Multi-tenancy is an essential feature in cloud computing and is a major component to achieve scalability and energy-efficient solution to gain high level of economic benefits. As the cloud, computing is gaining more audiences and high user base, the problem of scheduling the computational workflow for multi-tenant cloud scheduling is becoming a difficult task to achieve. In this study, we present a learning-based scheduler for catering heterogeneous software and hardware resources in the context of multi-tenant cloud computing. The experimentation has been carried out with the help of green cloud simulator and the results are compared with the state of the art techniques like minimum completion time, first come first serve and backfilling. The experimental results exhibit that the presented algorithm provides an effective means of utilizing cloud resources in addition with drastic reduction in cost of operation.

多租户是云计算的基本功能，并且是实现可伸缩性和高能效解决方案以获取高水平经济利益的主要组件。随着云计算的发展，越来越多的受众和较高的用户基础，为多租户云调度调度计算工作流的问题已成为难以实现的任务。在这项研究中，我们提出了一种基于学习的调度程序，用于在多租户云计算的情况下满足异构软件和硬件资源的需求。实验是在绿色云模拟器的帮助下进行的，并将结果与​​最短完成时间，先到先得和回填等最新技术进行了比较。