Resource-Oriented Partitioning for Multiprocessor Systems with Shared Resources

Yang, Maolin; Huang, Wen-Hung; Chen, Jian-Jia

doi:10.1109/tc.2018.2889985

Cited by 18 publications

(11 citation statements)

References 43 publications

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“…It has been shown that the relatively simple Min-min heuristic achieves minimum energy in comparison with the other strategies for the cases studied. The article [25] showed that the proposed Resource-Oriented Partitioned (ROP) scheduling with a distributed resource sharing strategy would achieve a significant speed-up factor guarantee. The authors of [26] aimed to reduce energy consumption under real-time and reliability constraints.…”

Section: Related Workmentioning

confidence: 99%

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Energy-Efficient Task Partitioning for Real-Time Scheduling on Multi-Core Platforms

et al. 2021

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Multi-core processors have become widespread computing engines for recent embedded real-time systems. Efficient task partitioning plays a significant role in real-time computing for achieving higher performance alongside sustaining system correctness and predictability and meeting all hard deadlines. This paper deals with the problem of energy-aware static partitioning of periodic, dependent real-time tasks on a homogenous multi-core platform. Concurrent access of the tasks to shared resources by multiple tasks running on different cores induced a higher blocking time, which increases the worst-case execution time (WCET) of tasks and can cause missing the hard deadlines, consequently resulting in system failure. The proposed blocking-aware-based partitioning (BABP) algorithm aims to reduce the overall energy consumption while avoiding deadline violations. Compared to existing partitioning strategies, the proposed technique achieves more energy-saving. A series of experiments test the capabilities of the suggested algorithm compared to popular heuristics partitioning algorithms. A comparison was made between the most used bin-packing algorithms and the proposed algorithm in terms of energy consumption and system schedulability. Experimental results demonstrate that the designed algorithm outperforms the Worst Fit Decreasing (WFD), Best Fit Decreasing (BFD), and Similarity-Based Partitioning (SBP) algorithms of bin-packing algorithms, reduces the energy consumption of the overall system, and improves schedulability.

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Section: Related Workmentioning

confidence: 99%

“…Then, the algorithm arranges tasks in non-increasing order based on their cost function V, grouped by shared resources (lines [23][24][25][26][27][28][29][30][31][32]. The output of BABP is the task set where the tasks are sorted depending on their cost function.…”

Section: Blocking-aware-based Partitioning (Babp) Algorithmmentioning

confidence: 99%

Energy-Efficient Task Partitioning for Real-Time Scheduling on Multi-Core Platforms

et al. 2021

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“…The latter seeks to partition a graph into even-sized components while minimizing the number of edges. Figure 1 summarizes other problems that can be modeled as Balanced Partition, including: People Assignment [34], Routing [31], [35]- [38], Task Allocation [39], [40], File Placement [41], [42] and Scheduling [43]- [45]. We briefly describe them in the following lines.…”

Section: A Balanced Partition (Bp)mentioning

confidence: 99%

“…Here, the goal is to improve the parallel execution efficiency of tasks that share resources (e.g., external devices, shared memory, and files). To solve this problem, there exist multiple paradigms, such as a Partitioned Schedule [43], [45], a Global Schedule [46], and a Semi-partitioned Schedule [44].…”

Section: A Balanced Partition (Bp)mentioning

confidence: 99%

Sequence-Based Selection Hyper-Heuristic Model via MAP-Elites

et al. 2021

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Combinatorial optimization problems (COPs) are paramount for real-life problems with discrete variables. Even though the number of combinations is finite, some problems exhibit exponential growth, rendering exact approaches unfeasible. So, approximate methods, such as heuristics, are customary for making fast decisions. Despite their small computational cost, each heuristic specializes in specific kinds of problems. Hence, other approaches have appeared for merging their strengths. One of these approaches is commonly known as selection hyper-heuristics. However, a trained hyper-heuristic usually carries the following problem: it provides scarce information about its sensitivity. This makes it more difficult to estimate the effect of changing a parameter. Illumination algorithms seek to improve this issue by focusing on exploration rather than on exploitation while preserving information about the best solutions with different criteria. Still, literature falls short when merging both approaches, representing a knowledge gap. Therefore, in this work, we test the feasibility of using an illumination algorithm, the MAP-Elites (ME), for tuning a sequence-based selection hyper-heuristic model for Balanced Partition problems. We choose ME since it has been successfully applied to a different COP, and by linking it with hyper-heuristics, we may exploit upon its benefits. So, we may achieve a hyper-heuristic that represents the best combination of heuristics. Simultaneously, we can gain information about the expected performance of diverse hyperheuristics, e.g., those that begin with a different heuristic. Our approach operates by creating a multidimensional map, where each design variable represents the application of a given heuristic. Afterward, ME generates mutated sequences and tests them to determine if they represent a better-performing solution. To test our approach, we consider 1500 instances that include easy and hard instances, analyzed under different scenarios. We also include limit instances that are neither easy nor hard. Our resulting data supports the feasibility of the proposed approach. We show that this kind of hyper-heuristic can perform toe-to-toe with a synthetic oracle. Besides, under some conditions, the former may even outperform the latter. This represents an outstanding result, since such an oracle can only be obtained by a brute-force approach, and because it demonstrates that merging both approaches (ME and hyper-heuristics) is a path worth pursuing. We also present how each parameter affects the model performance, concluding that some parameters can be critical while others are virtually irrelevant. This is also a relevant result, since it serves as the groundwork for future works so that they can focus on exploiting the most relevant parameters.

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“…Real-time Scheduling. To schedule real-time tasks on multi-core platforms, different paradigms have been widely studied: partitioned [4,13,35], global [3,7,22], and semipartitioned scheduling [8,10,20]. A comprehensive survey of real-time scheduling for multiprocessor systems can be found in [12].…”

Section: Related Workmentioning

confidence: 99%

Citta

Xiao

Pimentel

2020

The 21st ACM SIGPLAN/SIGBED Conference on Languages, Compilers, and Tools for Embedded Systems

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Shared caches in multi-core processors introduce serious difficulties in providing guarantees on the real-time properties of embedded software due to the interaction and the resulting contention in the shared caches. Prior work has studied the schedulability analysis of global scheduling for real-time multi-core systems with shared caches. This paper considers another common scheduling paradigm: partitioned scheduling in the presence of shared cache interference. To achieve this, we propose CITTA, a cache-interference aware task partitioning algorithm. An integer programming formulation is constructed to calculate the upper bound on cache interference exhibited by a task, which is required by CITTA. We conduct schedulability analysis of CITTA and formally prove its correctness. A set of experiments is performed to evaluate the schedulability performance of CITTA against global EDF scheduling over randomly generated tasksets. Our empirical evaluations show that CITTA outperforms global EDF scheduling in terms of task sets deemed schedulable.CCS Concepts • Computer systems organization → Embedded software.

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Resource-Oriented Partitioning for Multiprocessor Systems with Shared Resources

Cited by 18 publications

References 43 publications

Energy-Efficient Task Partitioning for Real-Time Scheduling on Multi-Core Platforms

Energy-Efficient Task Partitioning for Real-Time Scheduling on Multi-Core Platforms

Sequence-Based Selection Hyper-Heuristic Model via MAP-Elites

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