essential study to evaluate the performance of the cloud system. Beside the classic task scheduling activity, handling input data for tasks becomes one of the most challenges towards intensive-data applications. In fact, input data should normally be gathered on the same datacenter where the task is scheduled for execution. So, to meet that, migration strategies are required for distant data and should be as efficient as possible. Data migration strategies consist on moving data across geographically distributed data centers in order to execute tasks in the cloud. Therefore, we need to know statistics about the entire mechanism, which has led us to extend Cloudsim, a popular simulation toolkit for large scale cloud computing infrastructures and application services. In this paper, we show how Cloudsim can be investigated to provide a scalable module able to simulate and perform data migration in cloud applications. In order to illustrate our module, different simulation scenarios are suggested to make explicit description for the implemented strategies and to prove the significance of our tool.

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Cited By

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  • (2023)Online Task Scheduling of Big Data Applications in the Cloud EnvironmentInformation10.3390/info1405029214:5(292)Online publication date: 15-May-2023
  • (2022)Dynamic data replication and placement strategy in geographically distributed data centersConcurrency and Computation: Practice and Experience10.1002/cpe.685835:14Online publication date: Mar-2022
  • (2020)A Big Data Placement Strategy in Geographically Distributed Datacenters2020 5th International Conference on Cloud Computing and Artificial Intelligence: Technologies and Applications (CloudTech)10.1109/CloudTech49835.2020.9365881(1-9)Online publication date: 24-Nov-2020

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cover image ACM Other conferences
ICBDR '19: Proceedings of the 3rd International Conference on Big Data Research
November 2019
192 pages
ISBN:9781450372015
DOI:10.1145/3372454
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

In-Cooperation

  • Shandong Univ.: Shandong University
  • The University of Versailles Saint-Quentin: The University of Versailles Saint-Quentin, Versailles, France

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 21 January 2020

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Author Tags

  1. Big Data
  2. Cloud Computing
  3. Cloudsim
  4. Data Migration

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  • Refereed limited

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Cited By

View all
  • (2023)Online Task Scheduling of Big Data Applications in the Cloud EnvironmentInformation10.3390/info1405029214:5(292)Online publication date: 15-May-2023
  • (2022)Dynamic data replication and placement strategy in geographically distributed data centersConcurrency and Computation: Practice and Experience10.1002/cpe.685835:14Online publication date: Mar-2022
  • (2020)A Big Data Placement Strategy in Geographically Distributed Datacenters2020 5th International Conference on Cloud Computing and Artificial Intelligence: Technologies and Applications (CloudTech)10.1109/CloudTech49835.2020.9365881(1-9)Online publication date: 24-Nov-2020

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