Autor:

Vasiliu, Laura ; Pop, Florin ; Negru, Catalin ; Mocanu, Mariana ; Cristea, Valentin ; Kołodziej, Joanna

Współtwórca:

Korbicz, Józef (1951- ) - red. ; Uciński, Dariusz - red.

Tytuł:

A hybrid scheduler for many task computing in big data systems

Tytuł publikacji grupowej:

AMCS, Volume 27 (2017)

Temat i słowa kluczowe:

many task computing ; scheduling heuristics ; QoS ; big data systems ; simulation

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.

Wydawca:

Zielona Góra: Uniwersytet Zielonogórski

Data wydania:

2017

Typ zasobu:

artykuł

DOI:

10.1515/amcs-2017-0027

Strony:

385-399

Źródło:

AMCS, volume 27, number 2 (2017) ; kliknij tutaj, żeby przejść

Jezyk:

eng

Licencja CC BY 4.0:

kliknij tutaj, żeby przejść

Prawa do dysponowania publikacją:

Biblioteka Uniwersytetu Zielonogórskiego