Deep‐Q learning‐based heterogeneous earliest finish time scheduling algorithm for scientific workflows in cloud

Kaur, Avinash; Singh, Parminder; Batth, Ranbir Singh; Lim, Chee Peng

doi:10.1002/spe.2802

Cited by 26 publications

(11 citation statements)

References 42 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Avinash Kaur et al [15] have proposed a new workflow scheduling scheme by integrating the Deep Q-learning mechanism and the HEFT algorithm is called DQ-HEFT. The scheme is considered the most common heuristic scheduling in literature.…”

Section: Related Workmentioning

confidence: 99%

Multiobjective Task Scheduling in Cloud Environment Using Decision Tree Algorithm

et al. 2022

View full text Add to dashboard Cite

In recent years, Cloud computing has been developed and become the foundation of a wide range of applications. It allows users to access a catalog of standardized services and respond to their business needs flexibly and adaptively, in the event of unforeseen demands, paying solely for the consumption they have made. Task scheduling problem is considered one of the most critical cloud computing challenges. The problem refers to how to reasonably order and allocate the applications tasks provided by the users to be executed on virtual machines. Furthermore, the quality of scheduling performance has a direct effect on customer satisfaction. The task scheduling problem in cloud computing must be more accurately described in order to improve scheduling performance. In this paper, a multi-objective task scheduling algorithm is proposed based on the decision tree in a heterogenous environment. We introduce a new Task Scheduling-Decision Tree (TS-DT) algorithm for allocating and executing an application's task. To evaluate the performance of the proposed TS-DT algorithm, a comparative study was conducted among the existing algorithms; Heterogeneous Earliest Finish Time (HEFT), Technique for Order of Preference by Similarity to Ideal Solution that incorporates the Entropy Weight Method (TOPSIS-EWM), and combining Q-Learning with the Heterogeneous Earliest Finish Time (QL-HEFT). Our results show that the proposed TS-DT algorithm outperforms the existing HEFT, TOPSIS-EWM, and QL-HEFT algorithms by reducing makespan by 5.21%, 2.54%, and 3.32%, respectively, improving resource utilization by 4.69%, 6.81%, and 8.27%, respectively, and improving load balancing by 33.36%, 19.69%, and 59.06%, respectively in average.

show abstract

Section: Related Workmentioning

confidence: 99%

Multiobjective Task Scheduling in Cloud Environment Using Decision Tree Algorithm

et al. 2022

View full text Add to dashboard Cite

show abstract

“…Some of the real-world applications, such as Epigenomics (I/O intensive workflow) and Montage (compute intensive workflow), are taken for analysis with 100 tasks, as shown in Figures 8 and 9. Montage has a high communication to computation ratio (𝐶𝐶𝑅) [29][30][31], and epigenomics has a low communication to computation ratio (𝐶𝐶𝑅). The success rate is The graph of SR as in Equation ( 16) is captured with around 50 runs of simulations.…”

Section: Resultsmentioning

confidence: 99%

Cost and Time Economical Planning Algorithm for Scientific Workflows in Cloud Computing

Hilda

Chandrasekaran

2021

Future Internet

View full text Add to dashboard Cite

A heterogeneous system can be portrayed as a variety of unlike resources that can be locally or geologically spread, which is exploited to implement data-intensive and computationally intensive applications. The competence of implementing the scientific workflow applications on heterogeneous systems is determined by the approaches utilized to allocate the tasks to the proper resources. Cost and time necessity are evolving as different vital concerns of cloud computing environments such as data centers. In the area of scientific workflows, the difficulties of increased cost and time are highly challenging, as they elicit rigorous computational tasks over the communication network. For example, it was discovered that the time to execute a task in an unsuited resource consumes more cost and time in the cloud data centers. In this paper, a new cost- and time-efficient planning algorithm for scientific workflow scheduling has been proposed for heterogeneous systems in the cloud based upon the Predict Optimistic Time and Cost (POTC). The proposed algorithm computes the rank based not only on the completion time of the current task but also on the successor node in the critical path. Under a tight deadline, the running time of the workflow and the transfer cost are reduced by using this technique. The proposed approach is evaluated using true cases of data-exhaustive workflows compared with other algorithms from written works. The test result shows that our proposed method can remarkably decrease the cost and time of the experimented workflows while ensuring a better mapping of the task to the resource. In terms of makespan, speedup, and efficiency, the proposed algorithm surpasses the current existing algorithms—such as Endpoint communication contention-aware List Scheduling Heuristic (ELSH)), Predict Earliest Finish Time (PEFT), Budget-and Deadline-constrained heuristic-based upon HEFT (BDHEFT), Minimal Optimistic Processing Time (MOPT) and Predict Earlier Finish Time (PEFT)—while holding the same time complexity.

show abstract

“…Kaur et al 28 proposed a deep‐Q learning‐based heterogeneous earliest‐finish‐time (DQ‐HEFT) algorithm for a cloud environment. This strategy achieves significantly better speed metrics and makespan.…”

Section: Literature Surveymentioning

confidence: 99%

An efficient list‐based task scheduling algorithm for heterogeneous distributed computing environment

2022

View full text Add to dashboard Cite

The task scheduling in heterogeneous distributed computing systems plays a crucial role in reducing the makespan and maximizing resource utilization. The diverse nature of the devices in heterogeneous distributed computing systems intensifies the complexity of scheduling the tasks. To overcome this problem, a new list-based static task scheduling algorithm namely Deadline-Aware-Longest-Path-of-all-Predecessors (DA-LPP) is being proposed in this article. In the prioritization phase of the DA-LPP algorithm, the path length of the current task from all its predecessors at each level is computed and among them, the longest path length value is assigned as the rank of the task. This strategy emphasizes the tasks in the critical path. This well-optimized prioritization phase leads to an observable minimization in the makespan of the applications. In the processor selection phase, the DA-LPP algorithm implements the improved insertion-based policy which effectively utilizes the unoccupied leftover free time slots of the processors which improve resource utilization, further least computation cost allocation approach is followed to minimize the overall computation cost of the processors and parental prioritization policy is incorporated to further reduce the scheduling length. To demonstrate the robustness of the proposed algorithm, a synthetic graph generator is used in this experiment to generate a huge variety of graphs. Apart from the synthetic graphs, real-world application graphs like Montage, LIGO, Cybershake, and Epigenomic are also considered to grade the performance of the DA-LPP algorithm. Experimental results of the DA-LPP algorithm show improvement in performance in terms of scheduling length ratio, makespan reduction rate , and resource reduction rate when compared with other algorithms like DQWS, DUCO, DCO and EPRD. The results reveal that for 1000 task set with deadline equals to two times of the critical path, the scheduling length ratio of the DA-LPP algorithm is better than

show abstract

Deep‐Q learning‐based heterogeneous earliest finish time scheduling algorithm for scientific workflows in cloud

Cited by 26 publications

References 42 publications

Multiobjective Task Scheduling in Cloud Environment Using Decision Tree Algorithm

Multiobjective Task Scheduling in Cloud Environment Using Decision Tree Algorithm

Cost and Time Economical Planning Algorithm for Scientific Workflows in Cloud Computing

An efficient list‐based task scheduling algorithm for heterogeneous distributed computing environment

Contact Info

Product

Resources

About