Cross-Layer Scheduling in Cloud Systems

Abstract

Today, cloud computing engines such as stream-processing Storm and batch-processing Hadoop are being increasingly run atop software-defined networks (SDNs). In such cloud stacks, the scheduler of the application engine (which allocates tasks to servers) remains decoupled from the SDN scheduler (which allocates network routes). We propose a new approach that performs cross-layer scheduling between the application layer and the networking layer. This coordinated scheduling orchestrates the placement of application tasks (e.g., Hadoop maps and reduces, or Storm bolts) in tandem with the selection of network routes that arise from these tasks. We present results from both cluster deployment and simulation, and using two representative network topologies: Fat-tree and Jellyfish. Our results show that cross-layer scheduling can improve throughput of Hadoop and Storm by between 26% to 34% in a 30-host cluster, and it scales well.