Thermal-constrained energy efficient real-time scheduling on multi-core platforms

Sha, Shi; Wen, Wujie; Chaparro-Baquero, Gustavo A.; Quan, Gang

doi:10.1016/j.parco.2019.01.003

Cited by 7 publications

(5 citation statements)

References 18 publications

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“…The problem of finding a HRT multiprocessor scheduler which accounts for energy and thermal constraints was recently studied in [12]. The authors try to maximize workloadper-joule (WPJ) subject to a thermal constraint.…”

Section: Related Workmentioning

confidence: 99%

“…The downside of the heuristic approach in [12] is that it cannot ensure schedulability, and only explores a very small portion of the solution space, yielding sub-optimal results. Also, authors in [12] apply an abstraction of an RC thermal and power model at the chip level, whereas we can fine-tune thermal properties and dynamics by discretizing the MPSoC or the relevant part of it with prisms as small as desired [13], [14]. We provide a holistic formal model of the system, whereas RC power and thermal models do not provide a true integrated state model.…”

Section: Related Workmentioning

confidence: 99%

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Maximizing Utilization and Minimizing Migration in Thermal-Aware Energy-Efficient Real-Time Multiprocessor Scheduling

et al. 2021

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This work proposes CAlECs, a clustered scheduling system for MPSoCs subject to thermal and energy constraints. It calculates off-line a cyclic executive honoring temporal and thermal constraints, for a hard real-time (HRT) task set at minimum frequency to reduce consumed energy, minimizing context switches and migrations. It also provides an on-line controller able to manage system and task parametric variations and soft real-time (SRT) tasks, always meeting the HRT task set constraints and the system thermal bound. CAlECS maximizes CPU utilization to help avoid overprovisioning contributing to a low SWaP factor. Its modular design allows the utilization of different modeling and scheduling approaches, and makes the off-line and on-line components independent from each other to better suit the requirements of a specific system. We experimentally show that the cyclic executive provided by CAlECS for HRT task sets outperforms RUN, a reference off-line algorithm in terms of optimal number of context switches.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Maximizing Utilization and Minimizing Migration in Thermal-Aware Energy-Efficient Real-Time Multiprocessor Scheduling

et al. 2021

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“…Concerning the literature on the VSBPP, four main objective functions are usually considered. These are the minimum total bin usage (Xavier and Miyazawa, 2008;Kochetov and Kondakov, 2017), the minimization of the total size of used bins (Chu and La, 2001;Zhang, 2002;AlEnezi et al, 2016), the minimization of total unused bin capacity (Alves and de Carvalho, 2007;Bang-Jensen and Larsen, 2012), and total cost minimization resulting from selected bins (Sha et al, 2019;Anand and Guericke, 2020). It is seen in the literature that the cost function is defined in three different ways.…”

Section: Introductionmentioning

confidence: 99%

“…In addition to academic studies, VSBPP has many applications in practical life. Some of those are containerization (Baldi et al., 2014; Anand and Guericke, 2020; Liu et al., 2021), co‐printing (Peeters and Degraeve, 2004; Kochetov and Kondakov, 2017), multi‐media storage systems (Xavier and Miyazawa, 2008), cutting (Eısemann, 1957; Chu and La, 2001; Belov and Scheithauer, 2002; Alves and de Carvalho, 2008; Bang‐Jensen and Larsen, 2012), multi‐core task partitioning (Sha et al., 2019), and machinery (Bang‐Jensen and Larsen, 2012). In the original version of VSBPP, only the items to be packed and the dimensions of the bins are taken into account.…”

Section: Introductionmentioning

confidence: 99%

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An iterated greedy algorithm for the planning of yarn‐dyeing boilers

Demir

2022

Int Trans Operational Res

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Yarn dyeing is a critical link in the textile production chain that consumes the most time and energy. Today's dyeing shops receive hundreds of demands with thousands of different colors, different due dates, and different production requirements. This situation has made it very difficult for the human brain to create a minimum‐cost production plan by complying with the due dates. In this study, a real‐life problem of a company operating in the textile industry is discussed and a solution has been developed for the planning of yarn‐dyeing boilers. The application was held in Bursalı Textile, which is the major towel manufacturer operating in Turkey. The problem dealt with is basically in the nature of the variable‐size bin‐packing problem (VSBPP). The limited availability of bins (boilers) of different sizes and the packing of the items (yarn work orders) with due date constraints are the original aspects of this study. Multi‐objective mixed integer programming model is developed to minimize two objectives. For the solution, the preemptive method called the lexicographic approach, in which the objectives are solved in order, is preferred. As the first objective, the overcapacity usage is minimized and then the second objective, which is the boiler usage cost, is minimized. Given that the VSBPP is strongly NP‐hard, an iterated greedy algorithm with two different decoding approaches is proposed. Computational experiments were conducted on 20 randomly generated benchmark instances and a real‐world industrial dataset. The numerical results show that good solutions can be obtained in seconds using the proposed approaches.

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TEFRED: A Temperature and Energy Cognizant Fault-Tolerant Real-Time Scheduler Based on Deadline Partitioning for Heterogeneous Platforms

Sharma

Das

Moulik

2022

Lecture Notes in Computer Science

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Thermal-constrained energy efficient real-time scheduling on multi-core platforms

Cited by 7 publications

References 18 publications

Maximizing Utilization and Minimizing Migration in Thermal-Aware Energy-Efficient Real-Time Multiprocessor Scheduling

Maximizing Utilization and Minimizing Migration in Thermal-Aware Energy-Efficient Real-Time Multiprocessor Scheduling

An iterated greedy algorithm for the planning of yarn‐dyeing boilers

TEFRED: A Temperature and Energy Cognizant Fault-Tolerant Real-Time Scheduler Based on Deadline Partitioning for Heterogeneous Platforms

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