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Artículos de revistas sobre el tema "DAG task"

Autor: Grafiati

Publicado: 25 de mayo de 2024

Última modificación: 29 de julio de 2025

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1

Liu, Zhiguo, Luxi Zhang, Lin Wang, Xiaoqi Dong, and Junlin Rong. "Research on Multi-DAG Satellite Network Task Scheduling Algorithm Based on Cache-Composite Priority." Electronics 13, no. 4 (2024): 763. http://dx.doi.org/10.3390/electronics13040763.

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The problem of multiple DAGs sharing satellite constellation resources has gradually attracted widespread attention. Due to the limited computing resources and energy consumption of satellite networks, it is necessary to formulate a reasonable multi-DAG task scheduling scheme to ensure the fairness of each workflow under the premise of considering latency and energy consumption. Therefore, in this paper, we propose a multi-DAG satellite network task scheduling algorithm based on cache-composite priority under the Software-Defined Networking satellite network architecture. The basic idea of thi

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2

Zhao, Yao, Jian Dong, Hongwei Liu, Jin Wu, and Yanxin Liu. "Performance Improvement of DAG-Aware Task Scheduling Algorithms with Efficient Cache Management in Spark." Electronics 10, no. 16 (2021): 1874. http://dx.doi.org/10.3390/electronics10161874.

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Directed acyclic graph (DAG)-aware task scheduling algorithms have been studied extensively in recent years, and these algorithms have achieved significant performance improvements in data-parallel analytic platforms. However, current DAG-aware task scheduling algorithms, among which HEFT and GRAPHENE are notable, pay little attention to the cache management policy, which plays a vital role in in-memory data-parallel systems such as Spark. Cache management policies that are designed for Spark exhibit poor performance in DAG-aware task-scheduling algorithms, which leads to cache misses and perf

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3

Cai, Lingfeng, Xianglin Wei, Changyou Xing, Xia Zou, Guomin Zhang, and Xiulei Wang. "Failure-resilient DAG task scheduling in edge computing." Computer Networks 198 (October 2021): 108361. http://dx.doi.org/10.1016/j.comnet.2021.108361.

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4

Jiang, Yuyi, Zhiqing Shao, Yi Guo, Huanhuan Zhang, and Kun Niu. "DRSCRO: A Metaheuristic Algorithm for Task Scheduling on Heterogeneous Systems." Mathematical Problems in Engineering 2015 (2015): 1–20. http://dx.doi.org/10.1155/2015/396582.

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An efficient DAG task scheduling is crucial for leveraging the performance potential of a heterogeneous system and finding a schedule that minimizes themakespan(i.e., the total execution time) of a DAG is known to be NP-complete. A recently proposed metaheuristic method, Chemical Reaction Optimization (CRO), demonstrates its capability for solving NP-complete optimization problems. This paper develops an algorithm named Double-Reaction-Structured Chemical Reaction Optimization (DRSCRO) for DAG scheduling on heterogeneous systems, which modifies the conventional CRO framework and incorporates C

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5

Vucha, Mahendra, and Arvind Rajawat. "Dynamic Task Distribution Model for On-Chip Reconfigurable High Speed Computing System." International Journal of Reconfigurable Computing 2015 (2015): 1–12. http://dx.doi.org/10.1155/2015/783237.

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Modern embedded systems are being modeled as Reconfigurable High Speed Computing System (RHSCS) where Reconfigurable Hardware, that is, Field Programmable Gate Array (FPGA), and softcore processors configured on FPGA act as computing elements. As system complexity increases, efficient task distribution methodologies are essential to obtain high performance. A dynamic task distribution methodology based on Minimum Laxity First (MLF) policy (DTD-MLF) distributes the tasks of an application dynamically onto RHSCS and utilizes available RHSCS resources effectively. The DTD-MLF methodology takes th

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6

Voudouris, Petros, Per Stenström, and Risat Pathan. "Federated Scheduling of Sporadic DAGs on Unrelated Multiprocessors." ACM Transactions on Embedded Computing Systems 20, no. 5s (2021): 1–25. http://dx.doi.org/10.1145/3477018.

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This paper presents a federated scheduling algorithm for implicit-deadline sporadic DAGs that execute on an unrelated heterogeneous multiprocessor platform. We consider a global work-conserving scheduler to execute a single DAG exclusively on a subset of the unrelated processors. Formal schedulability analysis to find the makespan of a DAG on its dedicated subset of the processors is proposed. The problem of determining each subset of dedicated unrelated processors for each DAG such that the DAG meets its deadline (i.e., designing the federated scheduling algorithm) is tackled by proposing a n

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7

Zhai, Wenzheng, Yue-Li Hu, and Feng Ran. "CQPSO scheduling algorithm for heterogeneous multi-core DAG task model." Modern Physics Letters B 31, no. 19-21 (2017): 1740050. http://dx.doi.org/10.1142/s0217984917400504.

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Efficient task scheduling is critical to achieve high performance in a heterogeneous multi-core computing environment. The paper focuses on the heterogeneous multi-core directed acyclic graph (DAG) task model and proposes a novel task scheduling method based on an improved chaotic quantum-behaved particle swarm optimization (CQPSO) algorithm. A task priority scheduling list was built. A processor with minimum cumulative earliest finish time (EFT) was acted as the object of the first task assignment. The task precedence relationships were satisfied and the total execution time of all tasks was

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8

Kaur, Mandeep, and Balwinder Singh Sohi. "Efficient DAG Task Scheduling Algorithm for Wireless Sensor Networks." International Journal of Computer Sciences and Engineering 6, no. 12 (2018): 735–43. http://dx.doi.org/10.26438/ijcse/v6i12.735743.

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9

Singh, Manjeet, and Javalkar Dinesh Kumar. "DBRS: Directed Acyclic Graph based Reliable Scheduling Approach in Large Scale Computing." International Journal on Recent and Innovation Trends in Computing and Communication 10, no. 11 (2022): 40–46. http://dx.doi.org/10.17762/ijritcc.v10i11.5778.

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In large scale environments, scheduling presents a significant challenge because it is an NP-hard problem. There are basically two types of task in execution- dependent task and independent task. The execution of dependent task must follow a strict order because output of one activity is typically the input of another. In this paper, a reliable fault tolerant approach is proposed for scheduling of dependent task in large scale computing environments. The workflow of dependent task is represented with the help of a DAG (directed acyclic graph). The proposed methodology is evaluated over various

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10

Taufer, Michela, and Arnold L. Rosenberg. "Scheduling DAG-based workflows on single cloud instances: High-performance and cost effectiveness with a static scheduler." International Journal of High Performance Computing Applications 31, no. 1 (2016): 19–31. http://dx.doi.org/10.1177/1094342015594518.

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The problem of achieving high-performance cost-effectively in cloud computing is challenging when workflows have Directed Acyclic Graph (DAG)-structured inter-task dependencies. We study this problem within single cloud instances and provide empirical evidence that the static Area-Oriented DAG-Scheduling (AO) paradigm, which predetermines the order for executing a DAG’s tasks, provides both high performance and cost effectiveness. AO produces schedules in a platform-oblivious manner; it ignores the performance characteristics of the platform’s resources and focuses only on the dependency struc

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11

Gao, Zhibin, Gaoyu Luo, Shanhao Zhan, Bang Liu, Lianfen Huang, and Han-Chieh Chao. "ST-HO: Symmetry-Enhanced Energy-Efficient DAG Task Offloading Algorithm in Intelligent Transport System." Symmetry 16, no. 2 (2024): 164. http://dx.doi.org/10.3390/sym16020164.

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In Intelligent Transport Systems (ITSs), Internet of Vehicles (IoV) communications and computation offloading technology have been introduced to assist with the burdensome sensing task processing, thus prompting a new design paradigm called mobile sensing–communication–computation (MSCC) synergy. Most researchers have focused on offloading strategy design to reduce energy consumption or execution costs, but ignore the intrinsic characteristics of tasks, which may lead to poor performance. This paper studies the offloading strategy of vehicle MSCC tasks represented by a Directed Acyclic Graph (

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12

Chen, Yumeng, Songlin Liu, Zongmiao He, and Xiang Ling. "A Tetris-Based Task Allocation Strategy for Real-Time Operating Systems." Electronics 14, no. 1 (2024): 98. https://doi.org/10.3390/electronics14010098.

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Real-time constrained multiprocessor systems have been widely applied across various domains. In this paper, we focus on the scheduling algorithm for directed acyclic graph (DAG) tasks under partitioned scheduling on multiprocessor systems. Effective real-time task scheduling algorithms significantly enhance the performance and stability of multiprocessor systems. Traditional real-time task scheduling algorithms commonly rely on a single-heuristic parameter as the reference for task allocation, which typically results in suboptimal performance. Inspired by the Tetris algorithm, we propose a no

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13

Yang, Hao, Huifu Zhang, Fan Luo, Fangjun Liu, and Hao Chen. "Pruning-based Deep Reinforcement Learning for Task Offloading in End-Edge-Cloud Collaborative Mobile Edge Computing." Journal of Computing and Electronic Information Management 13, no. 1 (2024): 1–9. http://dx.doi.org/10.54097/291dyi3r.

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The task offloading of Mobile Edge Computing (MEC) brings infinite possibilities to compute-intensive and latency-sensitive mobile applications. However, the dynamic nature of MEC systems and complex dependencies between computational tasks pose significant challenges to offloading decisions. In this paper, we address the task offloading problem of end-edge-cloud collaborative computing in MEC with task dependencies. Initially, we model inter-task dependencies using Directed Acyclic Graphs (DAG) and propose a task priority queue model to transform the DAG task model into a sequential queue mod

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14

Zhang, Han, Rui Feng Guo, and Cong Geng. "A Model and Decomposition Mechanism of Complex Task in MAS." Applied Mechanics and Materials 467 (December 2013): 609–14. http://dx.doi.org/10.4028/www.scientific.net/amm.467.609.

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In this paper, a complex task decomposition mechanism and the formal definition of atomic task and the complex task were proposed. The operation mechanism and the characteristics of MAS were firstly studied. Then, a hierarchical task decomposition mechanism is proposed according to the requirements of complex task decomposition in MAS. Finally, the formal definition of atomic task and the complex task based on DAG (Directed Acyclic Graph) and TAEMS (Task Analysis, Environment Modeling and Simulation) in MAS are described.

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15

Xiao, Xianghui, and Zhiyong Li. "Chemical Reaction Multi-Objective Optimization for Cloud Task DAG Scheduling." IEEE Access 7 (2019): 102598–605. http://dx.doi.org/10.1109/access.2019.2926500.

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16

Chen, Qingfeng, Yu Han, Jing Wu, and Yu Gan. "Energy-Saving Task Scheduling Based on Hard Reliability Requirements: A Novel Approach with Low Energy Consumption and High Reliability." Sustainability 14, no. 11 (2022): 6591. http://dx.doi.org/10.3390/su14116591.

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With the increasing complexity of application situations in multi-core processing systems, how to assure task execution reliability has become a focus of scheduling algorithm research in recent years. Most fault-tolerant algorithms achieve hard reliability requirements through task redundancy, which increases energy consumption and contradicts the concept of sustainable development. In this paper, we propose a new algorithm called HDFE (Heterogeneous-Dag-task-fault-tolerance-energy-efficiency algorithm) that combines DVFS technology and task replication technology to solve the scheduling probl

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17

Wu, Hao, Qinggeng Jin, Chenghua Zhang, and He Guo. "A Selective Mirrored Task Based Fault Tolerance Mechanism for Big Data Application Using Cloud." Wireless Communications and Mobile Computing 2019 (February 26, 2019): 1–12. http://dx.doi.org/10.1155/2019/4807502.

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With the wide deployment of cloud computing in big data processing and the growing scale of big data application, managing reliability of resources becomes a critical issue. Unfortunately, due to the highly intricate directed-acyclic-graph (DAG) based application and the flexible usage of processors (virtual machines) in cloud platform, the existing fault tolerant approaches are inefficient to strike a balance between the parallelism and the topology of the DAG-based application while using the processors, which causes a longer makespan for an application and consumes more processor time (comp

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18

Wahyudi, Erwin Eko, Muhammad Auzan, Andi Dharmawan, et al. "Akuisisi Data Prediksi Curah Hujan Secara Periodik Menggunakan Apache Airflow." Journal of Informatics, Information System, Software Engineering and Applications (INISTA) 4, no. 2 (2022): 1–12. http://dx.doi.org/10.20895/inista.v4i2.574.

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Akuisisi data, bertujuan untuk mengambil data awal, merupakan salah satu tahapan dalam metodologi penambangan data. Data awal akan diproses menjadi data akhir yang digunakan untuk proses pemodelan, seperti pembuatan model untuk memprediksi potensi terjadinya tanah longsor. Data prediksi curah hujan yang disediakan oleh Badan Meteorologi, Klimatologi, dan Geofisika (BMKG) dapat digunakan untuk pemodelan tersebut. Data akan disimpan di komputer lokal dengan menggunakan alat atau aplikasi otomasi yang bernama Apache Airflow. Proses akuisisi data dari server BMKG ke komputer lokal dijalankan secar

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19

Xiao, Wei, Hongbin Wang, Ming He, and Nianbin Wang. "Boosting Causal Structure Learning: An Asymmetric Exponential Modulation Gaussian-Based Adaptive Sample Reweighting Framework." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 20 (2025): 21661–69. https://doi.org/10.1609/aaai.v39i20.35470.

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Recent advances in differentiable score-based methods for Directed Acyclic Graph (DAG) structure learning have revolutionized the problem of combinatorial structure learning, transforming it into a continuous optimization task. Despite their remarkable success, these methods rely on a key assumption that all samples have the same level of difficulty and no data heterogeneity. When this assumption does not hold, causal discovery algorithms based on it inevitably return networks with many spurious edges. Despite existing research, the current method ignores the reality of outliers in the samples

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20

Zheng, Huiji, Sicong Yu, and Xiaolong Cui. "GMOM: An Offloading Method of Dependent Tasks Based on Deep Reinforcement Learning." Mobile Information Systems 2022 (November 8, 2022): 1–13. http://dx.doi.org/10.1155/2022/9587040.

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Mobile edge computing (MEC) is considered as an effective solution to delay-sensitive services, and computing offloading, the central technology in MEC, can expand the capacity of resource-constrained mobile terminals (MTs). However, because of the interdependency among applications, and the dynamically changing and complex nature of the MEC environment, offloading decision making turns out to be an NP-hard problem. In the present work, a graph mapping offloading model (GMOM) based on deep reinforcement learning (DRL) is proposed to address the offloading problem of dependent tasks in MEC. Spe

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21

Zhu, Liangbin, Ying Shang, Jinglei Li, Yiming Jia, and Qinghai Yang. "Reliability-Constrained Task Scheduling for DAG Applications in Mobile Edge Computing." Wireless Communications and Mobile Computing 2024 (January 29, 2024): 1–12. http://dx.doi.org/10.1155/2024/6980514.

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The development of the internet of things (IoT) and 6G has given rise to numerous computation-intensive and latency-sensitive applications, which can be represented as directed acyclic graphs (DAGs). However, achieving these applications poses a huge challenge for user equipment (UE) that are constrained in computational power and battery capacity. In this paper, considering different requirements in various task scenarios, we aim to optimize the execution latency and energy consumption of the entire mobile edge computing (MEC) system. The system consists of single UE and multiple heterogeneou

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22

Jia, Runa, Kuang Zhao, Xianglin Wei, Guoliang Zhang, Yangang Wang, and Gangyi Tu. "Joint Trajectory Planning, Service Function Deploying, and DAG Task Scheduling in UAV-Empowered Edge Computing." Drones 7, no. 7 (2023): 443. http://dx.doi.org/10.3390/drones7070443.

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Efficient task scheduling plays a key role in unmanned aerial vehicle (UAV)-empowered edge computing due to the limitation in energy supply and computation resource on the UAV platforms. This problem becomes much more complicated when the processing-dependent tasks that can be described as directed acyclic graphs (DAGs) and each of their components can only be processed on a virtual machine or container that deploys the desired service function (SF). In this paper, we first build an optimization problem that aims to minimize the completion time of all DAG tasks subject to constraints including

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23

Yang, Dezhi, Guoxian Yu, Jun Wang, Zhengtian Wu, and Maozu Guo. "Reinforcement Causal Structure Learning on Order Graph." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 9 (2023): 10737–44. http://dx.doi.org/10.1609/aaai.v37i9.26274.

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Learning directed acyclic graph (DAG) that describes the causality of observed data is a very challenging but important task. Due to the limited quantity and quality of observed data, and non-identifiability of causal graph, it is almost impossible to infer a single precise DAG. Some methods approximate the posterior distribution of DAGs to explore the DAG space via Markov chain Monte Carlo (MCMC), but the DAG space is over the nature of super-exponential growth, accurately characterizing the whole distribution over DAGs is very intractable. In this paper, we propose Reinforcement Causal Struc

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24

EL-NATTAT, Amal, Nirmeen El-Bahnasawy, and Ayman EL-SAYED. "Enhanced Leveled DAG Prioritized Task Scheduling Algorithm in Distributed Computing System." International Conference on Electrical Engineering 10, no. 10 (2016): 1–13. http://dx.doi.org/10.21608/iceeng.2016.30296.

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25

Long, Linbo, Zhi Liu, Jingcheng Shen, and Yi Jiang. "SecDS: A security-aware DAG task scheduling strategy for edge computing." Future Generation Computer Systems 166 (May 2025): 107627. https://doi.org/10.1016/j.future.2024.107627.

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26

Kaur, Gurjit. "A DAG based Task Scheduling Algorithms for Multiprocessor System - A Survey." International Journal of Grid and Distributed Computing 9, no. 9 (2016): 103–14. http://dx.doi.org/10.14257/ijgdc.2016.9.9.10.

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27

COSNARD, MICHEL, and EMMANUEL JEANNOT. "AUTOMATIC PARALLELIZATION TECHNIQUES BASED ON COMPACT DAG EXTRACTION AND SYMBOLIC SCHEDULING." Parallel Processing Letters 11, no. 01 (2001): 151–68. http://dx.doi.org/10.1142/s012962640100049x.

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Symbolic allocation and dynamic scheduling of tasks on a distributed memory machine for coarse-grained applications represented by parameterized task graphs (PTG) are presented in this paper. A PTG is a new computation model for symbolically representing directed acyclic task graphs (DAGs). The size of a PTG is independent of the problem size and its parameters can be instantiated at run time. Parameter independent optimization is important for exploiting non-static parallelism in scientific computing programs with varying problem sizes. Previous DAG scheduling algorithms are not able to handl

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28

Bramas, Bérenger, and Alain Ketterlin. "Improving parallel executions by increasing task granularity in task-based runtime systems using acyclic DAG clustering." PeerJ Computer Science 6 (January 13, 2020): e247. http://dx.doi.org/10.7717/peerj-cs.247.

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The task-based approach is a parallelization paradigm in which an algorithm is transformed into a direct acyclic graph of tasks: the vertices are computational elements extracted from the original algorithm and the edges are dependencies between those. During the execution, the management of the dependencies adds an overhead that can become significant when the computational cost of the tasks is low. A possibility to reduce the makespan is to aggregate the tasks to make them heavier, while having fewer of them, with the objective of mitigating the importance of the overhead. In this paper, we

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29

da Silva, Eduardo C., and Paulo H. R. Gabriel. "A Comprehensive Review of Evolutionary Algorithms for Multiprocessor DAG Scheduling." Computation 8, no. 2 (2020): 26. http://dx.doi.org/10.3390/computation8020026.

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The multiprocessor task scheduling problem has received considerable attention over the last three decades. In this context, a wide range of studies focuses on the design of evolutionary algorithms. These papers deal with many topics, such as task characteristics, environmental heterogeneity, and optimization criteria. To classify the academic production in this research field, we present here a systematic literature review for the directed acyclic graph (DAG) scheduling, that is, when tasks are modeled through a directed acyclic graph. Based on the survey of 56 works, we provide a panorama ab

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30

Berenjian, Golnaz, Homayun Motameni, Mehdi Golsorkhtabaramiri, and Ali Ebrahimnejad. "Distribution slack allocation algorithm for energy aware task scheduling in cloud datacenters." Journal of Intelligent & Fuzzy Systems 41, no. 1 (2021): 251–72. http://dx.doi.org/10.3233/jifs-201696.

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Regarding the ever-increasing development of data and computational centers due to the contribution of high-performance computing systems in such sectors, energy consumption has always been of great importance due to CO2 emissions that can result in adverse effects on the environment. In recent years, the notions such as “energy” and also “Green Computing” have played crucial roles when scheduling parallel tasks in datacenters. The duplication and clustering strategies, as well as Dynamic Voltage and Frequency Scaling (DVFS) techniques, have focused on the reduction of the energy consumption a

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31

Yin, Naiyu, Tian Gao, Yue Yu, and Qiang Ji. "Effective Causal Discovery under Identifiable Heteroscedastic Noise Model." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 15 (2024): 16486–94. http://dx.doi.org/10.1609/aaai.v38i15.29586.

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Capturing the underlying structural causal relations represented by Directed Acyclic Graphs (DAGs) has been a fundamental task in various AI disciplines. Causal DAG learning via the continuous optimization framework has recently achieved promising performance in terms of accuracy and efficiency. However, most methods make strong assumptions of homoscedastic noise, i.e., exogenous noises have equal variances across variables, observations, or even both. The noises in real data usually violate both assumptions due to the biases introduced by different data collection processes. To address the he

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32

Maity, Arka, Anuj Pathania, and Tulika Mitra. "PkMin: Peak Power Minimization for Multi-Threaded Many-Core Applications." Journal of Low Power Electronics and Applications 10, no. 4 (2020): 31. http://dx.doi.org/10.3390/jlpea10040031.

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Multiple multi-threaded tasks constitute a modern many-core application. An accompanying generic Directed Acyclic Graph (DAG) represents the execution precedence relationship between the tasks. The application comes with a hard deadline and high peak power consumption. Parallel execution of multiple tasks on multiple cores results in a quicker execution, but higher peak power. Peak power single-handedly determines the involved cooling costs in many-cores, while its violations could induce performance-crippling execution uncertainties. Less task parallelization, on the other hand, results in lo

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33

Dougani, Bentabet, and Abdeslem Dennai. "Bandwidth Allocation Algorithm for Makespan Optimization in a Fog-Cloud Environment: Monitoring Application." Computer Science Journal of Moldova 31, no. 1(91) (2023): 45–69. http://dx.doi.org/10.56415/csjm.v31.03.

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Fog computing technology has emerged to handle a large amount of data generated by the Internet of Things (IoT) terminals and cope with latency-sensitive application requests by allocating computation and storage resources at the edge of the Internet. In many IoT applications, the data acquisition procedures must apply the Directed Acyclic Graph (DAG) to get real-time results. The principal goal of DAG scheduling is to reduce total completion time without breaking priority constraints by properly allocating tasks to processors and arranging task execution sequencing. In this paper, we propose

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34

Ariffin, W. N. M. "The Reduction of Directed Cyclic Graph for Task Assignment Problem." MATEC Web of Conferences 150 (2018): 06031. http://dx.doi.org/10.1051/matecconf/201815006031.

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In this paper, a directed cyclic graph (DCG) is proposed as the task graph. It is undesirable and impossible to complete the task according to the constraints if the cycle exists. Therefore, an effort should be done in order to eliminate the cycle to obtain a directed acyclic graph (DAG), so that the minimum amount of time required for the entire task can be found. The technique of reducing the complexity of the directed cyclic graph to a directed acyclic graph by reversing the orientation of the path is the main contribution of this study. The algorithm was coded using Java programming and co

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35

Yue, Shasha, Yan Ma, Lajiao Chen, Yuzhu Wang, and Weijing Song. "Dynamic DAG scheduling for many-task computing of distributed eco-hydrological model." Journal of Supercomputing 75, no. 2 (2017): 510–32. http://dx.doi.org/10.1007/s11227-017-2047-1.

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Xie, Haitao, Hongwei Chen, and Chunzhi Wang. "A Priority-driven ACO Algorithm for DAG Task Scheduling in Cloud Environment." International Journal of Hybrid Information Technology 8, no. 6 (2015): 205–16. http://dx.doi.org/10.14257/ijhit.2015.8.6.20.

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Cheng, Yuxia, Zhiwei Wu, Kui Liu, Qing Wu, and Yu Wang. "Smart DAG Tasks Scheduling between Trusted and Untrusted Entities Using the MCTS Method." Sustainability 11, no. 7 (2019): 1826. http://dx.doi.org/10.3390/su11071826.

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Task scheduling is critical for improving system performance in the distributed heterogeneous computing environment. The Directed Acyclic Graph (DAG) tasks scheduling problem is NP-complete and it is hard to find an optimal schedule. Due to its key importance, the DAG tasks scheduling problem has been extensively studied in the literature. However, many previously proposed traditional heuristic algorithms are usually based on greedy methods and also lack the consideration of scheduling tasks between trusted and untrusted entities, which makes the problem more complicated, but there still exist

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38

Senapati, Debabrata, Arnab Sarkar, and Chandan Karfa. "HMDS: A Makespan Minimizing DAG Scheduler for Heterogeneous Distributed Systems." ACM Transactions on Embedded Computing Systems 20, no. 5s (2021): 1–26. http://dx.doi.org/10.1145/3477037.

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The problem of scheduling Directed Acyclic Graphs in order to minimize makespan ( schedule length ), is known to be a challenging and computationally hard problem. Therefore, researchers have endeavored towards the design of various heuristic solution generation techniques both for homogeneous as well as heterogeneous computing platforms. This work first presents HMDS-Bl , a list-based heuristic makespan minimization algorithm for task graphs on fully connected heterogeneous platforms. Subsequently, HMDS-Bl has been enhanced by empowering it with a low-overhead depth-first branch and bound bas

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39

Hellani, Houssein, Layth Sliman, Abed Ellatif Samhat, and Ernesto Exposito. "Computing Resource Allocation Scheme for DAG-Based IOTA Nodes." Sensors 21, no. 14 (2021): 4703. http://dx.doi.org/10.3390/s21144703.

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IOTA is a distributed ledger technology (DLT) platform proposed for the internet of things (IoT) systems in order to tackle the limitations of Blockchain in terms of latency, scalability, and transaction cost. The main concepts used in IOTA to reach this objective are a directed acyclic graph (DAG) based ledger, called Tangle, used instead of the chain of blocks, and a new validation mechanism that, instead of relying on the miners as it is the case in Blockchain, relies on participating nodes that cooperate to validate the new transactions. Due to the different IoT capabilities, IOTA classifi

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.K, Nithyanandakumari. "Assessment of Ant Colony Optimization Algorithm for DAG Task Scheduling in Cloud Computing." International Journal of Advanced Trends in Computer Science and Engineering 9, no. 4 (2020): 5278–86. http://dx.doi.org/10.30534/ijatcse/2020/159942020.

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Lee, Hyunsung, Sangwoo Cho, Yeongjae Jang, Jinkyu Lee, and Honguk Woo. "A Global DAG Task Scheduler Using Deep Reinforcement Learning and Graph Convolution Network." IEEE Access 9 (2021): 158548–61. http://dx.doi.org/10.1109/access.2021.3130407.

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EL-NATTAT, Amal, Nirmeen A. El-Bahnasawy, and Ayman EL-SAYED. "An Enhancement of Leveled DAG Prioritized Task Scheduling Algorithm in Distributed Computing Systems." Menoufia Journal of Electronic Engineering Research 26, no. 1 (2017): 171–92. http://dx.doi.org/10.21608/mjeer.2017.63443.

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Chaudhuri, Pranay, and Jeffrey Elcock. "Scheduling DAG-based applications in multicluster environments with background workload using task duplication." International Journal of Computer Mathematics 87, no. 11 (2010): 2387–97. http://dx.doi.org/10.1080/00207160902803314.

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Lee, Heejo, Jong Kim, Sung Je Hong, and Sunggu Lee. "Task scheduling using a block dependency DAG for block-oriented sparse Cholesky factorization." Parallel Computing 29, no. 1 (2003): 135–59. http://dx.doi.org/10.1016/s0167-8191(02)00220-x.

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Li, Chenxi, Xiaofei Liao, and Hai Jin. "Enhancing application performance via DAG-driven scheduling in task parallelism for cloud center." Peer-to-Peer Networking and Applications 12, no. 2 (2017): 381–91. http://dx.doi.org/10.1007/s12083-017-0576-2.

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Yu, Siyang, Kenli Li, and Yuming Xu. "A DAG task scheduling scheme on heterogeneous cluster systems using discrete IWO algorithm." Journal of Computational Science 26 (May 2018): 307–17. http://dx.doi.org/10.1016/j.jocs.2016.09.008.

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Chen, Jian-Jia. "Federated scheduling admits no constant speedup factors for constrained-deadline DAG task systems." Real-Time Systems 52, no. 6 (2016): 833–38. http://dx.doi.org/10.1007/s11241-016-9255-2.

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Xiong, Feng, Yi Ping Yuan, Yu Ying Wang, and Guang Wen Wang. "Task Scheduling in Multi-Process with Resource Constraints under MG Workflow." Advanced Materials Research 33-37 (March 2008): 1425–30. http://dx.doi.org/10.4028/www.scientific.net/amr.33-37.1425.

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In manufacturing Grid workflow, multiple tasks share a common and limited resource pool. In order to solve task scheduling in multi-process with resource constraints under MG workflow, the Task-Resource Constrained model is set up to descript the assignment relation between task and resource. The framework of the task scheduling and the scheduling policies are also presented that can readjust the tasks according to the priority rules and the time parameters in the process. Then we present a heuristic scheduling algorithm that includes multiple policies. The heuristic scheduling algorithm will

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Tomar, Divya, and Sonali Agarwal. "Leaf Recognition for Plant Classification Using Direct Acyclic Graph Based Multi-Class Least Squares Twin Support Vector Machine." International Journal of Image and Graphics 16, no. 03 (2016): 1650012. http://dx.doi.org/10.1142/s0219467816500121.

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As most of the plant species are at the risk of extinction, the task of plant identification has become a challenging process and an active area of research. In this paper, we propose a leaf recognition system for plant species classification using leaf image data through a novel direct acyclic graph based multi-class least squares twin support vector machine (DAG-MLSTSVM) classifier. Hybrid feature selection (HFS) approach is used to obtain the best discriminant features for the recognition of individual plant species. Leaves are recognized on the basis of shape and texture features. The expe

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Zuo, Hao, Jinshen Jiang, and Yun Zhou. "DAGOR: Learning DAGs via Topological Sorts and QR Factorization." Mathematics 12, no. 8 (2024): 1198. http://dx.doi.org/10.3390/math12081198.

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Recently, the task of acquiring causal directed acyclic graphs (DAGs) from empirical data has been modeled as an iterative process within the framework of continuous optimization with a differentiable acyclicity characterization. However, learning DAGs from data is an NP-hard problem since the DAG space increases super-exponentially with the number of variables. In this work, we introduce the graph topological sorts in solving the continuous optimization problem, which is substantially smaller than the DAG space and beneficial in avoiding local optima. Moreover, the topological sorts space doe

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