Task scheduling algorithm using minimized duplications in homogeneous systems

Shin, Kyung-A; Cha, MyongJin; Jang, Mun-Suck; Jung, Jinha; Yoon, Wan-Oh; Choi, Sang-Bang

doi:10.1016/j.jpdc.2008.04.001

Cited by 42 publications

(15 citation statements)

References 16 publications

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“…Therefore, it can be concluded that the active power consumption for a processor under the voltage and frequency set (v j , f j ) is calculated through Equation 13. 2 highest ×f highest P proc.highest = ACv 2 highest f highest (13) Since the proposed algorithm adopts the task duplication strategy for scheduling a DAG with n tasks on DVFS-enabled processors, the total energy consumption can be calculated through Equation 14.…”

Section: Computation Energymentioning

confidence: 99%

EATSDCD: A green energy-aware scheduling algorithm for parallel task-based application using clustering, duplication and DVFS technique in cloud datacenters

Barzegar

Motameni

Movaghar

2019

IFS

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Energy consumption and performance metrics have become critical issues for scheduling parallel task-based applications in high-performance computing systems such as cloud datacenters. The duplication and clustering strategy, as well as Dynamic Voltage Frequency Scaling (DVFS) technique, have separately been concentrated on reducing energy consumption and optimizing performance parameters such as throughput and makespan. In this paper, a dual-phase algorithm called EATSDCD which is an energy efficient time aware has been proposed. The algorithm uses the combination of duplication and clustering strategies to schedule the precedence-constrained task graph on datacenter processors through DVFS. The first phase focuses on a smart combination of duplication and clustering strategy to reduce makespan and energy consumed by processors in an effort to execute Directed Acyclic Graph (DAG) while satisfying the throughput constraint. The main idea behind EATSDCD intended to minimize energy consumption in the second phase. After determining the critical path and specifying a set of dependent tasks in non-critical paths, the slack time for each task in non-critical paths was distributed among all dependent tasks in that path. Then, the frequency of DVFS-enabled processors is scaled down to execute non-critical tasks as well as idle and communication phases, without extending the execution time of tasks. Finally, a testbed is developed and different parameters are tested on the randomly generated DAG to evaluate and illustrate the effectiveness of EATSDCD. It was also compared against duplication and clustering-based algorithms and DVFS-based algorithms. In terms of energy consumption and makespan, the results show that our proposed algorithm can save up to 8.3% and 20% energy compared against Power Aware List-based Scheduling (PALS) and Power Aware Task Clustering (PATC) algorithms, respectively. Furthermore, there is 16% improvement over Parallel Pipeline Latency Optimization (PaPilo) algorithm with En cur = 1.2En min (G). In comparison with Reliability Aware Scheduling with Duplication (RASD) algorithm, the execution time has been reduced in heterogeneous environments.

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Section: Computation Energymentioning

confidence: 99%

EATSDCD: A green energy-aware scheduling algorithm for parallel task-based application using clustering, duplication and DVFS technique in cloud datacenters

Barzegar

Motameni

Movaghar

2019

IFS

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“…In a task graph, the t-level of node i is the length of the longest path from an entry node to i (excluding i). Here, the length of a path is the sum of all the node and edge weights along the path [39].…”

Section: Stepmentioning

confidence: 99%

Learning Based Genetic Algorithm for Task Graph Scheduling

Izadkhah

2019

Applied Computational Intelligence and Soft Computing

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Nowadays, parallel and distributed based environments are used extensively; hence, for using these environments effectively, scheduling techniques are employed. The scheduling algorithm aims to minimize the makespan (i.e., completion time) of a parallel program. Due to the NP-hardness of the scheduling problem, in the literature, several genetic algorithms have been proposed to solve this problem, which are effective but are not efficient enough. An effective scheduling algorithm attempts to minimize the makespan and an efficient algorithm, in addition to that, tries to reduce the complexity of the optimization process. The majority of the existing scheduling algorithms utilize the effective scheduling algorithm, to search the solution space without considering how to reduce the complexity of the optimization process. This paper presents a learner genetic algorithm (denoted by LAGA) to address static scheduling for processors in homogenous computing systems. For this purpose, we proposed two learning criteria named Steepest Ascent Learning Criterion and Next Ascent Learning Criterion where we use the concepts of penalty and reward for learning. Hence, we can reach an efficient search method for solving scheduling problem, so that the speed of finding a scheduling improves sensibly and is prevented from trapping in local optimal. It also takes into consideration the reuse idle time criterion during the scheduling process to reduce the makespan. The results on some benchmarks demonstrate that the LAGA provides always better scheduling against existing well-known scheduling approaches.

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“…Algorithms in this category are typically static and thus inappropriate to dynamic cases. Duplication-based scheduling algorithms [13], [14], [15], duplicate tasks across multiple processors to reduce communication overheads. The algorithms in this group are usually for an unbounded number of homogeneous processors and they have much higher complexity values than the algorithms in other groups.…”

Section: Related Workmentioning

confidence: 99%

Adaptive Scheduling of Task Graphs with Dynamic Resilience

Luo

Wang³

et al. 2017

IEEE Trans. Comput.

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This paper studies a scheduling problem of task graphs on a non-dedicated networked computing platform. The networked platform is characterized by a set of fully connected processors such as a multiprocessor system that can be shared by multiple tasks. Therefore, the computation and communication capacities of the computing platform dynamically fluctuate. To deal with this fluctuations for high performance task graph computing, we propose an online dynamic resilience scheduling algorithm called Adaptive Scheduling Algorithm (ASA) that bears certain distinct features compared to existing algorithms. First, the proposed algorithm deliberately assigns tasks to idle processors in multiple rounds to prevent any unfavorable decisions and also to avoid inefficient assignments of certain key tasks to slow processors. Second, the algorithm adopts task duplication as an attempt to minimize serious increase of schedule length due to unexpected processor slowdown. Finally, a look-ahead message transmission policy is applied to save communication time and further improve the overall performance. Performance evaluation results are presented to demonstrate the effectiveness and competitiveness of our approaches when compared with the existing algorithms.

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Task scheduling algorithm using minimized duplications in homogeneous systems

Cited by 42 publications

References 16 publications

EATSDCD: A green energy-aware scheduling algorithm for parallel task-based application using clustering, duplication and DVFS technique in cloud datacenters

EATSDCD: A green energy-aware scheduling algorithm for parallel task-based application using clustering, duplication and DVFS technique in cloud datacenters

Learning Based Genetic Algorithm for Task Graph Scheduling

Adaptive Scheduling of Task Graphs with Dynamic Resilience

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