A High-Performance DAG Task Scheduling Algorithm for Heterogeneous Networked Embedded Systems

Xie, Guoqi; Li, Renfa; Xiao, Xiongren; Chen, Yuekun

doi:10.1109/aina.2014.123

Cited by 13 publications

(16 citation statements)

References 15 publications

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“…Thus, for accuracy, each task should have its own upward rank value on different processors. A new upward rank value of heterogeneous computing environments is defined in :

({, \begin{matrix} arrayleft \\ {rank}_{u} (n_{i}, p_{k}) = \max_{n_{j} \in succ (n_{i})} {{rank}_{u} (n_{j}, p_{k}) + w_{i, k} + \overset{true¯}{c_{i, j}}}; \\ {rank}_{u} (n_{exit}, p_{k}) = w_{exit,k} . \end{matrix})

…”

Section: Task Priority Allocation and Fairnessmentioning

confidence: 99%

Scheduling trade‐off of dynamic multiple parallel workflows on heterogeneous distributed computing systems

Xie

Liu

et al. 2016

Concurrency and Computation

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Summary Scheduling multiple parallel workflows, which arrive at different instants on heterogeneous distributed computing systems, is a great challenge because of the different requirements of resource providers and users. Overall scheduling length is the main concern of resource providers, whereas deadlines of workflows are the major requirements of users. Most algorithms use fairness‐based strategies to reduce the overall scheduling length. However, these algorithms cause obvious unfairness to longer‐makespan workflows or shorter‐makespan workflows. Furthermore, the systems cannot meet the deadlines of all workflows, particularly on large‐scale resource‐constrained computational grids. Gaining a reasonable balance between the overall scheduling length and the deadlines of workflows is a desirable goal. In this study, we first propose a fairness‐based scheduling algorithm called fairness‐based dynamic multiple heterogeneous selection value to achieve high performance of systems compared with existing works. Then, to meet the deadlines of partial higher‐priority workflows, we present a priority‐based scheduling algorithm called priority‐based dynamic multiple heterogeneous selection value. Finally, combining fairness‐based dynamic multiple heterogeneous selection value and priority‐based dynamic multiple heterogeneous selection value, we present the tradeoff‐based scheduling algorithm to meet the deadlines of more higher‐priority workflows while still allowing the lower‐priority workflows to be processed actively for better performance of systems. Both example and extensive experimental evaluations demonstrate significant improvement of our proposed algorithms. Copyright © 2016 John Wiley & Sons, Ltd.

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“…Thus, for accuracy, each task should have its own upward rank value on different processors. A new upward rank value of heterogeneous computing environments is defined in :

({, \begin{matrix} arrayleft \\ {rank}_{u} (n_{i}, p_{k}) = \max_{n_{j} \in succ (n_{i})} {{rank}_{u} (n_{j}, p_{k}) + w_{i, k} + \overset{true¯}{c_{i, j}}}; \\ {rank}_{u} (n_{exit}, p_{k}) = w_{exit,k} . \end{matrix})

…”

Section: Task Priority Allocation and Fairnessmentioning

confidence: 99%

Scheduling trade‐off of dynamic multiple parallel workflows on heterogeneous distributed computing systems

Xie

Liu

et al. 2016

Concurrency and Computation

Self Cite

View full text Add to dashboard Cite

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“…Single precedence-constrained task scheduling provides a research basis for multiple precedence-constrained task scheduling. The core idea of precedence-constrained task scheduling includes two phases, where the first phase is ordering the tasks in a list according to a descending order of priorities, and the second phase is to assign each task to a proper processor [17], [19]. Scheduling tasks for fastest execution is a well-known NP-hard optimization problem, and Task A1 A2 A3 A4 A5 A6 A7 A8 A9 A10 B1 B2 B3 B4 B5 p1 14 13 11 13 12 13 7 5 18 21 4 9 18 21 7 p2 8 1 9 1 3 8 1 3 1 6 1 5 1 1 1 2 7 5 1 0 1 7 1 5 6 p3 16 18 19 17 10 9 11 14 20 16 6 11 16 19 5 many heuristic list scheduling algorithms have been proposed to generate near-optimal solutions of the problem in parallel and distributed systems.…”

Section: A System Modelmentioning

confidence: 99%

“…Hence, for accuracy, each task should have its own upward rank value on different processors. A new upward rank value of heterogeneous computing environments is defined in our previous work [19]:…”

Section: A Task Priority Analysismentioning

confidence: 99%

“…In order to obtain an accurate prioritizing for tasks of a job, we also used the HPRV (Heterogeneous Priority Rank Value) defined in work [19] for task prioritizing, namely,…”

Section: A Task Priority Analysismentioning

confidence: 99%

“…However, it neglects one more important effective factor, that is, from "upward". Therefore, HSV (Heterogeneous Selection Value) is employed in work [19] for processor assignment, which is denoted by…”

Section: B Task Allocation Analysismentioning

confidence: 99%

See 2 more Smart Citations

Schedule Dynamic Multiple Parallel Jobs with Precedence-Constrained Tasks on Heterogeneous Distributed Computing Systems

Liu

Xie

Liu

et al. 2015

2015 14th International Symposium on Parallel and Distributed Computing

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Computer systems tend to be heterogeneous parallel and distributed computing systems, which are characterized by having various types of computational units interconnected via networks for executing multiple parallel jobs precedenceconstrained tasks. Scheduling multiple jobs, which arrive at different instants, on such systems for fastest execution is a wellknown NP-hard optimization problem. In order to achieve highperformance of systems, two important factors can be improved. One factor is the heterogeneity. Most algorithms use the upward rank value for ordering tasks and the earliest finish time for assigning processors. These two criteria can be improved to permit creating accurate and efficient schedules in heterogeneous distributed computing systems. Another factor is the fairness, existing algorithms are for static scheduling, and failed to make full use of the fairness in dynamic environments, such that obvious unfairness to longer-makespan jobs or shorter-makespan jobs can be caused. A dynamic multiple parallel jobs scheduling algorithm called F DMHSV (Fairness of Dynamic Multiple Heterogeneous Selection Value) is proposed to address the above problems to achieve high-performance of systems in this paper. Both example and extensive experimental evaluation demonstrate significant improvement of the F DMHSV algorithm.

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Automated Scheduling for Tightly-Coupled Embedded Multi-core Systems Using Hybrid Genetic Algorithms

Cuadra

Krawczyk

Höttger

et al. 2017

Communications in Computer and Information Science

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A High-Performance DAG Task Scheduling Algorithm for Heterogeneous Networked Embedded Systems

Cited by 13 publications

References 15 publications

Scheduling trade‐off of dynamic multiple parallel workflows on heterogeneous distributed computing systems

Scheduling trade‐off of dynamic multiple parallel workflows on heterogeneous distributed computing systems

Schedule Dynamic Multiple Parallel Jobs with Precedence-Constrained Tasks on Heterogeneous Distributed Computing Systems

Automated Scheduling for Tightly-Coupled Embedded Multi-core Systems Using Hybrid Genetic Algorithms

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