- Tytuł:
- A hybrid scheduler for many task computing in big data systems
- Autorzy:
-
Vasiliu, L.
Pop, F.
Negru, C.
Mocanu, M.
Cristea, V.
Kolodziej, J. - Powiązania:
- https://bibliotekanauki.pl/articles/907647.pdf
- Data publikacji:
- 2017
- Wydawca:
- Uniwersytet Zielonogórski. Oficyna Wydawnicza
- Tematy:
-
many task computing
scheduling heuristics
QoS
big data system
simulation
obliczenia wielofunkcyjne
szeregowanie zadań
duży zbiór danych - Opis:
- 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.
- Źródło:
-
International Journal of Applied Mathematics and Computer Science; 2017, 27, 2; 385-399
1641-876X
2083-8492 - Pojawia się w:
- International Journal of Applied Mathematics and Computer Science
- Dostawca treści:
- Biblioteka Nauki
Artykuł