Scheduling and power management in energy harvesting computing systems with real-time constraints

Chetto, Maryline; Ghor, Hussein El

doi:10.1016/j.sysarc.2019.06.002

Cited by 15 publications

(12 citation statements)

References 11 publications

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“…To ensure that the simulation experiment results can be close to the real environment, this article uses the data from the real system PlanetLab trace as the experimental data 40 . Launched in 2003, the PlanetLab project includes 1160 compute nodes in 25 countries around the world.…”

Section: Experimental Results and Analysismentioning

confidence: 99%

An energy efficient and resource‐constrained scheduling framework for smart city application

Jing

Miao

Song

et al. 2020

Trans Emerging Tel Tech

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Summary With the development of smart Internet of things devices, intelligent applications are expected to lead further innovation in smart city. However, although cloud computing infrastructure can be used to meet traditional challenges, the scheduling model for new big data intelligent application has still not matured. In this work, we proposed a two‐stage scheduling framework for smart city intelligent application. In the first stage, we propose a virtual machine selection algorithm for edge computing to enhance relative migration benefits. The algorithm defines the invalid virtual machine migration and relative migration benefits from the change in the overall computing resources of the cloud data center after the virtual machine migration. In the second stage, we proposed an energy efficient and resource‐constrained scheduling framework for edge computing. The historical data of the cloud and edge computing workload of the computing node are processed in a sliding window manner, and the median absolute deviation of the historical data is used as the base of the physical reserved resource constraint when the base also changes as the workload changes. The experimental results show that energy‐efficient and resource‐constrained can make the computer resource provide high‐quality services for users in a low‐energy state.

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Section: Experimental Results and Analysismentioning

confidence: 99%

An energy efficient and resource‐constrained scheduling framework for smart city application

Jing

Miao

Song

et al. 2020

Trans Emerging Tel Tech

View full text Add to dashboard Cite

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“…Further, Sinha and Chandrakasan [145] have proposed a scheme for enhancing the energy efficiency of nodes which is actually a power management scheme based on the microoperating system. Next, Chetto and Ghor [146] have analyzed the energy harvesting system having dynamic power management capabilities for selecting the appropriate scheduler on a uniprocessor platform with applicative conditions. The mobility-enabled WSNs are suffering from higher latency factors in specific activities towards the collection of data.…”

Section: Schemes Based On Optimization Of Battery Operationmentioning

confidence: 99%

Green Communication for Next‐Generation Wireless Systems: Optimization Strategies, Challenges, Solutions, and Future Aspects

Rathore

Sangwan

Kaiwartya

et al. 2021

Wireless Communications and Mobile Computing

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Wireless sensor networks (WSNs) have emerged as a backbone technology for the wireless communication era. The demand for WSN is rapidly increasing due to their major role in various applications with a wider deployment and omnipresent nature. The WSN is rapidly integrated into a large number of applications such as industrial, security, monitoring, tracking, and applications in home automation. The widespread use in many different areas attracts research interest in WSNs. Therefore, researchers are taking initiatives in exploring innovation day by day particularly towards the Internet of Things (IoT). But, WSN is having lots of challenging issues that need to be addressed, and the inherent characteristics of WSN severely affect the performance. Energy constraints are one of the primary issues that require urgent attention from the research community. Optimal energy optimization strategies are needed to counter the issue of energy constraints. Although one of the most appropriate schemes for handling energy constraints issues is the appropriate energy harvesting technique, the optimal energy optimization strategies should be coupled together for effectively utilizing the harvested energy. In this high-level systematic and taxonomical survey, we have organized the energy optimization strategies for EH-WSNs into eleven factors, namely, radio optimization schemes, optimizing the energy harvesting process, data reduction schemes, schemes based on cross-layer optimization, schemes based on cross-layer optimization, sleep/wake-up policies, schemes based on load balancing, schemes based on optimization of power requirement, optimization of communication mechanism, schemes based on optimization of battery operations, mobility-based schemes, and finally energy balancing schemes. We have also prepared the summarized view of various protocols/algorithms with their remarkable details. This systematic and taxonomy survey also provides a progressive detailed overview and classification of various optimization challenges for the EH-WSNs that require attention from the researcher followed by a survey of corresponding solutions for corresponding optimization issues. Further, this systematic and taxonomical survey also provides a deep analysis of various emerging energy harvesting technologies in the last twenty years of the era.

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“…T i ⟵ period of τ i (8) sum ⟵ E i ×⌈hp/T i ⌉ + sum (9) end for (10) if (sum−hp×R) < 0 then (11) return false (12) else (13) return true (14) end if (15) end function ALGORITHM 1: Holistic energy evaluation.…”

Section: Ac-ielb Algorithmmentioning

confidence: 99%

“…T i ⟵ period of τ i (9) sum ⟵ E i ×⌈wt/T i ⌉ + sum (10) end for (11) if (wt×R−sum) < 0 then (12) if |wt × R − sum| ≥ C max then (13) returnC max ( 14) else (15) return |wt × R − sum| (16) end if (17) end if (18) end function (19)…”

Section: Input: a ⟵ Set Of N Active Tasks At Time Tmentioning

confidence: 99%

“…is technology harvests ambient energy and converts it into electrical energy for direct use in an embedded system or stores it in a storage module (i.e., battery) for future uses. e benefit of this approach is that it can increase system running time and eliminate the demand of battery replacement [9]. We refer to an embedded system that uses energy-harvesting technology as an energy-harvesting embedded system (EHES).…”

Section: Introductionmentioning

confidence: 99%

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Analysis and Design of the Battery Initial Energy Level with Task Scheduling for Energy-Harvesting Embedded Systems

Miao

Wei

2021

Complexity

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When the energy-harvesting embedded system (EHES) is running, its available energy (harvesting energy and battery storage energy) seems to be sufficient overall. However, in the process of EHES task execution, an energy shortage may occur in the busy period such that system tasks cannot be scheduled. We call this issue the energy deception (ED) of the EHES. Aiming to address the ED issue, we design an appropriate initial energy level of the battery. In this paper, we propose three algorithms to judge the feasibility of the task set and calculate the appropriate initial energy level of the battery. The holistic energy evaluation (HEE) algorithm makes a preliminary judgment of the task set feasibility according to available energy and consumption energy. A worst-case response time-based initial energy level of the battery (WCRT-IELB) algorithm and an accurate cycle-initial energy level of the battery (AC-IELB) algorithm can calculate the proper initial battery capacity. We use the YARTISS tool to simulate the above three algorithms. We conducted 250 experiments on As Late As Possible (ALAP) and As Soon As Possible (ASAP) scheduling with the maximum battery capacities of 50, 100, 200, 300, and 400. The experimental results show that setting a reasonable initial energy level of the battery can effectively improve the feasibility of the task set. Among the 250 task sets, the HEE algorithm filtered 2.8% of them as infeasible task sets. When the battery capacity is set to 400, the WCRT-BIEL algorithm increases the success rates of the ALAP and ASAP by 17.2% and 26.8%, respectively. The AC-BIEL algorithm increases the success rates of the ALAP and ASAP by 18% and 26.8%, respectively.

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Scheduling and power management in energy harvesting computing systems with real-time constraints

Cited by 15 publications

References 11 publications

An energy efficient and resource‐constrained scheduling framework for smart city application

An energy efficient and resource‐constrained scheduling framework for smart city application

Green Communication for Next‐Generation Wireless Systems: Optimization Strategies, Challenges, Solutions, and Future Aspects

Analysis and Design of the Battery Initial Energy Level with Task Scheduling for Energy-Harvesting Embedded Systems

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