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Job scheduling in cloud computing environments is a critical yet complexproblem. Cloud computing user job requirements are highly dynamic anduncertain, while cloud computing resources are heterogeneous and constrained.This paper studies the online resource allocation problem for elastic computingjobs with soft deadlines in cloud computing environments. The maincontributions include: 1) Integer linear programming modeling is used to designan auction time scheduling framework with three key modules - resourceallocation, evaluation, and operation, which can dynamically allocate resourcesin closed loops. 2) Methods such as time-based single resource utilizationevaluation and weighted average evaluation are proposed to evaluate resourceusage efficiency. 3) Soft acceptance protocols are introduced to achieveelastic online resource allocation. 4) The time complexity of the proposedalgorithms is analyzed and proven to be polynomial time, demonstratingefficiency. 5) Modular design makes the framework extensible. This paperprovides a structured cloud computing auction framework as a reference forbuilding practical cloud resource management systems. Future work may exploremore complex models of random arrival and multi-dimensional resourceconstraints, evaluate algorithm performance on real cloud workloads, andfurther enhance system robustness, efficiency and fairness.

A Direct Approach for Solving Cloud Computing Task Assignment with Soft Deadlines