Abstract
In heterogeneous multi-core architecture, to improve the running efficiency of the program is of critical importance. However, it depends on whether the subtasks of the program can be efficiently scheduled to the heterogeneous processing units, which is unfortunately a NP-complete problem. List scheduling algorithms are currently considered to be promising, which can approach suboptimal solutions in terms of low time consumption, but the overall quality of their solutions is often lower than those of ant colony algorithms. On the contrary, ant colony algorithms can obtain better solutions, but the convergence speed of the algorithms is worse. Especially when the number of nodes increases, this shortcoming is remarkably unacceptable. To tackle above observations, we propose a new ant colony optimization method for DAG task scheduling in heterogeneous multi-core environments, by combining culture algorithm and ant colony system (ACS) algorithm (namely, CACS). The group space of CACS is redesigned based on the ACS. Under the DAG tasks model, the heuristic function is improved by using the increase value of the scheduling length as the visibility of the ant colony, in order to improve the ants to obtain the local optimal nodes more effectively. To avoid the algorithm falling into the local optimum and improving the convergence speed, CACS adopts the critical tasks optimization algorithm in the belief space to mutate the local optimum individuals introduced in the group space, probabilistically generate elite individuals, and guide the group space evolution through the dynamic pheromone enhancement mechanism. Experiments with sufficient amount and randomness demonstrate that the CACS algorithm outperforms list scheduling algorithms and ant colony algorithms in terms of scheduling length and convergence speed.
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Acknowledgement
The research was financially supported by National Natural Science The research was financially supported by National Natural Science Foundation of China (No. 61972366), the Provincial Key Research and Development Program of Hubei (No. 2020BAB105), the Foundation of Henan Key Laboratory of Network Cryptography Technology (No. LNCT2020-A01), the Foundation of Key Laboratory of Network Assessment Technology, Chinese Academy of Sciences (No. KFKT2019-003), and the Foundation of State Key Laboratory of Public Big Data (No.PBD2021-02, No. 2019BDKFJJ003, No. 2019BDKFJJ011).
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Liu, N., Ma, L., Ren, W., Wang, M. (2022). An Improved ACS Algorithm by CA for Task Scheduling in Heterogeneous Multiprocessing Environments. In: Cai, Z., Chen, Y., Zhang, J. (eds) Theoretical Computer Science. NCTCS 2022. Communications in Computer and Information Science, vol 1693. Springer, Singapore. https://doi.org/10.1007/978-981-19-8152-4_16
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DOI: https://doi.org/10.1007/978-981-19-8152-4_16
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