Efficient Task Allocation for Real-Time Partitioned Scheduling on Multi-Core Systems

Akram, Naveed; Zhang, Yangyang; Ali, Shahbaz; Amjad, Hafiz Muhammad

doi:10.1109/ibcast.2019.8667139

Cited by 10 publications

(4 citation statements)

References 26 publications

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“…Additionally, the algorithms were classified according to various scheduling strategies, including Global, Semi-Partitioned, and Partitioned scheduling. The article [34] proposed the Inter-task Affinity-aware Task Allocation (IATA) algorithm that groups tasks depending on their preferences, dependencies, and constraints and allocates these groups to various cores to reduce WCET overheads. The authors in [35] proposed a novel scheduling algorithm called Resource-Oriented Partitioned (ROP), which employs a distributed resource-sharing policy based on the Distributed Priority Ceiling Protocol (DPCP), which can guarantee a significant speedup factor, indicating a substantial performance improvement.…”

Section: Related Workmentioning

confidence: 99%

Energy-aware Real-time Task Partitioning on Multi-core Processors with Shared Resources

Konswa,

El-Henawy,

Abdelatif

2024

Preprint

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Nowadays, multi-core processors are increasingly adopted in embedded systems. These processors can achieve energy consumption minimization by employing dynamic voltage/frequency scaling techniques (DVFS). Several energy-aware real-time task partitioning algorithms have been suggested for multicore processors. While many of these algorithms focus on independent real-time tasks, there has been relatively limited research dedicated to task synchronization. This paper focuses on optimizing energy consumption by assigning dependent real-time tasks to a multi-core processor. When multiple tasks on different cores access shared resources simultaneously, it can result in longer blocking times, consequently increasing the execution time of tasks. This situation can result in missing hard deadlines, potentially causing system failure. The Highest Task-Based Partitioning (HTBP) algorithm is structured to decrease overall energy consumption while ensuring deadlines are met. It allocates tasks with high similarity (accessing the same set of resources) to the same core, effectively minimizing the occurrence of remote blockings. In the evaluation of the HTBP algorithm, we compared it with similarity-based partitioning (SBP), worst-fit decreasing (WFD) and best-fit decreasing (BFD). Our results indicate that our proposed (HTBP) algorithm outperforms SBP, WFD, and BFD algorithms (bin-packing algorithms), minimizes the overall energy dissipation, and improves schedulability.

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Section: Related Workmentioning

confidence: 99%

Energy-aware Real-time Task Partitioning on Multi-core Processors with Shared Resources

Konswa,

El-Henawy,

Abdelatif

2024

Preprint

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“…Regarding the sets of tasks that are considered schedulable, their empirical tests reveal that CITTA performs better than global EDF scheduling. By grouping tasks according to their constraints, dependencies, as well as preferences, and then allocating these groups over several cores, the authors of [13] suggest an Inter-task Affinity-aware Tasks Assignment (IATA) method that minimises the additive overheads in WCET. Dedicated I/O cores handle sluggish I/O peripherals, while scratch-pad RAM is used for high-priority activities that share datasets in order to decrease cache evictions.…”

Section: Related Workmentioning

confidence: 99%

Examining partitioned caches performance in heterogeneous multi-core processors

Yenugula

2022

Int. J. Commun. Inf. Technol.

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The last-level cache (LLC) is shared by many distinct kinds of cores in asymmetric multi-core systems. There is greater rivalry in the LLC since different core types have different memory access needs. Our new technique for replacing the split cache, HAPC, takes heterogeneity into account. To improve coreto-core interference, this method uses cache partitioning. In multithreaded applications, it guides the replacement strategy by monitoring the shared reuse state of every cache block inside the partition during runtime. This ensures that cache blocks shared among different cores are maintained. For huge cores to make more efficient LLC visits, cache replacement algorithms generally modify the leftover state while preserving cache blocks needed by them. This approach takes into consideration the fact that heterogeneous cores have different memory accesses to LLC. In comparison to the state-of-the-art cache substitute's algorithms, LRU and SRCP, HAPC can greatly enhance the performance of large cores when running multithreaded applications, with almost no impact on small cores, leading to an overall improvement in system performance.

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“…They summed up several algorithms listed in the literature and grouped them by both homogeneous and heterogeneous multi-core processors, depending on Partitioned, Semi-Partitioned, and Global scheduling strategies. An Inter-task Affinity-aware Task Allocation (IATA) algorithm was proposed in [21] to nullify overheads in the WCET due to cache evictions. IATA collects the tasks considering their constraints, dependencies, preferences (shared resources, inter-core communication, and cache evictions) and assigns these groups to multiple cores to decrease the additive overheads in WCET.…”

Section: Related Workmentioning

confidence: 99%

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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Efficient Task Allocation for Real-Time Partitioned Scheduling on Multi-Core Systems

Cited by 10 publications

References 26 publications

Energy-aware Real-time Task Partitioning on Multi-core Processors with Shared Resources

Energy-aware Real-time Task Partitioning on Multi-core Processors with Shared Resources

Examining partitioned caches performance in heterogeneous multi-core processors

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

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