Energy-aware scientific workflow scheduling in cloud environment

Choudhary, Anita; Govil, Mahesh Chandra; Singh, Girdhari; Awasthi, Lalit Kumar; Pilli, Emmanuel S.

doi:10.1007/s10586-022-03613-3

Cited by 15 publications

(4 citation statements)

References 68 publications

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“…The algorithms in References 51,57 use the same technique to group the tasks that have a parent‐child relationship but with a slight difference in Reference 57, where the algorithm while grouping of tasks gives priority to the parent‐child pair having higher communication cost than its peer child thus resulting in further energy saving. The authors in Reference 61 have used a different technique and named it ‘Runtime balanced clustering’ for the clustering of tasks. The idea behind this approach is to balance the execution time of cluster(s) formed in this process such that a load imbalance problem does not arise.…”

Section: Analytical Discussionmentioning

confidence: 99%

“…Similarly, the algorithm in Reference 51 identifies clusters of the same type of tasks to be allocated to a single processor such that the task deadlines are not violated, whereas in Reference 57, the algorithm firstly calculates the optimal frequency for energy conservation and reliability for each task on all available VMs and then secondly, mapping of a task from each cluster is done to the highest reliable VM. Another technique 61 in which, based upon the sub‐deadline assigned to each task, each task is mapped to a VM on which the power consumption of running a task is minimal.…”

Section: Analytical Discussionmentioning

confidence: 99%

“…In Reference 61, the authors have proposed a heuristic‐based approach for scheduling workflow applications in a cloud computing environment. The authors have designed a scheduling framework consisting of three layers, namely: the user layer, the scheduling layer, and the resource layer.…”

Section: Taxonomy Of Energy‐efficient Workflow Scheduling Approachesmentioning

confidence: 99%

See 2 more Smart Citations

A survey on energy‐efficient workflow scheduling algorithms in cloud computing

Verma,

Maurya,

Yadav

2023

Softw Pract Exp

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The advancements in computing and storage capabilities of machines and their fusion with new technologies like the Internet of Thing (IoT), 5G networks, and artificial intelligence, to name a few, has resulted in a paradigm shift in the way computing is done in a cloud environment. In addition, the ever‐increasing user demand for cloud services and resources has resulted in cloud service providers (CSPs) expanding the scale of their data center facilities. This has increased energy consumption leading to more carbon dioxide emission levels. Hence, it becomes all the more important to design scheduling algorithms that optimize the use of cloud resources with minimum energy consumption. This paper surveys state‐of‐the‐art algorithms for scheduling workflow tasks to cloud resources with a focus on reducing energy consumption. For this, we categorize different workflow scheduling algorithms based on the scheduling approaches used and provide an analytical discussion of the algorithms covered in the paper. Further, we provide a detailed classification of different energy‐efficient strategies used by CSPs for energy saving in data centers. Finally, we describe some of the popular real‐world workflow applications as well as highlight important emerging trends and open issues in cloud computing for future research directions.

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Section: Analytical Discussionmentioning

confidence: 99%

Section: Analytical Discussionmentioning

confidence: 99%

See 1 more Smart Citation

A survey on energy‐efficient workflow scheduling algorithms in cloud computing

Verma,

Maurya,

Yadav

2023

Softw Pract Exp

View full text Add to dashboard Cite

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“…Deadline constraints, energy consumption are crucial challenges for scheduling in cloud paradigm. Authors in [21] proposed two stage scheduling model in which task clustering for fine grained tasks which were merged as a workflow and identifying critical path applications to execute workflows based on Dynamic Voltage Frequency Scaling (DVFS) technique. Extensive simulations conducted on Workflowsim using realtime scientific workflows.…”

Section: A Motivations and Contributionsmentioning

confidence: 99%

Multi Objective Prioritized Workflow Scheduling Using Deep Reinforcement Based Learning in Cloud Computing

Mangalampalli,

Hashmi,

Gupta

et al. 2024

IEEE Access

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Workflow Scheduling is a huge challenge in cloud paradigm as many number of workflows dynamically generated from various heterogeneous resources and task dependencies in each workflow varies from each other. Therefore, if a workflow with more number of dependencies is not scheduled onto an appropriate Virtual Machine i.e. with low processing capacity which leads to delay in executing workflows and it results in increase of makespan, cost, energy consumption. In order to effectively schedule complex workflows i.e. with more task dependencies, we propose a novel multi objective workflow scheduling algorithm using Deep reinforcement Learning. Initially, priorities of all workflows calculated based on their dependencies and then calculated priorities of VMs based on electricity cost at datacenters to map workflows onto precise VMs. These priorities are fed to scheduler which uses Deep Q-Network model to dynamically schedule tasks by considering both priorities of tasks and VMs. Extensive simulations carried out on workflowsim by considering realtime scientific workflows (Montage, cybershake, Epigenomics, LIGO). Our proposed MOPWSDRL compared against existing state of art approaches i.e. Heterogeneous Earliest First Deadline, Cat Swarm Optimization, Ant Colony Optimization. Results revealed that our proposed MOPDSWRL outperforms existing state of art algorithms by minimizing makespan, energy consumption.

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