Rack Level Scheduling for Containerized Workloads

High performance SSDs have become ubiquitous in warehouse scale computing. Increased adoptions can be attributed to their high bandwidth, low latency and excellent random I/O performance. Owing to this high performance, multiple I/O intensive services can now be co-located on the same server. SSDs also introduce periodic latency spikes due to garbage collection. This, combined with multi-tenancy increases latency unpredictability since co-located applications now compete for CPU, memory, and disk bandwidth. The combination of these latency spikes and unpredictability lead to long tail latencies that can significantly decrease the system performance at scale. In this paper, we present a rack-level scheduling algorithm, which dynamically detects and shifts workloads with long tail latencies within servers in the same rack. Different from the global resource management methods, rack-level scheduling utilizes lightweight containers to minimize data movement and message passing overheads, leading to a much more efficient solution to reduce tail latency.With the algorithms implemented in the storage driver of the containerization infrastructure, it becomes viable to deploy and migrate applications in existing server racks without extensive modifications to storage, OS and other subsystems.