In this paper, we study the market-oriented online bi-objective service scheduling problem for pleasingly parallel jobs with variable resources in cloud environments, from the perspective of SaaS (Software-as-as-Service) providers who provide job-execution services. The main process of scheduling SaaS services in clouds is: a SaaS provider purchases cloud instances from IaaS providers to schedule end users’ jobs and charges users accordingly. This problem has several particular features, such as the job-oriented end users, the pleasingly parallel jobs with soft deadline constraints, the online settings, and the variable numbers of resources. For maximizing both the revenue and the user satisfaction rate, we design an online algorithm for SaaS providers to optimally purchase IaaS instances and schedule pleasingly parallel jobs. The proposed algorithm can achieve competitive objectives in polynomial run-time. The theoretical analysis and simulations based on real-world Google job traces as well as synthetic datasets validate the effectiveness and efficiency of our algorithm.

在本文中，我们从提供作业执行服务的SaaS（软件即服务）提供者的角度研究了在云环境中具有可变资源的并行并行作业的面向市场的在线双目标服务调度问题。在云中调度SaaS服务的主要过程是：SaaS提供商从IaaS提供商处购买云实例，以调度最终用户的工作并据此向用户收费。该问题具有几个特殊功能，例如面向工作的最终用户，具有有限的期限约束的令人愉悦的并行作业，在线设置以及可变数量的资源。为了最大程度地提高收入和用户满意度，我们设计了一种在线算法，供SaaS提供商优化购买IaaS实例并安排令人愉悦的并行作业。所提出的算法可以在多项式运行时达到竞争目标。基于现实世界中的Google作业跟踪以及综合数据集的理论分析和仿真验证了我们算法的有效性和效率。