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Algorithms
Volume 19
Issue 7
10.3390/a19070539
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This is an early access version, the complete PDF, HTML, and XML versions will be available soon.
Article

A Novel Model for Online Scheduling of Approximation Jobs †

by
Qi Li
1,*,
Xiaolei Wang
1,
Shuo Wen
1,
Wei Du
1,
Li Mao
1 and
Lijun Cai
2
1
Department of Network Information Security, Guangdong Police College, Guangzhou 510440, China
2
Computer Science and Electronic Engineering, Hunan University, Changsha 410012, China
*
Author to whom correspondence should be addressed.
†
This paper is an extended version of our paper published in ISPA 2025 (Li, Q.; Wen, S.; Wang, X.; Du, W.; Cai, L.; Xu, H. Online Job Scheduling for Profit Maximization in a Heterogeneous Cluster. In 23rd IEEE International Symposium on Parallel and Distributed Processing with Applications; IEEE: New York, NY, USA, 2025) which is held in Shenyang of China
Algorithms 2026, 19(7), 539; https://doi.org/10.3390/a19070539
Submission received: 20 May 2026 / Revised: 22 June 2026 / Accepted: 23 June 2026 / Published: 2 July 2026
Versions Notes

Abstract

Approximation jobs are widely deployed on Amazon EC2, which compute partial task segments to obtain useful results. For such jobs, maximizing total profit is the primary goal, where profit equals the sum of job utilities minus the total machine costs. Unfortunately, maximizing the total profit of approximation jobs is an NP-hard problem. This problem is further complicated by online job arrivals and heterogeneous resource demands across different tasks. This work builds an optimization framework that clearly characterizes job utility and machine costs to resolve this problem. Within this framework, we propose an efficient dual algorithm for job scheduling. The proposed method leverages the dual-fitting approach to measure algorithm performance by analyzing the primal and dual objective growth at each step. This work proves that our algorithm achieves a constant competitive ratio. The results from the trace-driven simulations demonstrate that our algorithms consistently outperform these baselines across various metrics.
Keywords: online job assignments; maximizing profit; heterogeneous resources; dual- fitting; competitive performance online job assignments; maximizing profit; heterogeneous resources; dual- fitting; competitive performance

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MDPI and ACS Style

Li, Q.; Wang, X.; Wen, S.; Du, W.; Mao, L.; Cai, L. A Novel Model for Online Scheduling of Approximation Jobs. Algorithms 2026, 19, 539. https://doi.org/10.3390/a19070539

AMA Style

Li Q, Wang X, Wen S, Du W, Mao L, Cai L. A Novel Model for Online Scheduling of Approximation Jobs. Algorithms. 2026; 19(7):539. https://doi.org/10.3390/a19070539

Chicago/Turabian Style

Li, Qi, Xiaolei Wang, Shuo Wen, Wei Du, Li Mao, and Lijun Cai. 2026. "A Novel Model for Online Scheduling of Approximation Jobs" Algorithms 19, no. 7: 539. https://doi.org/10.3390/a19070539

APA Style

Li, Q., Wang, X., Wen, S., Du, W., Mao, L., & Cai, L. (2026). A Novel Model for Online Scheduling of Approximation Jobs. Algorithms, 19(7), 539. https://doi.org/10.3390/a19070539

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