Workload Adaptive Shared Memory Management for High Performance Network I/O in Virtualized Cloud

Abstract

This paper presents the design and implementation of MemPipe, a dynamic shared memory management system for high performance network I/O among virtual machines (VMs) located on the same host. MemPipe delivers efficient inter-VM communication with three unique features. First, MemPipe employs an inter-VM shared memory pipe to enable high throughput data delivery for both TCP and UDP workloads among co-located VMs. Second, instead of static allocation of shared memories, MemPipe manages its shared memory pipes through a demand driven and proportional memory allocation mechanism, which can dynamically enlarge or shrink the shared memory pipes based on the demand of the workloads in each VM. Third but not the least, MemPipe employs a number of optimizations, such as time-window based streaming partitions and socket buffer redirection, to further optimize its performance. Extensive experiments show that MemPipe improves the throughput of conventional (native) inter VM communication by up to 45 times, reduces the latency by up to 62 percent, and achieves up to 91 percent shared memory utilization.