A Dynamic Scheduling Method for Collaborated Cloud with Thick Clients
Pham Phuoc Hung1,
Golam Alam2, Nguyen Hai3, Quan Tho3, and Eui-Nam
Huh4
1Department of Computer Science,
Kent State University, USA
2Department of Computer Science and Engineering,
BRAC University, Bangladesh
3Ho Chi Minh City University of Technology, Vietnam National University,
Vietnam
4Department of Computer Engineering, Kyung Hee
University, Korea
Abstract: Nowadays, the emergence of computation-intensive
applications brings benefits to individuals and the commercial organization.
However, it still faces many challenges due to the limited processing capacity
of the local computing resources. Besides, the local computing resources
require a lot of finance and human forces. This problem, fortunately, has been
made less severe, thanks to the recent adoption of Cloud Computing (CC)
platform. CC enables offloading heavy processing tasks up to the
"cloud", leaving only simple jobs to the user-end capacity-limited
clients. Conversely, as CC is a pay-as-you-go model, it is necessary to find
out an approach that guarantees the highly efficient execution time of cloud
systems as well as the monetary cost for cloud resource use. Heretofore, a lot of
research studies have been carried out, trying to eradicate problems, but they
have still proved to be trivial. In this paper, we present a novel
architecture, which is a collaboration of the computing resources on cloud
provider side and the local computing resources (thick clients) on client side.
In addition, the main factor of this framework is the dynamic genetic task
scheduling to globally minimize the completion time in cloud service, while
taking into account network condition and cloud cost paid by customers. Our
simulation and comparison with other scheduling approaches show that the
proposal produces a reasonable performance together with a noteworthy cost
saving for cloud customers.
Keywords: Genetic, cloud computing, task
scheduling, thick client, distributed system.
Received September
10, 2014; accepted January 20, 2016