A workflow is an effective way for modeling complex applications and serves as a means for scientists and researchers to better understand the details of applications. Cloud computing enables the running of workflow applications on many types of computational resources which become available on-demand. As one of the most important aspects of cloud computing, workflow scheduling needs to be performed efficiently to optimize resources. Due to the existence of various resource types at different prices, workflow scheduling has evolved into an even more challenging problem on cloud computing. The present paper proposes a workflow scheduling algorithm in the cloud to minimize the execution cost of the deadline-constrained workflow. The proposed method, EDQWS, extends the current authors’ previous study (DQWS) and is a two-step scheduler based on divide and conquer. In the first step, the workflow is divided into sub-workflows by defining, scheduling, and removing a critical path from the workflow, similar to DQWS. The process continues until only chain-structured sub-workflows, called linear graphs, remain. In the second step which is linear graph scheduling, a new merging algorithm is proposed that combines the resulting linear graphs so as to reduce the number of used instances and minimize the overall execution cost. In addition, the current work introduces a scoring function to select the most efficient instances for scheduling the linear graphs. Experiments show that EDQWS outperforms its competitors, both in terms of minimizing the monetary costs of executing scheduled workflows and meeting user-defined deadlines. Furthermore, in more than 50% of the examined workflow samples, EDQWS succeeds in reducing the number of resource instances compared to the previously introduced DQWS method.
The development of cloud computing technology has been continuously growing since its invention and has attracted the attention of many researchers in the academia and the industry, particularly during the recent years. The majority of organizations, whether large corporate businesses or typical small companies, are moving towards employing this cutting edge technology. Using private cloud provides low cost and privacy for workflow applications execution. However, an organization's requirements to high performance resources and high capacity storage devices lead them to utilize public clouds. Public cloud leases information technology services in the form of small units and in larger scale compared to private cloud, but this model is potentially exposed to the risk of data breach and is less secure in comparison to a pure private cloud environment. The combination of public and private clouds is known as hybrid cloud, where workflow tasks can be executed on resources residing on either public or private clouds. The objective of this paper is to present a scheduling algorithm for maintaining data privacy in workflow applications, such that the budget is minimized, while the makespan limitation imposed by the user is satisfied.
In hybrid cloud model, organizations can keep their sensitive information and critical applications in the private cloud and move other data and applications to a public cloud, if necessary. To maintain data privacy in workflow applications, we present a budget constrained hybrid cloud scheduler (BCHCS) which is a static heuristic scheduling algorithm. It is able to make decisions about scheduling sensitive tasks on private cloud and uses public cloud's resources for non-sensitive tasks, such that the makespan is minimized, while the budget limitation imposed by the user is satisfied. Experimental results show that the proposed method guarantees the execution of sensitive tasks on private cloud while achieving at least 7 percent lower makespan and higher success rate in comparison to similar existing techniques.
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