An Approach: Applicability of Existing Heterogeneous Multicore Real-Time Task Scheduling in Commercially Available Heterogeneous Multicore Systems

Baital, Kalyan; Chakrabarti, Amlan

doi:10.1007/978-981-32-9949-8_8

Cited by 2 publications

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“…The orientation of research on computing resource demand balance is towards the analysis and modeling of tasks characteristics. In terms of tasks characteristics, the feasibility of dynamically allocating processing units based on tasks characteristics is discussed in literature (Baital et al, 2020). The traditional firstcome, first-served queue scheduling is changed based on tasks time characteristics in literature (Huang et al, 2018).…”

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confidence: 99%

“…The coupling of tasks characteristics for cluster division is considered in literature (Abbasi et al, 2021), where an affinity-based tasks scheduling algorithm is proposed. However, the identification of tasks characteristics is lacking in literature (Huang et al, 2018;Baital et al, 2020;Abbasi et al, 2021), resulting in the balanced solution not reflecting the differentiated processing process. On the topic of tasks computing resource demand modeling, tasks sequence demand is modeled based on cyclicity and similarity in literature (Alam et al, 2018).…”

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confidence: 99%

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Dynamic balancing method of power distribution and consumption tasks based on state iterative prediction and resource peaking shifting

Cai,

Chen,

et al. 2024

Front. Energy Res.

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In the context of the new power system, the widespread access to massive distributed new energy sources has led to the power distribution and consumption tasks characterized by multiple time scales, wide random distribution, and large demand differences, resulting in unpredictable resource peaks in the tasks computing resource demand curve. In view of this situation, a method of forecasting and dynamic balancing of computing resource demand for power distribution and consumption tasks based on state iteration was proposed: firstly, the tasks computing resource demand model was established under the analysis of the attributes and parameter demand of the power distribution and consumption tasks scenario. Secondly, on the basis of the short-term effectiveness prediction of the traditional Markov model, the first-order difference of the state is used for data training to track the state fluctuation, and the historical state and the predicted state are used for state iteration, so as to avoid the convergence of long-term prediction. Finally, a dynamic balancing model is established according to the time-scale characteristics of cyclical and non-cyclical tasks, and the optimal configuration of load imbalance is achieved through the identification and adjustment of historical data and burst data. The simulation results show that the improved Markov model based on first-order difference and state iteration has the short-term accuracy of the traditional model and the long-term traceability of data fluctuations. The dynamic balancing model can combine the characteristics of historical data and burst data to effectively reduce the imbalance of resource demand, and show good ability to cope with resource imbalance deviation.

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