Job scheduling in cloud computing environments is a critical yet complex problem. Cloud computing user job requirements are highly dynamic and uncertain, while cloud computing resources are heterogeneous and constrained. This paper studies the online resource allocation problem for elastic computing jobs with soft deadlines in cloud computing environments. The main contributions include: 1) Integer linear programming modeling is used to design an auction time scheduling framework with three key modules - resource allocation, evaluation, and operation, which can dynamically allocate resources in closed loops. 2) Methods such as time-based single resource utilization evaluation and weighted average evaluation are proposed to evaluate resource usage efficiency. 3) Soft acceptance protocols are introduced to achieve elastic online resource allocation. 4) The time complexity of the proposed algorithms is analyzed and proven to be polynomial time, demonstrating efficiency. 5) Modular design makes the framework extensible. This paper provides a structured cloud computing auction framework as a reference for building practical cloud resource management systems. Future work may explore more complex models of random arrival and multi-dimensional resource constraints, evaluate algorithm performance on real cloud workloads, and further enhance system robustness, efficiency and fairness.

翻译：在云计算环境中，作业调度是一个关键且复杂的问题。云计算用户作业需求具有高度动态性和不确定性，而云计算资源具有异构性和约束性。本文研究云计算环境下弹性计算作业带软截止时间的在线资源分配问题。主要贡献包括：1）采用整数线性规划建模，设计包含资源分配、评估和操作三个关键模块的拍卖时间调度框架，能够以闭环方式动态分配资源；2）提出基于时间的单一资源利用率评估和加权平均评估等方法，用于评价资源使用效率；3）引入软接受协议以实现弹性在线资源分配；4）对所提算法进行时间复杂度分析，证明其为多项式时间复杂度，展现了高效的性能；5）模块化设计使框架具有可扩展性。本文提供了一个结构化的云计算拍卖框架，可作为构建实用云资源管理系统的参考。未来工作可探索随机到达和多维资源约束等更复杂模型，在真实云工作负载上评估算法性能，并进一步增强系统的鲁棒性、效率和公平性。