Multiple DAG Applications Scheduling on a Cluster of Processors

Boregowda, Uma; Chakravarthy, Venugopal

doi:10.5121/csit.2014.4306

Cited by 3 publications

(1 citation statement)

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“…It uses a different approach to rank the ready tasks comparing to RANKU [10] and OWM [6]. The final study [11] presents a framework to receive submitted DAGs and schedules tasks on allotted processors, with the objectives of maximizing resource utilization and minimizing overall completion time. In this framework, a method is developed to find minimum, optimal and maximum number of processors that can be allocated to a DAG; it results in finding configurations that allow sharing available processors among multiple DAGs.…”

Section: Related Studiesmentioning

confidence: 99%

Online Multiple Workflow Scheduling under Privacy and Deadline in Hybrid Cloud Environment

Sharif

Taheri

Zomaya

et al. 2014

2014 IEEE 6th International Conference on Cloud Computing Technology and Science

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Organizations overcome resource shortages by utilizing the multiple services of cloud providers. This leads to sharing resources among various public and private clouds in order to improve the performance while executing the organization's complex workflow systems. Executing multiple workflows in such a hybrid environment needs an effective mapping between workflow's tasks and cloud resources that considers the tradeoff between budget and time. There is also a challenge when organizations are forced to deploy workflow's tasks on public resources to execute the tasks before their requested deadlines without violating customers' privacy. In recent years, several online and static approaches were presented to schedule single or multiple workflows considering deadline and budget in cloud environments. However, these studies neglect the privacy constraint along with other SLAs such as deadline and budget. In this paper, we present two online algorithms to schedule multiple workflows under deadline and privacy constraints, while considering the dynamic nature of hybrid cloud environment. The proposed algorithms were evaluated with a series of simulation as well as real experiments using real-life privacy constrained healthcare workflows. Our two algorithms use different methods to rank the tasks: one utilises a novel technique for ranking, the other uses a similar approach to current existing studies. Results show that the novel approach outperforms the current existing ranking methods.

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Section: Related Studiesmentioning

confidence: 99%