A New Scheduling Method for Workflows on Cloud Computing

Main Article Content

Seyed Ebrahim Dashti
Amir masoud Rahmani


Cloud computing has recently become a very popular topic. Nowadays, with the increasing demand for process automation in the
cloud, the investigation on cloud workflow scheduling strategies is becoming a significant issue. Majority of existing workflow scheduling
algorithms consider only compute resources that usually cannot be provisioned on demand size of workflows or not released to the environment
until the workflow execution completes. That is why the performance of these algorithms is being decreased and time and cost of them is being
increased. In this paper, we present a new workflow scheduling method based on Ant Colony Optimization algorithm in order to reduce this
scheduling overhead with considering the above problems in our dynamic environment. Furthermore, we do consider these problems and various
type VMs during the execution dynamically based on Amazon EC2. Also in comparison with state of the art in large-scale scheduling method,
our datasets are based on real workflow applications with maximum 100 nodes. The results show that performance of our algorithm is
significantly better than Greedy Randomized Adaptive Search Procedure (GRASP) and scalable for increasing nodes of workflow.


Keyword: workflow scheduling, cloud computing, VM allocation, Dynamic VM, user constraint, ACO (Ant Colony Optimization).


Download data is not yet available.

Article Details