Exploration of Secured Workflow Scheduling Models in Cloud Environment: A Survey

Francis, Akindipe Olusegun; Bugingo, Emmanuel; Zhang, Defu; Zheng, Wei; Qin, Yingsheng; Zhang, Dongzhan

doi:10.1109/cbd.2018.00022

Cited by 8 publications

(7 citation statements)

References 32 publications

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“…With increasing deadline factor, the deadline deviation of ROSA increases sharply, as it uses many high-performance and new VMs to reduce the makespan of the workflow. NOSF and RMWS also show trends of increasing deadline deviation when ∈ [1,4]; however, this situation is alleviated under the condition of ∈ [5,8]. This shows that the two algorithms can effectively allocate VMs for tasks in workflow scheduling under various deadline constraints.…”

Section: Variance Of Deadline Factormentioning

confidence: 93%

“…6 (c), the resource utilization rate and success rate of these algorithms are shown. Although the success rate of ROSA under the loose deadline constraint ( ∈ [5,8]) is close to 100%, its resource utilization rate is low (24.88%). This is because ROSA fails to fully utilize existing VMs when scheduling tasks; more tasks are allocated to the new VMs.…”

Section: Variance Of Deadline Factormentioning

confidence: 97%

“…This shows that each algorithm will give priority to assign tasks to low-performance VMs to reduce the lease cost when the deadline is further away. However, the cost of ROSA does not change significantly when ∈ [5,8], and the average value is 10.7*10 4 . In comparison, both NOSF and RMWS achieve lower costs.…”

Section: Variance Of Deadline Factormentioning

confidence: 98%

“…Moreover, the difference is more pronounced when the deadline factor is greater than 4. Under the loose deadline constraint ( ∈ [5,8] ), for NOSF and RMWS, the average resource utilization rates were 83.66% and 87.44%, respectively, and the average success rates were 80.28% and 85.8%, respectively. Fig.…”

Section: Variance Of Deadline Factormentioning

confidence: 99%

“…However, many studies on workflow scheduling fail to fully consider the characteristics of cloud computing, such as its heterogeneity, unlimited resources, and fluctuating performance. Most studies on this topic have emphasized the scheduling of a single workflow in a cloud, whereas few studies have focused on multiple-workflow scheduling in real time [5] [6]. Due to the sharing method, tasks in multiple workflows will be implemented on the same resource simultaneously.…”

Section: Introductionmentioning

confidence: 99%

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WITHDRAWN: Real-time Multiple-Workflow Scheduling in Cloud Environment

Gao

et al. 2021

Preprint

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With the development of cloud computing, an increasing number of applications in different fields have been deployed to the cloud. In this process, the real-time scheduling of multiple workflows composed of tasks from these different applications must consider various influencing factors which strongly affect scheduling performance. This paper proposes a real-time multiple-workflow scheduling (RMWS) scheme to schedule workflows dynamically with minimum cost under different deadline constraints. Due to the uncertainty of workflow arrival time and specification, RMWS dynamically allocates tasks and divides the scheduling process into three stages. First, when a new workflow arrives, the latest start time and the latest finish time of each task are calculated according to the deadline, and the subdeadline of each task is obtained by probabilistic upward ranking. Then, each ready task is allocated according to its subdeadline and the increased cost of the virtual machine (VM). Meanwhile, only one waiting task can be assigned to each VM to reduce delay fluctuations. Finally, when the task is completed on the assigned VM, all the parameters of the relevant tasks are updated before allocating them to appropriate VMs. The experimental results based on four real-world workflow traces show that the proposed algorithm is superior to two state-of-the-art algorithms in terms of total rental cost, resource utilization, success rate and deadline deviation under different conditions.

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Section: Variance Of Deadline Factormentioning

confidence: 93%

Section: Variance Of Deadline Factormentioning

confidence: 97%

Section: Variance Of Deadline Factormentioning

confidence: 98%

Section: Variance Of Deadline Factormentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

WITHDRAWN: Real-time Multiple-Workflow Scheduling in Cloud Environment

Gao

et al. 2021

Preprint

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Efficient workflow scheduling in cloud computing for security maintenance of sensitive data

Hammed

Arunkumar

2019

Int J Communication

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The seamless conveying of information's considering internet as a virtual space, connecting all users from the world is cloud computing. In simpler terms it is a means of storing and accessing information over internet irrespective of place and time. The cloud proceeds with the process of storing, retrieving and allowing access, on demand as a paid service. As the benefits of cloud is attractive, the number of users adopting to the cloud also increases, so the workflow management becomes tedious and more challenging in the cloud. The decision to provide with an enhanced work flow management requires proper scheduling recollecting value of the information. So the paper proposes with an efficient method of managing the work flow considering the value of the information by categorizing between the much value and non-value information's and framing the algorithm that functions as scheduler using the parallel implementation in natural process of genetic algorithm (GA) with secured frame work for the information's of high value. The proposed work shows an overwhelmed performance than the conventional techniques in the time taken for the execution and its total cost. The performance of the scheduler is validated in the WORKFLOWSIM on the grounds of time of execution and total cost.

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