Efficient scheduling of task graph collections on heterogeneous resources

Gallet, Matthieu; Marchal, Loris; Vivien, Frédéric

doi:10.1109/ipdps.2009.5161045

Cited by 14 publications

(39 citation statements)

References 22 publications

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“…This must be done in a way that respects the precedence constraints among tasks. An efficient schedule is one that minimizes the total execution time, or the schedule length, of the parallel application [15][16][17][18][19][20][21][22][23]. www.ijacsa.thesai.org…”

Section: Problem Definitionmentioning

confidence: 99%

Performance Enhancement of Scheduling Algorithm in Heterogeneous Distributed Computing Systems

Nasr¹,

El‐Bahnasawy²,

El‐Sayed³

2015

ijacsa

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Abstract-Efficient task scheduling is essential for obtaining high performance in heterogeneous distributed computing systems. Some algorithms have been proposed for both homogeneous and heterogeneous distributed computing systems. In this paper, a new static scheduling algorithm is proposed called Node Duplication in Critical Path (NDCP) algorithm to schedule the tasks efficiently on the heterogeneous distributed computing systems. The NDCP algorithm focuses on reducing the makespan and provides better performance than the other algorithms in metrics of speedup and efficiency. It consists of two phases, priority phase and processor selection phase. From the theoretical analysis of the NDCP algorithm with other algorithms for a Directed Acyclic Graph (DAG), the better performance is observed.

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Section: Problem Definitionmentioning

confidence: 99%

Performance Enhancement of Scheduling Algorithm in Heterogeneous Distributed Computing Systems

Nasr¹,

El‐Bahnasawy²,

El‐Sayed³

2015

ijacsa

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“…Iverson and Özgüner [10] propose an approach to minimize the average makespan in a heterogeneous environment with a Poisson arrival process of DAG-type workflows. Gallet et al [11] aim at maximizing the throughput, which corresponds to minimizing the response time in our formulation. However, theirs is an offline deployment algorithm, therefore the same for all workflow instances, and assumes deterministic task processing and communication times.…”

Section: Introductionmentioning

confidence: 99%

Resource Allocation for Multiple Cloud Workflows Under Execution Time Uncertainty

Wosko

Moser

Mansour

2014

Advances in Intelligent Systems and Computing

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We investigate the problem of scheduling tasks in a cloud computing scenario that involves a queue of workflow instances whose arrival into the system is governed by a stochastic process. We assume that each instance conforms to the same, known structure, expressed by a directed acyclic graph. Within this job model, the precise execution time of each atomic task and delay of each communication edge may be different and is known only in terms of probability distributions. We argue that in many serial information processing scenarios, minimizing the total response time of the system, i.e., the sum of the execution time and in-queue waiting time, is a more important objective than reducing the schedule length alone, as predominantly done in other approaches. We propose novel algorithms for the minimization of the expected response time and investigate the issue of estimation of the stability of the system. We show that for the given objective, greedy algorithms usually perform better at lower instance arrival rates or when the total amount of instances is limited. However, we also show that an algorithm that minimizes only the expected service time of an instance makes the system more stable and that it must be a non-greedy algorithm.

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“…It does this by considering the ratio between the code size of the tasks and the amount of data that is needed by the tasks. Data-intensive tasks [3] will be scheduled in a data-driven manner and code-intensive tasks [3] will be scheduled in a code-driven manner.…”

Section: Introductionmentioning

confidence: 99%

Hybrid Code-Data Prefetch-Aware Multiprocessor Task Graph Scheduling

Damavandpeyma

Stuijk

Basten

et al. 2011

2011 14th Euromicro Conference on Digital System Design

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Abstract-The ever increasing performance gap between processors and memories is one of the biggest performance bottlenecks for computer systems. In this paper, we propose a task scheduling technique that schedules an application, modeled with a task graph, on a multiprocessor system-onchip (MPSoC) that contains a limited on-chip memory. The proposed scheduling technique explores the trade-off between executing tasks in a code-driven (i.e. executing parallel tasks) or data-driven (i.e. executing pipelined tasks) manner to minimize the run-time of the application. Our static scheduler identifies those task sequences in which it is useful to use a codedriven execution and those task sequences that benefit from a data-driven execution. We extend the proposed technique to consider prefetching when choosing a suitable task order. The technique is implemented using an integer linear programming framework. To evaluate the effectiveness of the technique, we use an application from the multimedia domain and a synthetic task graph that is used in related work. Our experimental results show that our scheduler is able to reduce the run-time of an MP3 decoder application by 8% compared to a commonly used heuristic scheduler.

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Efficient scheduling of task graph collections on heterogeneous resources

Cited by 14 publications

References 22 publications

Performance Enhancement of Scheduling Algorithm in Heterogeneous Distributed Computing Systems

Performance Enhancement of Scheduling Algorithm in Heterogeneous Distributed Computing Systems

Resource Allocation for Multiple Cloud Workflows Under Execution Time Uncertainty

Hybrid Code-Data Prefetch-Aware Multiprocessor Task Graph Scheduling

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