qo s aware scheduling of workflows in cloud computing environments
TRANSCRIPT
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QOS-AWARE SCHEDULING OF WORKFLOWS IN CLOUD COMPUTING
ENVIRONMENTS
ABSTRACT:
Cloud Computing has emerged as a service model that enables on-demand network
access to a large number of available virtualized resources and applications with a minimal
management effort and a minor price. The spread of Cloud Computing technologies allowed
dealing with complex applications such as Scientific Workflows, which consists of a set of
intensive computational and data manipulation operations. Cloud Computing helps such
Workflows to dynamically provision compute and storage resources necessary for the
execution of its tasks thanks to the elasticity asset of these resources. However, the dynamic
nature of the Cloud incurs new challenges, as some allocated resources may be overloaded or
out of access during the execution of the Workflow. Moreover, for data intensive tasks, the
allocation strategy should consider the data placement constraints since data transmission
time can increase notably in this case which implicates the increase of the overall completion
time and cost of the Workflow. Likewise, for intensive computational tasks, the allocation
strategy should consider the type of the allocated virtual machines, more specifically its CPU,
memory and network capacities. Yet, a critical challenge is how to efficiently schedule the
Workflow tasks on Cloud resources to optimize its overall quality of service. In this paper,
we propose a QoS-aware algorithm for Scientific Workflows scheduling that aims to improve
the overall quality of service (QoS) by considering the metrics of execution time, data
transmission time, cost, resources availability and data placement constraints. We extended
the Parallel Cat Swarm Optimization (PCSO) algorithm to implement our proposed approach.
We tested our algorithm within two sample Workflows of different scales and we compared
the results to those given by the standard PSO, the CSO and the PCSO algorithms. The
results show that our proposed algorithm improves the overall quality of service of the tested
Workflows.