Task-set generator for schedulability analysis using the TACLebench benchmark suite

Bock, Yorick De; Altmeyer, Sebastian; Huybrechts, Thomas; Broeckhove, J.; Hellinckx, Peter

doi:10.1145/3199610.3199613

Cited by 8 publications

(4 citation statements)

References 4 publications

(4 reference statements)

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Section: Discussionmentioning

confidence: 99%

“…Allocating these tasks to a dedicated processor, I/O processor, and isolating them from the other short period processing tasks will increase system schedulability; we are leaving this for future work. The performance comparison of our proposed algorithm with other similar techniques on LITMUS RT 33 using real workload 54 and other synchronization protocols such as MSRP and MrsP 3 is another possibility of future work. From the schedulability point of view, we have used partitioned scheduling in this work, and we will investigate the performance of SRTA algorithm with global, semi‐partitioned and hierarchical scheduling protocols.…”

Section: Discussionmentioning

confidence: 99%

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Real‐time partitioned scheduling: Exploiting the inter‐resource affinity for task allocation on multiprocessors

Akram

Bai

et al. 2019

Concurrency and Computation

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Real-time edge computing is forging its place in cloud computing rapidly, and requirements for high-performance edge devices are becoming increasingly complex. Multiprocessor edge devices are an attractive choice to meet these higher performance requirements. However, multiprocessor devices encounter inherent challenges when handling on-chip shared resources. The concurrent access to these resources by the tasks requesting more than one shared resource and running on multiple processors may face huge blocking times, which can lead to missed hard real-time deadlines and cause a catastrophic system failure. To reduce the task blocking time, we propose a task allocation algorithm that takes advantage of inter-resource affinity and allocates all the tasks accessing multiple shared resources having inter-resource affinity on the same processor of the multiprocessor. The proposed algorithm reduces global resources and remote blocking, which subsequently increase schedulability of task sets and reduce the processor utilization. In our experiments, we compare the proposed task allocation algorithm SRTA with existing well-known task allocation strategies SPA, ROP, and blocking-agnostic FFD. The experimental results reveal that the SRTA, on the average, can allocate 1.80, 2.34, and 3.14 times more task sets as compared to ROP, SPA, and FFD, respectively and reduces the number of globally shared resources significantly.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Real‐time partitioned scheduling: Exploiting the inter‐resource affinity for task allocation on multiprocessors

Akram

Bai

et al. 2019

Concurrency and Computation

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“…Recent developments have furthermore explored the usage of different machine learning techniques, such as reinforcement learning and shallow learning, to distribute tasks among several components [20,21]. However, more research is required on the idea of online task migration with machine learning.…”

Section: Related Workmentioning

confidence: 99%

A Generalistic Approach to Machine-Learning-Supported Task Migration on Real-Time Systems

Delgadillo

Blieninger

Kuhn

et al. 2022

JLPEA

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Consolidating tasks to a smaller number of electronic control units (ECUs) is an important strategy for optimizing costs and resources in the automotive industry. In our research, we aim to enable ECU consolidation by migrating tasks at runtime between different ECUs, which adds redundancy and fail-safety capabilities to the system. In this paper, we present a setup with a generalistic and modular architecture that allows for integrating and testing different ECU architectures and machine learning (ML) models. As part of a holistic testbed, we introduce a collection of reproducible tasks, as well as a toolchain that controls the dynamic migration of tasks depending on ECU status and load. The migration is aided by the machine learning predictions on the schedulability analysis of possible future task distributions. To demonstrate the capabilities of the setup, we show its integration with FreeRTOS-based ECUs and two ML models—a long short-term memory (LSTM) network and a spiking neural network—along with a collection of tasks to distribute among the ECUs. Our approach shows a promising potential for machine-learning-based schedulability analysis and enables a comparison between different ML models.

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“…This framework is a collection of tools to examine the behaviour of code on different computing platforms [15]. The current implementation provides insights for embedded developers in three categories: WCRC analysis, scheduler optimisation [16] and design pattern-based performance optimisation for multi-core processors. In previous research, we implemented the hybrid methodology to estimate the WCET on embedded devices [15].…”

Section: Worst-case Resource Analysismentioning

confidence: 99%

Automated Testbench for Hybrid Machine Learning-Based Worst-Case Energy Consumption Analysis on Batteryless IoT Devices

et al. 2021

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Batteryless Internet-of-Things (IoT) devices need to schedule tasks on very limited energy budgets from intermittent energy harvesting. Creating an energy-aware scheduler allows the device to schedule tasks in an efficient manner to avoid power loss during execution. To achieve this, we need insight in the Worst-Case Energy Consumption (WCEC) of each schedulable task on the device. Different methodologies exist to determine or approximate the energy consumption. However, these approaches are computationally expensive and infeasible to perform on all type of devices; or are not accurate enough to acquire safe upper bounds. We propose a hybrid methodology that combines machine learning-based prediction on small code sections, called hybrid blocks, with static analysis to combine the predictions to a final upper bound estimation for the WCEC. In this paper, we present our work on an automated testbench for the Code Behaviour Framework (COBRA) that measures and profiles the upper bound energy consumption on the target device. Next, we use the upper bound measurements of the testbench to train eight different regression models that need to predict these upper bounds. The results show promising estimates for three regression models that could potentially be used for the methodology with additional tuning and training.

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Task-set generator for schedulability analysis using the TACLebench benchmark suite

Cited by 8 publications

References 4 publications

Real‐time partitioned scheduling: Exploiting the inter‐resource affinity for task allocation on multiprocessors

Real‐time partitioned scheduling: Exploiting the inter‐resource affinity for task allocation on multiprocessors

A Generalistic Approach to Machine-Learning-Supported Task Migration on Real-Time Systems

Automated Testbench for Hybrid Machine Learning-Based Worst-Case Energy Consumption Analysis on Batteryless IoT Devices

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