Lihua Qian, Mingxuan Wang, Yang Liu, Hao Zhou. Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers). 2024.

Yu Bao, Hao Zhou, Shujian Huang, Dongqi Wang, Lihua Qian, Xinyu Dai, Jiajun Chen, Lei Li. Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2022.

Lihua Qian, Hao Zhou, Yu Bao, Mingxuan Wang, Lin Qiu, Weinan Zhang, Yong Yu, Lei Li. Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers). 2021.

Lihua Qian, Lin Qiu, Weinan Zhang, Xin Jiang, Yong Yu. Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). 2019.

Allocating service capacities in cloud computing is based on the assumption that they are unlimited and can be used at any time. However, available service capacities change with workload and cannot satisfy users' requests at any time from the cloud provider's perspective because cloud services can be shared by multiple tasks. Cloud service providers provide available time slots for new user's requests based on available capacities. In this paper, we consider workflow scheduling with deadline and time slot availability in cloud computing. An iterated heuristic framework is presented for the problem under study which mainly consists of initial solution construction, improvement, and perturbation. Three initial solution construction strategies, two greedy- and fair-based improvement strategies and a perturbation strategy are proposed. Different strategies in the three phases result in several heuristics. Experimental results show that different initial solution and improvement strategies have different effects on solution qualities.

In cloud computing, services are unlimited and available at any time from the perspective of users or tenants. However, the remaining service capability could not satisfy tenants' requirements at any time from the perspective of service providers because the services are shared by multiple tasks. In this paper, we consider the workflow scheduling with both deadline and time slots constraints in cloud computing. An iterated heuristic framework is investigated, which mainly consists of three phases: the initial solution generation, improvement, reconstruction. Two constructive initial solution strategies, two improving methods and an reconstruction algorithm are proposed for the three phases, respectively. These strategies combine four heuristics, which are evaluated on a lot of test-bed instances.