Extension of Divisible-Load Theory from Scheduling Fine-Grained to Coarse-Grained Divisible Workloads on Networked Computing Systems

Xiaoli Wang (), Bharadwaj Veeravalli, Kangjian Wu and Xiaobo Song
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Xiaoli Wang: School of Computer Science and Technology, Xidian University, Xi’an 710071, China
Bharadwaj Veeravalli: Department of Electrical and Computer Engineering, National University of Singapore, 4 Engineering Drive 3, Singapore 119077, Singapore
Kangjian Wu: School of Computer Science and Technology, Xidian University, Xi’an 710071, China
Xiaobo Song: The 20th Research Institute of China Electronics Technology Group Corporation, Xi’an 710068, China

Mathematics, 2023, vol. 11, issue 7, 1-12

Abstract: The big data explosion has sparked a strong demand for high-performance data processing. Meanwhile, the rapid development of networked computing systems, coupled with the growth of Divisible-Load Theory (DLT) as an innovative technology with competent scheduling strategies, provides a practical way of conducting parallel processing with big data. Existing studies in the area of DLT usually consider the scheduling problem with regard to fine-grained divisible workloads. However, numerous big data loads nowadays can only be abstracted as coarse-grained workloads, such as large-scale image classification, context-dependent emotional analysis and so on. In view of this, this paper extends DLT from fine-grained to coarse-grained divisible loads by establishing a new multi-installment scheduling model. With this model, a subtle heuristic algorithm was proposed to find a feasible load partitioning scheme that minimizes the makespan of the entire workload. Simulation results show that the proposed algorithm is superior to the up-to-date multi-installment scheduling strategy in terms of achieving a shorter makespan of workloads when dealing with coarse-grained divisible loads.

Keywords: divisible load; coarse-grained workload; multi-installment scheduling; networked computing; 68W15 (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
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