TAMER: an adaptive task allocation method for aging reduction in multi-core embedded real-time systems

Saadatmand, Faezeh Sadat; Rohbani, Nezam; Baharvand, Farshad; Farbeh, Hamed

doi:10.1007/s11227-020-03326-7

Cited by 7 publications

(3 citation statements)

References 54 publications

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“…Emerging memory technologies, particularly those based on novel materials, are poised to address the limitations of traditional memory solutions and catalyze advancements in the IoT and edge computing, embedded devices, and high-performance multi-core systems. [49][50][51][52][53][54] PCM emerges as a particularly promising candidate, offering a compelling array of attributes that make it well-suited for energy-constrained environments typical of IoT edge devices. PCM utilizes chalcogenide glass compounds, such as Ge2Sb2Te5 (GST), which exhibit the ability to transition between distinct physical states-amorphous and polycrystalline-through the application of Joule heating.…”

Section: Phase Change Memorymentioning

confidence: 99%

Optical memory in IoT devices: an energy-efficient approach using phase change materials

Jahannia,

Ye,

Altaleb

et al. 2024

AI and Optical Data Sciences V

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As Internet-of-Things (IoT) devices continue to grow rapidly in number, developing energy-efficient memory solutions has become critically important. This paper introduces an innovative Phase Change Memory (PCM) architecture that can significantly reduce memory energy consumption in IoT devices. After highlighting the energy-inefficiency of current memory designs, we explore the possibilities of leveraging PCM. We demonstrate that the benefits of exploiting PCM are dependent on the working frequency of the CPU and show how PCM can surpass devices with SRAM and DRAM. As a replacement candidate for FLASH, PCM can also be utilized instead of SRAM and DRAM. We also demonstrate that, based on the application, we can also save more energy. Ongoing work focuses on deployment of this application dependency and enhancing energy efficiency of devices using PCM. This represents a major advance towards realizing widespread integration of photonics and electronics in IoT hardware.

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Section: Phase Change Memorymentioning

confidence: 99%

Optical memory in IoT devices: an energy-efficient approach using phase change materials

Jahannia,

Ye,

Altaleb

et al. 2024

AI and Optical Data Sciences V

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“…Allocation of the tasks in a system is a crucial step at the execution of initial stage for an application. The system may leads to get a catastrophe atmosphere, if tasks are not properly allocated and executed [4]. Time constraint is one of the major observation in different types of scheduling tasks.…”

Section: Introductionmentioning

confidence: 99%

Boosted System Performance based on Execution Time Using LiMca Scheduling Algorithm

Sreenath¹,

Vijaya²

2021

JUSST

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The scheduling algorithms have been examined by the process of task execution in a system to achieve maximum utilization of multiprocessors. Consequently, the research attempted to propose a new real-time scheduling algorithm to support a multiprocessor platform. The proposed scheduling algorithm, List Mcnaughton’s amalgamation (LiMca) scheduling algorithm has been developed for an optimum solution with the features of List and Mcnaughton’s scheduling algorithms to overcome the individual drawbacks (pre-emption and Precedence constraints). In LiMca, Workload has been distributed to the processors in a system by sorting the tasks in decreasing order with their precedence constraints including due dates, pre-emption, and context switching. In this arena, the LiMca scheduling algorithm has been developed on a traditional avionic mission system and also simulated on a real-time application. The extensive simulations were carried out on the time optimization of resources scheduling (TORSCHE) simulation toolbox. LiMca scheduling algorithm performed superior as compared to recently reported scheduling algorithms like list, hu’s, McNaughton’s, Brucker’s, and Hodgson’s in terms of computational performance.

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“…They are equipped with real-time system awareness environment data [12,13]. The real-time task scheduling algorithm directly determines the performance of the real-time system [14,15], so it is available how to design a real-time task scheduling algorithm.…”

Section: Introductionmentioning

confidence: 99%

Multiple Wirelessly Powered Sensing Platform Scheduling Algorithm Based on Dynamic Priority Preemption

et al. 2021

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The available energy of a wirelessly powered sensing platform is not enough, and there are constant real-time tasks to join the wirelessly powered sensing platform to run. So the wirelessly powered sensing system composed of many wirelessly powered sensing platforms is easy to enter the overloaded state, which may cause some tasks not to be executed on time. Therefore, to obtain as much task value as possible for the wirelessly powered sensing system when it is under the overloaded state, it is essential to design a reasonable task scheduling algorithm to arrange the task execution order. In this paper, we propose a policy named Wirelessly Dynamic Allocation Priority (WDAP) policy suitable for the wirelessly powered sensing system. The proposed WDAP is divided into a dynamic task priority allocation policy and a dynamic node priority allocation policy. Firstly, this paper analyzes the dynamic value density based on task value and execution time, studies the urgency of execution according to the execution time and the remaining idle time, and proposes the energy intensity through the task energy consumption and execution time. Based on the three impact factors of dynamic value density, urgency, and energy intensity, a policy for dynamic task priority allocation is proposed. Then, a policy for dynamic node priority allocation is proposed by combining the available energy and the energy acquisition speed of the nodes. Finally, the algorithm suitable for the wirelessly powered sensing system is proposed named Wirelessly Dynamic Real-time Task Scheduling (WDRTS) algorithm based on the WDAP. The algorithm clarifies the execution order of each task, responds to high-priority tasks first, and effectively guarantees task benefits. The experimental results show that compared with the main algorithms used in the literature among which is Generalized Earliest Deadline First, the WDRTS algorithm reduces the number of preemptive tasks by at least 36.49% and increases the successful scheduling rate of tasks by at least 15.17% and the overall system task income by at least 16.37% under high load.

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TAMER: an adaptive task allocation method for aging reduction in multi-core embedded real-time systems

Cited by 7 publications

References 54 publications

Optical memory in IoT devices: an energy-efficient approach using phase change materials

Optical memory in IoT devices: an energy-efficient approach using phase change materials

Boosted System Performance based on Execution Time Using LiMca Scheduling Algorithm

Multiple Wirelessly Powered Sensing Platform Scheduling Algorithm Based on Dynamic Priority Preemption

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