Ostro: Scalable Placement Optimization of Complex Application Topologies in Large-Scale Data Centers

Abstract

A complex cloud application consists of virtual machines (VMs) running software such as web servers and load balancers, storage in the form of disk volumes, and network connections that enable communication between VMs and between VMs and disk volumes. The application is also associated with various requirements, including not only quantities such as the sizes of the VMs and disk volumes, but also quality of service (QoS) attributes such as throughput, latency, and reliability. This paper presents Ostro, an Open Stack-based scheduler that optimizes the utilization of data center resources, while satisfying the requirements of the cloud applications. The novelty of the approach realized by Ostro is that it makes holistic placement decisions, in which all the requirements of an application -- described using an application topology abstraction -- are considered jointly. Specific placement algorithms for application topologies are described including an estimate-based greedy algorithm and a time-bounded A algorithm. These algorithms can deal with complex topologies that have heterogeneous resource requirements, while still being scalable enough to handle the placement of hundreds of VMs and volumes across several thousands of host servers. The approach is evaluated using both extensive simulations and realistic experiments. These results show that Ostro significantly improves resource utilization when compared with naive approaches.