<abstract xmlns="http://www.w3.org/1999/xhtml"><p> With the rapid evolution of the distributed computing world in the last few years, the amount of data created and processed has fast increased to petabytes or even exabytes scale. Such huge data sets need data-intensive computing applications and impose performance requirements to the infrastructures that support them, such as high scalability, storage, fault tolerance but also efficient scheduling algorithms. This paper focuses on providing a hybrid scheduling algorithm for many task computing that addresses big data environments with few penalties, taking into consideration the deadlines and satisfying a data dependent task model. The hybrid solution consists of several heuristics and algorithms (min-min, min-max and earliest deadline first) combined in order to provide a scheduling algorithm that matches our problem. The experimental results are conducted by simulation and prove that the proposed hybrid algorithm behaves very well in terms of meeting deadlines.</p></abstract>

With the rapid evolution of the distributed computing world in the last few years, the amount of data created and processed has fast increased to petabytes or even exabytes scale. Such huge data sets need data-intensive computing applications and impose performance requirements to the infrastructures that support them, such as high scalability, storage, fault tolerance but also efficient scheduling algorithms. This paper focuses on providing a hybrid scheduling algorithm for many task computing that addresses big data environments with few penalties, taking into consideration the deadlines and satisfying a data dependent task model. The hybrid solution consists of several heuristics and algorithms (min-min, min-max and earliest deadline first) combined in order to provide a scheduling algorithm that matches our problem. The experimental results are conducted by simulation and prove that the proposed hybrid algorithm behaves very well in terms of meeting deadlines.

A Hybrid Scheduler for Many Task Computing in Big Data Systems

International Journal of Applied Mathematics and Computer Science

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

{"article-title":"A Hybrid Scheduler for Many Task Computing in Big Data Systems"}

With the rapid evolution of the distributed computing world in the last few years, the amount of data created and processed has fast increased to petabytes or...

A Hybrid Scheduler for Many Task Computing in Big Data Systems

Online veröffentlicht: 08. Juli 2017

Seitenbereich: 385 - 399

Eingereicht: 26. Nov. 2016

Akzeptiert: 20. März 2017

DOI: https://doi.org/10.1515/amcs-2017-0027

Schlüsselwörter
many task computing, scheduling heuristics, QoS, big data systems, simulation

© by Florin Pop

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

A Hybrid Scheduler for Many Task Computing in Big Data Systems

Online veröffentlicht: 08. Juli 2017

Seitenbereich: 385 - 399

Eingereicht: 26. Nov. 2016

Akzeptiert: 20. März 2017

DOI: https://doi.org/10.1515/amcs-2017-0027

Schlüsselwörtermany task computing, scheduling heuristics, QoS, big data systems, simulation

© by Florin Pop

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Schlüsselwörter
many task computing, scheduling heuristics, QoS, big data systems, simulation