Artículos de revistas: "DAG task"

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Liu, Zhiguo, Luxi Zhang, Lin Wang, Xiaoqi Dong y Junlin Rong. "Research on Multi-DAG Satellite Network Task Scheduling Algorithm Based on Cache-Composite Priority". Electronics 13, n.º 4 (15 de febrero de 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 this algorithm lies in the DAG selection phase, where not only are task priorities computed but also the concept of fair scheduling is introduced, so as to prevent the excessively delayed scheduling of low-priority DAG tasks. In addition, the concept of public subtasks is introduced to reduce the system overhead caused by repetitive tasks. The experimental results show that the hybrid scheduling strategy proposed in this paper can meet the demand of DAG scheduling and improve the degree of task completion while effectively reducing the task latency and energy consumption.

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Zhao, Yao, Jian Dong, Hongwei Liu, Jin Wu y Yanxin Liu. "Performance Improvement of DAG-Aware Task Scheduling Algorithms with Efficient Cache Management in Spark". Electronics 10, n.º 16 (4 de agosto de 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 performance degradation. In this study, we propose a new cache management policy known as Long-Running Stage Set First (LSF), which makes full use of the task dependencies to optimize the cache management performance in DAG-aware scheduling algorithms. LSF calculates the caching and prefetching priorities of resilient distributed datasets according to their unprocessed workloads and significance in parallel scheduling, which are key factors in DAG-aware scheduling algorithms. Moreover, we present a cache-aware task scheduling algorithm based on LSF to reduce the resource fragmentation in computing. Experiments demonstrate that, compared to DAG-aware scheduling algorithms with LRU and MRD, the same algorithms with LSF improve the JCT by up to 42% and 30%, respectively. The proposed cache-aware scheduling algorithm also exhibits about 12% reduction in the average job completion time compared to GRAPHENE with LSF.

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Cai, Lingfeng, Xianglin Wei, Changyou Xing, Xia Zou, Guomin Zhang y Xiulei Wang. "Failure-resilient DAG task scheduling in edge computing". Computer Networks 198 (octubre de 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 y 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 CRO with the variable neighborhood search (VNS) method. DRSCRO has two reaction phases for super molecule selection and global optimization, respectively. In the molecule selection phase, the CRO as a metaheuristic algorithm is adopted to obtain a super molecule for accelerating convergence. For promoting the intensification capability, in the global optimization phase, the VNS algorithm with a new processor selection model is used as the initialization under the consideration of scheduling order and processor assignment, and the load balance neighborhood structure of VNS is also utilized in the ineffective reaction operator. The experimental results verify the effectiveness and efficiency of DRSCRO in terms ofmakespanand convergence rate.

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5

Vucha, Mahendra y 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 the advantage of runtime design parameters of an application represented as DAG and considers the attributes of tasks in DAG and computing resources to distribute the tasks of an application onto RHSCS. In this paper, we have described the DTD-MLF model and verified its effectiveness by distributing some of real life benchmark applications onto RHSCS configured on Virtex-5 FPGA device. Some benchmark applications are represented as DAG and are distributed to the resources of RHSCS based on DTD-MLF model. The performance of the MLF based dynamic task distribution methodology is compared with static task distribution methodology. The comparison shows that the dynamic task distribution model with MLF criteria outperforms the static task distribution techniques in terms of schedule length and effective utilization of available RHSCS resources.

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6

Kaur, Mandeep y Balwinder Singh Sohi. "Efficient DAG Task Scheduling Algorithm for Wireless Sensor Networks". International Journal of Computer Sciences and Engineering 6, n.º 12 (31 de diciembre de 2018): 735–43. http://dx.doi.org/10.26438/ijcse/v6i12.735743.

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7

Zhai, Wenzheng, Yue-Li Hu y Feng Ran. "CQPSO scheduling algorithm for heterogeneous multi-core DAG task model". Modern Physics Letters B 31, n.º 19-21 (27 de julio de 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 minimized. The experimental results show that the proposed algorithm has the advantage of optimization abilities, simple and feasible, fast convergence, and can be applied to the task scheduling optimization for other heterogeneous and distributed environment.

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8

Voudouris, Petros, Per Stenström y Risat Pathan. "Federated Scheduling of Sporadic DAGs on Unrelated Multiprocessors". ACM Transactions on Embedded Computing Systems 20, n.º 5s (31 de octubre de 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 novel processors-to-task assignment heuristic using a new concept called processor value . Empirical evaluation is presented to show the effectiveness of our approach.

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9

Singh, Manjeet y 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, n.º 11 (30 de noviembre de 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 parameters by applying it in a large scale computing environment- ‘grid computing’. Grid computing is a high performance computing for solving complex, large and data intensive problems in various fields. The result analysis shows that the proposed DAG based reliable scheduling (DBRS) approach increases the performance of system by decreasing the makespan, number of failures and increasing performance improvement ratio (PIR).

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Taufer, Michela y 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, n.º 1 (28 de julio de 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 structure of the workflow. Specifically, AO’s schedules strive to enhance the rate of rendering tasks eligible for execution. Using an archive of diverse DAG-structured workflows, we experimentally compare AO with a variety of competing DAG-schedulers: (a) the static locally optimal DAG-scheduler (LO), which, like AO, is static and platform-oblivious but chooses its DAG-ordering based on tasks’ outdegrees; and (b) five dynamic versions of static schedulers (including AO and LO), each of which can violate its parent static scheduler’s prescribed task orders to avoid stalling. Our results provide evidence of AO’s supremacy as compared with LO and its essential equivalence to dynamic-AO: neither competitor yields higher performance at an lower cost than AO does. Two aspects of these results are notable. Firstly, AO is platform-oblivious, whereas dynamic-AO is intensely platform-sensitive; one would expect platform sensitivity to enhance performance. Secondly, AO outperforms LO by an order of magnitude, together with lower costs; one would not expect such a performance gap.

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11

Gao, Zhibin, Gaoyu Luo, Shanhao Zhan, Bang Liu, Lianfen Huang y Han-Chieh Chao. "ST-HO: Symmetry-Enhanced Energy-Efficient DAG Task Offloading Algorithm in Intelligent Transport System". Symmetry 16, n.º 2 (31 de enero de 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 (DAG) structure. According to the DAG dependency of the subtasks, this paper proposes a computation offloading strategy to optimize energy consumption under time constraints. An energy consumption model for task execution is established. Then, the Simulated Annealing and Tabu Search hybrid optimization algorithm (ST-HO) is designed to solve the problem of minimizing the energy consumption. Crucially, this research integrates the concept of symmetry into the typical DAG structure of MSCC tasks, ensuring the integrity and efficiency of task execution in ITS. The simulation results show that ST-HO reduces energy consumption by at least 5.58% compared to the conventional algorithm. Particularly, the convergence speed of ST-HO is improved by 52.63% when the replication strategy of symmetric task is considered.

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Zhang, Han, Rui Feng Guo y Cong Geng. "A Model and Decomposition Mechanism of Complex Task in MAS". Applied Mechanics and Materials 467 (diciembre de 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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13

Xiao, Xianghui y 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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14

Chen, Qingfeng, Yu Han, Jing Wu y Yu Gan. "Energy-Saving Task Scheduling Based on Hard Reliability Requirements: A Novel Approach with Low Energy Consumption and High Reliability". Sustainability 14, n.º 11 (27 de mayo de 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 problem of DAG applications concerning energy-saving and hard reliability requirements in heterogeneous multi-core processor systems. Our algorithm is divided into three phases: the priority calculation phase, the task replication phase, and the task assignment phase. The HDFE algorithm achieved energy savings while meeting hard reliability requirements for applications, which was based on the interrelationship between reliability and energy consumption in filtering task replicas. In the experimental part of this paper, we designed four comparison experiments between the EFSRG algorithm, the HRRM algorithm, and the HDFE algorithm. The experimental results showed that the energy consumption of task scheduling using the HDFE algorithm is lower than other algorithms under different scales, thus achieving energy savings and complying with the concept of sustainable development.

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Wu, Hao, Qinggeng Jin, Chenghua Zhang y He Guo. "A Selective Mirrored Task Based Fault Tolerance Mechanism for Big Data Application Using Cloud". Wireless Communications and Mobile Computing 2019 (26 de febrero de 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 (computation cost). To address these issues, this paper presents a novel fault tolerant framework named Fault Tolerance Algorithm using Selective Mirrored Tasks Method (FAUSIT) for the fault tolerance of running a big data application on cloud. First, we provide comprehensive theoretical analyses on how to improve the performance of fault tolerance for running a single task on a processor. Second, considering the balance between the parallelism and the topology of an application, we present a selective mirrored task method. Finally, by employing the selective mirrored task method, the FAUSIT is designed to improve the fault tolerance for DAG based application and incorporates two important objects: minimizing the makespan and the computation cost. Our solution approach is evaluated through rigorous performance evaluation study using real-word workflows, and the results show that the proposed FAUSIT approach outperforms existing algorithms in terms of makespan and computation cost.

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Wahyudi, Erwin Eko, Muhammad Auzan, Andi Dharmawan, Danang Eko Nuryanto, Nanang Susyanto, Guruh Samodra y Danang Sri Hadmoko. "Akuisisi Data Prediksi Curah Hujan Secara Periodik Menggunakan Apache Airflow". Journal of Informatics, Information System, Software Engineering and Applications (INISTA) 4, n.º 2 (19 de mayo de 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 secara otomatis dalam dua kali sehari, yaitu pada pukul 00.00 dan 12.00. Terdapat dua task yang dibuat di Directed Acyclic Graph (DAG) untuk proses ini, yaitu task pertama sebagai sensor ketersediaan data dan task kedua yang melakukan proses utama. Status dari DAG pada Apache Airflow juga dapat diketahui secara cepat, misalnya status telah berhasil, gagal, atau sedang berjalan. Apache Airflow juga menyediakan log yang dapat diakses untuk mengetahui alasan kegagalan suatu task. Hasil dari penelitian ini adalah terdapat pipeline pada aplikasi otomasi Apache Airflow untuk membantu proses akuisisi data secara periodik.

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Zheng, Huiji, Sicong Yu y Xiaolong Cui. "GMOM: An Offloading Method of Dependent Tasks Based on Deep Reinforcement Learning". Mobile Information Systems 2022 (8 de noviembre de 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. Specifically, the MT application is first modeled into a directed acyclic graph (DAG), which is called a DAG task. Then, the DAG task is transformed into a subtask sequence vector according to the predefined order of priorities to facilitate processing. Finally, the sequence vector is input into an encoding-decoding framework based on the attention mechanism to obtain the offloading strategy vector. The GMOM is trained using the advanced proximal policy optimization (PPO) algorithm to minimize the comprehensive cost function including delay and energy consumption. Experiments show that the proposed model has good decision-making performance, with verified effectiveness in convergence, delay, and energy consumption.

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Zhu, Liangbin, Ying Shang, Jinglei Li, Yiming Jia y Qinghai Yang. "Reliability-Constrained Task Scheduling for DAG Applications in Mobile Edge Computing". Wireless Communications and Mobile Computing 2024 (29 de enero de 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 heterogeneous MEC servers to improve the execution efficiency of a DAG application. In addition, the execution reliability of a DAG application is viewed as a constraint. Based on the strong search capability and Pareto optimality theory of the cuckoo search (CS) algorithm and our previously proposed improved multiobjective cuckoo search (IMOCS) algorithm, we improve the initialization process and the update strategy of the external archive, and propose a reliability-constrained multiobjective cuckoo search (RCMOCS) algorithm. According to the simulation results, our proposed RCMOCS algorithm is able to obtain better Pareto frontiers and achieve satisfactory performance while ensuring execution reliability.

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EL-NATTAT, Amal, Nirmeen El-Bahnasawy y Ayman EL-SAYED. "Enhanced Leveled DAG Prioritized Task Scheduling Algorithm in Distributed Computing System". International Conference on Electrical Engineering 10, n.º 10 (1 de abril de 2016): 1–13. http://dx.doi.org/10.21608/iceeng.2016.30296.

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Kaur, Gurjit. "A DAG based Task Scheduling Algorithms for Multiprocessor System - A Survey". International Journal of Grid and Distributed Computing 9, n.º 9 (30 de septiembre de 2016): 103–14. http://dx.doi.org/10.14257/ijgdc.2016.9.9.10.

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Jia, Runa, Kuang Zhao, Xianglin Wei, Guoliang Zhang, Yangang Wang y Gangyi Tu. "Joint Trajectory Planning, Service Function Deploying, and DAG Task Scheduling in UAV-Empowered Edge Computing". Drones 7, n.º 7 (5 de julio de 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 task dependency, computation resource occupied by the UAVs, etc. To tackle this problem, a genetic algorithm-based joint deployment and scheduling algorithm, named GA-JoDeS, is put forward, since solving the established 0–1 integer programming problem in polynomial time is infeasible. Subtask offloading decision and UAV position are encoded into the chromosome in the GA-JoDeS algorithm, and the fitness value of an individual is decided by the maximum completion time of all DAG tasks. Through selection, crossover, and mutation, the GA-JoDeS algorithm evolves until it determines the individual with the optimal fitness value as the suboptimal solution to the problem. To evaluate the performance of the proposal, a series of simulations is conducted, and three traditional methods are chosen as comparison benchmarks. The results show that the GA-JoDeS algorithm can convergence quickly, and it can effectively reduce the completion time of DAG tasks with different parameter settings.

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Yang, Dezhi, Guoxian Yu, Jun Wang, Zhengtian Wu y Maozu Guo. "Reinforcement Causal Structure Learning on Order Graph". Proceedings of the AAAI Conference on Artificial Intelligence 37, n.º 9 (26 de junio de 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 Structure Learning on Order Graph (RCL-OG) that uses order graph instead of MCMC to model different DAG topological orderings and to reduce the problem size. RCL-OG first defines reinforcement learning with a new reward mechanism to approximate the posterior distribution of orderings in an efficacy way, and uses deep Q-learning to update and transfer rewards between nodes. Next, it obtains the probability transition model of nodes on order graph, and computes the posterior probability of different orderings. In this way, we can sample on this model to obtain the ordering with high probability. Experiments on synthetic and benchmark datasets show that RCL-OG provides accurate posterior probability approximation and achieves better results than competitive causal discovery algorithms.

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Bramas, Bérenger y Alain Ketterlin. "Improving parallel executions by increasing task granularity in task-based runtime systems using acyclic DAG clustering". PeerJ Computer Science 6 (13 de enero de 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 study an existing clustering/partitioning strategy to speed up the parallel execution of a task-based application. We provide two additional heuristics to this algorithm and perform an in-depth study on a large graph set. In addition, we propose a new model to estimate the execution duration and use it to choose the proper granularity. We show that this strategy allows speeding up a real numerical application by a factor of 7 on a multi-core system.

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COSNARD, MICHEL y EMMANUEL JEANNOT. "AUTOMATIC PARALLELIZATION TECHNIQUES BASED ON COMPACT DAG EXTRACTION AND SYMBOLIC SCHEDULING". Parallel Processing Letters 11, n.º 01 (marzo de 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 handle such cases. We present and study a symbolic scheduling algorithm called SLC (Symbolic Linear Clustering) which derives task clusters from a PTG using affine piecewise mapping functions and then evenly assigns clusters to processors. Thus a complete automatic parallelization method is presented.

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da Silva, Eduardo C. y Paulo H. R. Gabriel. "A Comprehensive Review of Evolutionary Algorithms for Multiprocessor DAG Scheduling". Computation 8, n.º 2 (10 de abril de 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 about the last 30 years of research in this field. From the analyzes of the selected studies, we found a diversity of application domains and mapped their main contributions.

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Berenjian, Golnaz, Homayun Motameni, Mehdi Golsorkhtabaramiri y Ali Ebrahimnejad. "Distribution slack allocation algorithm for energy aware task scheduling in cloud datacenters". Journal of Intelligent & Fuzzy Systems 41, n.º 1 (11 de agosto de 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 and the optimization of the performance parameters. Concerning scheduling Directed Acyclic Graph (DAG) of a datacenter processors equipped with the technique of DVFS, this paper proposes an energy- and time-aware algorithm based on dual-phase scheduling, called EATSDCDD, to apply the combination of the strategies for duplication and clustering along with the distribution of slack-time among the tasks of a cluster. DVFS and control procedures in the proposed green system are mapped into Petri net-based models, which contribute to designing a multiple decision process. In the first phase, we use an intelligent combined approach of the duplication and clustering strategies to run the immediate tasks of DAG along with monitoring the throughput by concentrating on the reduction of makespan and the energy consumed in the processors. The main idea of the proposed algorithm involves the achievement of a maximum reduction in energy consumption in the second phase. To this end, the slack time was distributed among non-critical dependent tasks. Additionally, we cover the issues of negotiation between consumers and service providers at the rate of μ based on Green Service Level Agreement (GSLA) to achieve a higher saving of the energy. Eventually, a set of data established for conducting the examinations and also different parameters of the constructed random DAG are assessed to examine the efficiency of our proposed algorithm. The obtained results confirms that our algorithm outperforms compared the other algorithms considered in this study.

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27

Yin, Naiyu, Tian Gao, Yue Yu y Qiang Ji. "Effective Causal Discovery under Identifiable Heteroscedastic Noise Model". Proceedings of the AAAI Conference on Artificial Intelligence 38, n.º 15 (24 de marzo de 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 heteroscedastic noise issue, we introduce relaxed implementable sufficient conditions and prove the identifiability of a general class of SEM subject to those conditions. Based on the identifiable general SEM, we propose a novel formulation for DAG learning which accounts for the noise variance variation across variables and observations. We then propose an effective two-phase iterative DAG learning algorithm to address the increasing optimization difficulties and learn a causal DAG from data with heteroscedastic variables noise under varying variance. We show significant empirical gains of the proposed approaches over state-of-the-art methods on both synthetic data and real data.

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28

Maity, Arka, Anuj Pathania y Tulika Mitra. "PkMin: Peak Power Minimization for Multi-Threaded Many-Core Applications". Journal of Low Power Electronics and Applications 10, n.º 4 (30 de septiembre de 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 lower peak power, but a more prolonged deadline violating execution. The problem of peak power minimization in many-cores is to determine task-to-core mapping configuration in the spatio-temporal domain that minimizes the peak power consumption of an application, but ensures application still meets the deadline. All previous works on peak power minimization for many-core applications (with or without DAG) assume only single-threaded tasks. We are the first to propose a framework, called PkMin, which minimizes the peak power of many-core applications with DAG that have multi-threaded tasks. PkMin leverages the inherent convexity in the execution characteristics of multi-threaded tasks to find a configuration that satisfies the deadline, as well as minimizes peak power. Evaluation on hundreds of applications shows PkMin on average results in 49.2% lower peak power than a similar state-of-the-art framework.

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29

Dougani, Bentabet y Abdeslem Dennai. "Bandwidth Allocation Algorithm for Makespan Optimization in a Fog-Cloud Environment: Monitoring Application". Computer Science Journal of Moldova 31, n.º 1(91) (abril de 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 a bandwidth-aware workflow allocation (BW-AWA) that schedules tasks by priority to the resource and optimizes the total execution time (Makespan) in the entire computing system. The task allocation process needs to consider the dependency between tasks. The proposed approach is tested with a monitoring application case study, and the results are compared to well-known approaches to demonstrate its effectiveness in optimizing the Makespan.

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30

Yue, Shasha, Yan Ma, Lajiao Chen, Yuzhu Wang y Weijing Song. "Dynamic DAG scheduling for many-task computing of distributed eco-hydrological model". Journal of Supercomputing 75, n.º 2 (19 de abril de 2017): 510–32. http://dx.doi.org/10.1007/s11227-017-2047-1.

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31

Xie, Haitao, Hongwei Chen y Chunzhi Wang. "A Priority-driven ACO Algorithm for DAG Task Scheduling in Cloud Environment". International Journal of Hybrid Information Technology 8, n.º 6 (30 de junio de 2015): 205–16. http://dx.doi.org/10.14257/ijhit.2015.8.6.20.

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32

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 consistently produced good assignment and task schedule.

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33

Cheng, Yuxia, Zhiwei Wu, Kui Liu, Qing Wu y Yu Wang. "Smart DAG Tasks Scheduling between Trusted and Untrusted Entities Using the MCTS Method". Sustainability 11, n.º 7 (27 de marzo de 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 exists a large optimization space to be explored. In this paper, we propose a trust-aware adaptive DAG tasks scheduling algorithm using the reinforcement learning and Monte Carlo Tree Search (MCTS) methods. The scheduling problem is defined using the reinforcement learning model. Efficient scheduling state space, action space and reward function are designed to train the policy gradient-based REINFORCE agent. The MCTS method is proposed to determine actual scheduling policies when DAG tasks are simultaneously executed in trusted and untrusted entities. Leveraging the algorithm’s capability of exploring long term reward, the proposed algorithm could achieve good scheduling policies while guaranteeing trusted tasks scheduled within trusted entities. Experimental results showed the effectiveness of the proposed algorithm compared with the classic HEFT/CPOP algorithms.

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34

Senapati, Debabrata, Arnab Sarkar y Chandan Karfa. "HMDS: A Makespan Minimizing DAG Scheduler for Heterogeneous Distributed Systems". ACM Transactions on Embedded Computing Systems 20, n.º 5s (31 de octubre de 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 based search approach, resulting in a new algorithm called HMDS . HMDS has been equipped with a set of novel tunable pruning mechanisms, which allow the designer to obtain a judicious balance between performance ( makespan ) and solution generation times, depending on the specific scenario at hand. Experimental analyses using randomly generated DAGs as well as benchmark task graphs, have shown that HMDS is able to comprehensively outperform state-of-the-art algorithms such as HEFT , PEFT , PPTS , etc., in terms of archived makespans while incurring bounded additional computation time overhead.

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35

Hellani, Houssein, Layth Sliman, Abed Ellatif Samhat y Ernesto Exposito. "Computing Resource Allocation Scheme for DAG-Based IOTA Nodes". Sensors 21, n.º 14 (9 de julio de 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 classifies these devices into full and light nodes. The light nodes are nodes with low computing resources which seek full nodes’ help to validate and attach its transaction to the Tangle. The light nodes are manually connected to the full nodes by using the full node IP address or the IOTA client load balancer. This task distribution method overcharges the active full nodes and, thus, reduces the platform’s performance. In this paper, we introduce an efficient mechanism to distribute the tasks fairly among full nodes and hence achieve load balancing. To do so, we consider the task allocation between the nodes by introducing an enhanced resource allocation scheme based on the weight least connection algorithm (WLC). To assess its performance, we investigate and test different implementation scenarios. The results show an improved balancing of data traffic among full nodes based on their weights and number of active connections.

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36

.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, n.º 4 (25 de agosto de 2020): 5278–86. http://dx.doi.org/10.30534/ijatcse/2020/159942020.

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37

Lee, Hyunsung, Sangwoo Cho, Yeongjae Jang, Jinkyu Lee y 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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38

EL-NATTAT, Amal, Nirmeen A. El-Bahnasawy y Ayman EL-SAYED. "An Enhancement of Leveled DAG Prioritized Task Scheduling Algorithm in Distributed Computing Systems". Menoufia Journal of Electronic Engineering Research 26, n.º 1 (1 de enero de 2017): 171–92. http://dx.doi.org/10.21608/mjeer.2017.63443.

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39

Chaudhuri, Pranay y Jeffrey Elcock. "Scheduling DAG-based applications in multicluster environments with background workload using task duplication". International Journal of Computer Mathematics 87, n.º 11 (septiembre de 2010): 2387–97. http://dx.doi.org/10.1080/00207160902803314.

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40

Lee, Heejo, Jong Kim, Sung Je Hong y Sunggu Lee. "Task scheduling using a block dependency DAG for block-oriented sparse Cholesky factorization". Parallel Computing 29, n.º 1 (enero de 2003): 135–59. http://dx.doi.org/10.1016/s0167-8191(02)00220-x.

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41

Li, Chenxi, Xiaofei Liao y Hai Jin. "Enhancing application performance via DAG-driven scheduling in task parallelism for cloud center". Peer-to-Peer Networking and Applications 12, n.º 2 (16 de junio de 2017): 381–91. http://dx.doi.org/10.1007/s12083-017-0576-2.

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42

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

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43

Chen, Jian-Jia. "Federated scheduling admits no constant speedup factors for constrained-deadline DAG task systems". Real-Time Systems 52, n.º 6 (31 de mayo de 2016): 833–38. http://dx.doi.org/10.1007/s11241-016-9255-2.

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44

Xiong, Feng, Yi Ping Yuan, Yu Ying Wang y Guang Wen Wang. "Task Scheduling in Multi-Process with Resource Constraints under MG Workflow". Advanced Materials Research 33-37 (marzo de 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 update the critical path of DAG (Direct Acyclic Graph) and the beginning time of post-tasks. MG Workflow engine can dynamically schedule the resources according the task requirement. An example is given to illustrate the method at last.

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45

Tomar, Divya y 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, n.º 03 (julio de 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 experimental results indicate that the proposed DAG-MLSTSVM based plant leaf recognition system is highly accurate and having faster processing speed as compared to artificial neural network and direct acyclic graph based support vector machine.

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46

Zuo, Hao, Jinshen Jiang y Yun Zhou. "DAGOR: Learning DAGs via Topological Sorts and QR Factorization". Mathematics 12, n.º 8 (17 de abril de 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 does not require consideration of acyclicity, which can significantly reduce the computational cost. To further deal with the inherent asymmetries of DAGs, we investigate the acyclicity characterization and propose a new DAGs learning optimization strategy based on QR factorization, named DAGOR. First, using the matrix congruent transformation, the adjacency matrix of the DAG is transformed into an upper triangular matrix with a topological sort. Next, using the QR factorization as a basis, we construct a least-square penalty function as constraints for optimization in the graph autoencoder framework. Numerical experiments are performed to further validate our theoretical results and demonstrate the competitive performance of our method.

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47

Garg, Akanksha, Navdeep S.Sethi, Nidhi Arora y Amit Makkar. "BNP TASK SCHEDULING ALGORITHMS FOR PERFORMANCE EVALUATION IN PARALLEL SYSTEM". INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 12, n.º 8 (20 de febrero de 2014): 3768–77. http://dx.doi.org/10.24297/ijct.v12i8.3014.

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Scheduling is the process to minimize the schedule length by proper allocation of the tasks to the processors and arrangement of execution sequencing of the tasks. Multiprocessor Scheduling using Directed Acyclic Graph (DAG) is used in this research. Â An important implication of minimization of schedule length is that the system throughput is maximized. The objective of this survey is to describe various scheduling algorithms and their functionalities in a contrasting fashion as well as examine their relative merits in terms of performance and time-complexity. In this research, three BNP Scheduling Algorithms are considered namely HLFET Algorithm, MCP Algorithm and ETF Algorithm to calculate effective output by comparing the algorithms with eight test case scenarios with varying number of nodes and processors.

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48

Jin, Jin y Yue Wang. "T2-DAG: a powerful test for differentially expressed gene pathways via graph-informed structural equation modeling". Bioinformatics 38, n.º 4 (10 de noviembre de 2021): 1005–14. http://dx.doi.org/10.1093/bioinformatics/btab770.

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Abstract Motivation A major task in genetic studies is to identify genes related to human diseases and traits to understand functional characteristics of genetic mutations and enhance patient diagnosis. Compared with marginal analyses of individual genes, identification of gene pathways, i.e. a set of genes with known interactions that collectively contribute to specific biological functions, can provide more biologically meaningful results. Such gene pathway analysis can be formulated into a high-dimensional two-sample testing problem. Given the typically limited sample size of gene expression datasets, most existing two-sample tests tend to have compromised powers because they ignore or only inefficiently incorporate the auxiliary pathway information on gene interactions. Results We propose T2-DAG, a Hotelling’s T2-type test for detecting differentially expressed gene pathways, which efficiently leverages the auxiliary pathway information on gene interactions from existing pathway databases through a linear structural equation model. We further establish its asymptotic distribution under pertinent assumptions. Simulation studies under various scenarios show that T2-DAG outperforms several representative existing methods with well-controlled type-I error rates and substantially improved powers, even with incomplete or inaccurate pathway information or unadjusted confounding effects. We also illustrate the performance of T2-DAG in an application to detect differentially expressed KEGG pathways between different stages of lung cancer. Availability and implementation The R (R Development Core Team, 2021) package T2DAG which implements the proposed T2-DAG test is available on Github at https://github.com/Jin93/T2DAG. Supplementary information Supplementary data are available at Bioinformatics online.

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49

Rajak, Nidhi. "A CRITICAL-PATH AND TOP-LEVEL ATTRIBUTES BASED TASK SCHEDULING ALGORITHM FOR DAG (CPTL)". International Journal of New Computer Architectures and their Applications 4, n.º 4 (2014): 130–36. http://dx.doi.org/10.17781/p0012.

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50

WANG, Jian, Huaihu CAO, Haifeng LI, Lixin CUI y Yanmei ZHANG. "Fusion-partitioning genetic task scheduling algorithm based on deterministic annealing technology in DAG blockchains". SCIENTIA SINICA Informationis 50, n.º 2 (1 de febrero de 2020): 261–74. http://dx.doi.org/10.1360/n112019-00025.

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