POSTER: Improving Datacenter Efficiency Through Partitioning-Aware Scheduling

Abstract

Datacenter servers often colocate multiple applications to improve utilization and efficiency. However, colocated applications interfere in shared resources, e.g., the last-level cache (LLC) and DRAM bandwidth, causing performance inefficiencies. Prior work has proposed two disjoint approaches to address interference. First, techniques that partition shared resources like the LLC can provide isolation and trade performance among colocated applications within a single node. But partitioning techniques are limited by the fixed resource demands of the applications running on the node. Second, interference-aware schedulers try to find resource-compatible applications and schedule them across nodes to improve performance. But prior schedulers are hampered by the lack of partitioning hardware in conventional multicores, and are forced to take conservative colocation decisions, leaving significant performance on the table. We show that memory-system partitioning and scheduling are complementary, and performing them in a coordinated fashion yields significant benefits. We present Shepherd, a joint scheduler and resource partitioner that seeks to maximize cluster-wide throughput. Shepherd uses detailed application profiling data to partition the shared LLC and to estimate the impact of DRAM bandwidth contention among colocated applications. Shepherd's scheduler leverages this information to colocate applications with complementary resource requirements, improving resource utilization and cluster throughput. We evaluate Shepherd in simulation and on a real cluster with hardware support for cache partitioning. When managing mixes of server and scientific applications, Shepherd improves cluster throughput over an unpartitioned system by 38% on average.