Directed Acyclic Graph Based Task Scheduling Algorithm for Heterogeneous Systems

Tariq, Rehan; Aadil, Farhan; Malik, Muhammad Faizan; Ejaz, Sadaf; Khan, Madad; Khan, Muhammad Fahad

doi:10.1007/978-3-030-01057-7_69

Cited by 9 publications

(3 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…After removing terminating self-loop edges, it was found these graphs had a Directed Acyclic Graph (DAG) [24] structure. Since DAG is also feasible for describing data process networks [25], it is a suitable structure for characterizing distributed telemedicine systems as well.…”

Section: Evaluation Of State Graphsmentioning

confidence: 99%

A graph-based data quality analysis in distributed telemedicine systems

Jánki

2022

Pollack

View full text Add to dashboard Cite

Telemedicine is one of the most rapidly developing areas of healthcare and it plays an increasing role in modern medicine. As the amount of data and demand for features increase, the data paths are becoming ever-more complex. Owing to this, it is vital in telemedicine to find a proper balance between consistency and availability under any given circumstances. However, making a trade-off can significantly influence the quality of the data. This study seeks to get an in-depth view of the problem by considering a real-world telemedicine use-case and elaborating the formal system specification of the scenario. After evaluating the specification, the constructed state graph is examined using graph coloring and other graph algorithms.

show abstract

Section: Evaluation Of State Graphsmentioning

confidence: 99%

A graph-based data quality analysis in distributed telemedicine systems

Jánki

2022

Pollack

View full text Add to dashboard Cite

show abstract

“…The proposed scheduling algorithms can be classified as heuristic-based and metaheuristic. Heuristic-based scheduling algorithms typically find a scheduling scheme in polynomial time based on incomplete information [39]- [44]. Topcuoglu et al in [38] proposed two classical algorithms: Heterogeneous Earliest Finish Time (HEFT) and Critical Path On a Processor (CPOP).…”

Section: Related Workmentioning

confidence: 99%

Energy-Aware Task Scheduling on Heterogeneous Computing Systems With Time Constraint

et al. 2020

View full text Add to dashboard Cite

As a technique to help achieve high performance in parallel and distributed heterogeneous computing systems, task scheduling has attracted considerable interest. In this paper, we propose an effective Cuckoo Search algorithm based on Gaussian random walk and Adaptive discovery probability which combined with a cost-to-time ratio Modification strategy (GACSM), to address task scheduling on heterogeneous multiprocessor systems using Dynamic Voltage and Frequency Scaling (DVFS). First, to overcome the shortcomings of poor performance in exploitation of the cuckoo search algorithm, we use chaos variables to initialize populations to maintain the population diversity, a Gaussian random walk strategy to balance the exploration and exploitation capabilities of the algorithm, and an adaptive discovery probability strategy to improve population diversity. Then, we apply the improved Cuckoo Search (CS) algorithm to assign tasks to resources, and a widely used downward rank heuristic strategy to find the corresponding scheduling sequence. Finally, we apply a cost-to-time ratio improvement strategy to further improve the performance of the improved CS algorithm. Extensive experiments are conducted to evaluate the effectiveness and efficiency of our method. The results validate our approach and show its superiority in comparison with the state-of-the-art methods. INDEX TERMS Task scheduling, DVFS, cuckoo search algorithm, heterogeneous multiprocessor system.

show abstract

“…To analyze the sequence of the tasks, directed acyclic graphs (DAGs) have been adopted. DAGs have been widely used in the scheduling for a task with dependency constraints [193][194][195]. Unlike existing approaches that represent tasks as vertices on the DAG, data attributes associated with the cDMLA data model are the vertices, defined as v attr .…”

Section: Dependency Representationmentioning

confidence: 99%

Timely dropout prediction with learner behavior data

Li¹

View full text Add to dashboard Cite

I wish to express my greatest gratitude to my advisor Dr. Andy W. H. Khong for his continual support during my Ph.D. Study. His guidance, patience, and encouragement have tremendously benefited me with the skillset and mindset on research. I would like to thank all the members in the research group. It is their kind help and support that have made me to pave towards the completion of my study. I would like to express my gratitude to my family. I would like to honor my father, who gave me the power to persevere. I would like to thank my mother. Without her tremendous understanding and support in the past few years, it would be impossible for me to complete my study. I would like to thank my wife. During my Ph.D. studies, we have been through various life events together. You make me feel loved every day. I'm so blessed of having you. Finally, to my unborn baby: your mom and I are ready to embark on the new expedition with you!

show abstract

Directed Acyclic Graph Based Task Scheduling Algorithm for Heterogeneous Systems

Cited by 9 publications

References 21 publications

A graph-based data quality analysis in distributed telemedicine systems

A graph-based data quality analysis in distributed telemedicine systems

Energy-Aware Task Scheduling on Heterogeneous Computing Systems With Time Constraint

Timely dropout prediction with learner behavior data

Contact Info

Product

Resources

About