Energy-efficient contention-aware application mapping and scheduling on NoC-based MPSoCs

Li, Dawei; Wu, Jie

doi:10.1016/j.jpdc.2016.04.006

Cited by 28 publications

(27 citation statements)

References 20 publications

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“…Compared to the state-of-the-art approach proposed by Li and Wu [24] that does not consider conditional precedence constraints, our approach using the ILP-based algorithm achieves an average improvement of 31% and a maximum improvement of 61%, and our approach using the polynomial-time heuristic achieves an average improvement of 20% and a maximum improvement of 46%. Furthermore, both our approach using the ILPbased algorithm and our approach using the polynomialtime heuristic run approximately three times faster than the state-of-the-art approach.…”

Section: )mentioning

confidence: 84%

“…Ghosh et al [13] consider a model similar to that of Huang et al [21] and propose an energy-aware task scheduling heuristic based on MILP relaxation and randomized rounding. Li et al [24] assume a NoC model with voltage scalable links. They propose a task mapping algorithm and a genetic algorithm-based task voltage/frequency assignment algorithm.…”

Section: Related Workmentioning

confidence: 99%

“…The wormhole switching [27], [24] and deterministic XY routing are used. We do not scale router frequencies as adjusting router frequencies makes the problem too complex.…”

Section: Modelsmentioning

confidence: 99%

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Energy-Aware Scheduling of Conditional Task Graphs on NoC-Based MPSoCs

Tariq¹,

Ishak²

2018

Proceedings of the 51st Hawaii International Conference on System Sciences

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We investigate the problem of scheduling a set of tasks with individual deadlines and conditional precedence constraints on a heterogeneous Network on Chip (NoC)-based Multi-Processor System-on-Chip (MPSoC) such that the total expected energy consumption of all the tasks is minimized, and propose a novel approach. Our approach consists of a scheduling heuristic for constructing a single unified schedule for all the tasks and assigning a frequency to each task and each communication assuming continuous frequencies, an Integer Linear Programming (ILP)-based algorithm and a polynomial time heuristic for assigning discrete frequencies and voltages to tasks and communications. We have performed experiments on 16 synthetic and 4 real-world benchmarks. The experimental results show that compared to the state-of-theart approach, our approach using the ILP-based algorithm and our approach using the polynomial-time heuristic achieve average improvements of 31% and 20%, respectively, in terms of energy reduction. compared to v j and traverse the same links that v j traverses. 4) If v j is a task node, compute its finish time ζ j = r j + t k,j , and insert unconditional directed edges from v j to unscheduled nodes concurrent to v j and mapped on the same processor where v j is mapped. 5) Delete v j from ReadySet and insert all ready nodes in G to ReadySet. Consider the CTG in Figure 1(b) and the MPSoC in Figure 2(a) where all the processors are identical. The execution times of tasks at the maximum processor frequency are t 1,1

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Section: )mentioning

confidence: 84%

Section: Related Workmentioning

confidence: 99%

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Energy-Aware Scheduling of Conditional Task Graphs on NoC-Based MPSoCs

Tariq¹,

Ishak²

2018

Proceedings of the 51st Hawaii International Conference on System Sciences

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“…Kumar et al proposed the use of genetic algorithm to find a suboptimal mapping solution in the Cloud to minimize energy consumption and execution time . Li and Wu adjusted the frequency of hosts' processors and network nodes (Network on Chip) to minimize total energy consumption. In a study by Lee and Zomaya, the frequency of hosts are adjusted to save energy depending on the resource requests.…”

Section: Related Workmentioning

confidence: 99%

“…Since to find a suboptimal mapping solution in the Cloud to minimize energy consumption and execution time. 34 Li and Wu 35 adjusted the frequency of hosts' processors and network nodes (Network on Chip) to minimize total energy consumption. In a study by Lee and Zomaya, 36 the frequency of hosts are adjusted to save energy depending on the resource FIGURE 5 The box plots for mean execution time (in seconds) of EAM in comparison to ThrMMT, 8 MOH, 26 ThrMaxUtil, 13 Although the above-mentioned migration strategies brought energy efficiency, they suggest static rule for resolving the imbalance in the system.…”

Section: Related Workmentioning

confidence: 99%

Energy‐efficient adaptive virtual machine migration mechanism for private Clouds

Sohrabi

Yang

Moser

et al. 2017

Concurrency and Computation

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Summary Private Cloud provides Cloud services with its relatively limited resources compared to public Clouds. Resources in private Clouds should be used energy efficiently. The resource utilization is determined by the assignment of virtual machines (VMs) to hosts. Because of the frequent changes in the resource requests on VMs, the system might become imbalanced, some hosts are overloaded/underloaded. Virtual machine migration is a solution to ease the imbalance problem. Virtual machine migration includes the selection of a VM for migration and decision upon where it should be taken to (mapped). In this work, VM selection and VM mapping are integrated and aimed to ease the imbalance problem for energy efficiency. Moreover, selection and mapping have become adaptive to ease imbalance, while optimizing energy consumption and adaptively responding to changes in the system. Our proposed adaptive mechanism applies Bayesian inference to estimate the likelihood of a VM migration decision, both VM selection for migration and VM mapping, optimizing energy consumption. The proposed mechanism is evaluated on CloudSim using PlanetLab workload on a heterogeneous Cloud. It is demonstrated to reduce energy consumption significantly, (on average) by 116%, while its total execution time is also, (on average) 5.39 times, shorter than the competing state‐of‐the‐art policies.

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Energy-Efficient Scheduling of Tasks with Conditional Precedence Constraints on MPSoCs

Tariq

Ishak

2019

Towards Integrated Web, Mobile, and IoT Technology

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Energy-efficient contention-aware application mapping and scheduling on NoC-based MPSoCs

Cited by 28 publications

References 20 publications

Energy-Aware Scheduling of Conditional Task Graphs on NoC-Based MPSoCs

Energy-Aware Scheduling of Conditional Task Graphs on NoC-Based MPSoCs

Energy‐efficient adaptive virtual machine migration mechanism for private Clouds

Energy-Efficient Scheduling of Tasks with Conditional Precedence Constraints on MPSoCs

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