ETL cluster scheduling technology plays a crucial role, which unveils the latent value of medical data and improve the medical digitization. However, in real-world scheduling scenarios, variations in the performance c...
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This article focuses on the green hybrid flow shop batch scheduling problem (EHFSP) considering energy consumption, establishes an optimization model with the goal of minimizing maximum completion time and total energ...
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It is well known that the logical verification for the operating system is very essential. Event-B, as a formal verification tool, is one of the most widely used methods for verifying programs at the operating system ...
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Time Sensitive Networks (TSN) is a set of revisions to the IEEE 802.1 standard enabling safety-critical and real-time transmission over Ethernet. It is crucial in TSN to use a reasonable scheduling algorithm to guaran...
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In an electric power system, the main goal of Hydrothermal Generation scheduling (HTGS) is to effectively manage hydrothermal unit power generation while abiding by system restrictions. The scheduling timeframe can ra...
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Cloud computing (CC) is a modern technology which deploys networks of servers, positioned in extensive remote areas, for conducting operations on a larger quantity of data. In CC, a workflow module is employed for rep...
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In order to address the issue of excessive resource consumption due to frequent context switching when running a large number of compute-intensive tasks in high-load scenarios on Linux. The paper proposes a scheduling...
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This study explores the application of artificial intelligence technology in the optimization and scheduling of photovoltaic storage and charging microgrid parks. By analyzing the characteristics and requirements of m...
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In this paper, a new energy scheduling system based on improved grey model algorithm is proposed, and its performance is evaluated comprehensively. Firstly, aiming at the uncertainty and volatility problems in new ene...
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We study the resilient scheduling of moldable parallel jobs on high-performance computing (HPC) platforms. Moldable jobs allow for choosing a processor allocation before execution, and their execution time obeys vario...
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We study the resilient scheduling of moldable parallel jobs on high-performance computing (HPC) platforms. Moldable jobs allow for choosing a processor allocation before execution, and their execution time obeys various speedup models. The objective is to minimize the overall completion time or the makespan, when jobs can fail due to silent errors and hence may need to be re-executed after each failure until successful completion. Our work generalizes the classical scheduling framework for failure-free jobs. To cope with silent errors, we introduce two resilient scheduling algorithms, Lpa-List and Batch-List, both of which use the List strategy to schedule the jobs. Without knowing a priori how many times each job will fail, Lpa-List relies on a local strategy to allocate processors to the jobs, while Batch-List schedules the jobs in batches and allows only a restricted number of failures per job in each batch. We prove approximation ratios for the two algorithms under several prominent speedup models (e.g., roofline, communication, Amdahl, power, monotonic, and a mix model). An extensive set of simulations is conducted to evaluate different variants of the two algorithms, and the results show that they consistently outperform some baseline heuristics. Overall, our best algorithm is within a factor of 1.6 of a lower bound on average over the entire set of experiments, and within a factor of 4.2 in the worst case.
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