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Wyszukujesz frazę "Cloud Algorithm" 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:
An ANN-based scalable hashing algorithm for computational clouds with schedulers
Autorzy:
Tchórzewski, Jacek
Jakóbik, Agnieszka
Iacono, Mauro
Powiązania:
https://bibliotekanauki.pl/articles/2055176.pdf
Data publikacji:
2021
Wydawca:
Uniwersytet Zielonogórski. Oficyna Wydawnicza
Tematy:
hashing algorithm
artificial neural network
scalable cryptography algorithm
computational cloud
task scheduler
algorytm haszowania
sztuczna sieć neuronowa
algorytm kryptograficzny
chmura obliczeniowa
Opis:
The significant benefits of cloud computing (CC) resulted in an explosion of their usage in the last several years. From the security perspective, CC systems have to offer solutions that fulfil international standards and regulations. In this paper, we propose a model for a hash function having a scalable output. The model is based on an artificial neural network trained to mimic the chaotic behaviour of the Mackey–Glass time series. This hashing method can be used for data integrity checking and digital signature generation. It enables constructing cryptographic services according to the user requirements and time constraints due to scalable output. Extensive simulation experiments are conduced to prove its cryptographic strength, including three tests: a bit prediction test, a series test, and a Hamming distance test. Additionally, flexible hashing function performance tests are run using the CloudSim simulator mimicking a cloud with a global scheduler to investigate the possibility of idle time consumption of virtual machines that may be spent on the scalable hashing protocol. The results obtained show that the proposed hashing method can be used for building light cryptographic protocols. It also enables incorporating the integrity checking algorithm that lowers the idle time of virtual machines during batch task processing.
Źródło:
International Journal of Applied Mathematics and Computer Science; 2021, 31, 4; 697--712
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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