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Wyszukujesz frazę "Computing simulation" wg kryterium: Temat


Wyświetlanie 1-2 z 2
Tytuł:
Using a vision cognitive algorithm to schedule virtual machines
Autorzy:
Zhao, J.
Mhedheb, Y.
Tao, J.
Jrad, F.
Liu, Q.
Streit, A.
Powiązania:
https://bibliotekanauki.pl/articles/330838.pdf
Data publikacji:
2014
Wydawca:
Uniwersytet Zielonogórski. Oficyna Wydawnicza
Tematy:
cloud computing
vision cognitive algorithm
VM scheduling
simulation
chmura obliczeniowa
algorytm poznawczy
szeregowanie
symulacja
Opis:
Scheduling virtual machines is a major research topic for cloud computing, because it directly influences the performance, the operation cost and the quality of services. A large cloud center is normally equipped with several hundred thousand physical machines. The mission of the scheduler is to select the best one to host a virtual machine. This is an NP-hard global optimization problem with grand challenges for researchers. This work studies the Virtual Machine (VM) scheduling problem on the cloud. Our primary concern with VM scheduling is the energy consumption, because the largest part of a cloud center operation cost goes to the kilowatts used. We designed a scheduling algorithm that allocates an incoming virtual machine instance on the host machine, which results in the lowest energy consumption of the entire system. More specifically, we developed a new algorithm, called vision cognition, to solve the global optimization problem. This algorithm is inspired by the observation of how human eyes see directly the smallest/largest item without comparing them pairwisely. We theoretically proved that the algorithm works correctly and converges fast. Practically, we validated the novel algorithm, together with the scheduling concept, using a simulation approach. The adopted cloud simulator models different cloud infrastructures with various properties and detailed runtime information that can usually not be acquired from real clouds. The experimental results demonstrate the benefit of our approach in terms of reducing the cloud center energy consumption.
Źródło:
International Journal of Applied Mathematics and Computer Science; 2014, 24, 3; 535-550
1641-876X
2083-8492
Pojawia się w:
International Journal of Applied Mathematics and Computer Science
Dostawca treści:
Biblioteka Nauki
Artykuł
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
    Wyświetlanie 1-2 z 2

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