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.
翻译:在本文中,我们从提供工作执行服务的SaaS(软软件即服务)供应商的角度,从提供工作执行服务的SaaS(软件即服务)供应商的角度,研究在云环境中与可变资源平行工作的市场导向在线双目标服务时间安排问题。在云中安排SaaS服务的主要过程是:SaaS供应商从IaS供应商购买云样片,以便安排最终用户的工作,并相应地向用户收取费用。这个问题有几个特殊特点,如面向工作的终端用户、有软期限限制的可喜平行工作、在线设置和资源的可变数。为了最大限度地增加收入和用户满意度,我们为SaaS供应商设计了在线算法,以便最佳地购买IaS实例并安排同步工作。提议的算法可以在多式运行时实现竞争性目标。基于真实世界谷歌工作痕迹以及合成数据集的理论分析和模拟证实了我们的算法的有效性和效率。