A DLT-Aware Performance Evaluation Framework for Virtual-Core Speedup Modeling

Zile Xiang () and Thomas G. Robertazzi ()
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Zile Xiang: Department of Electrical and Computer Engineering, Stony Brook University, Stony Brook, NY 11794, USA
Thomas G. Robertazzi: Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, NY 11794, USA

Future Internet, 2025, vol. 17, issue 11, 1-17

Abstract: Scheduling computing is a well-studied area focused on improving task execution by reducing processing time and increasing system efficiency. Divisible Load Theory (DLT) provides a structured analytical framework for distributing partitionable computational and communicational loads across processors, and its adaptability has allowed researchers to integrate it with other models and modern technologies. Building on this foundation, previous studies have shown that Amdahl-like laws can be effectively combined with DLT to produce more realistic performance models. This paper further develops analytical models that further extend such integration by incorporating Gustafson’s Law and Juurlink’s Law into DLT to capture broader scaling behaviors. It also extends the analysis to workload distribution in virtual multicore systems, providing a more structured basis for evaluating parallel performance. Methods include analytically computing speedup as a function of the number of cores and the parallelizable fraction under different scheduling strategies, with comparisons across workload conditions. Results show that combining DLT with speedup laws and virtual core design offers a deeper and more structured approach for analytical parallel system evaluation. While the analysis remains theoretical, the proposed framework establishes a mathematical foundation for future empirical validation, heterogeneous workload modeling, and sensitivity analysis.

Keywords: scheduling computing; distributed computing; speedup analysis; Amdahl’s law; Gustafson’s law; Juurlink’s law (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2025
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