This paper presents the two generations of storage network stacks that reduced the average I/O latency of Alibaba Cloud's EBS service by 72% in the last five years: Luna, a user-space TCP stack that corresponds the latency of network to the speed of SSD; and Solar, a storage-oriented UDP stack that enables both storage and network hardware accelerations.

The number of requests to transfer large files is increasing rapidly in web server and remote-storage scenarios, and this increase requires a higher processing capacity from the network stack. However, to fully decouple from applications, many latest userspace network stacks, such as VPP (vector packet processing) and snap, adopt a shared-memory-based solution to communicate with upper applications. During this communication, the application or network stack needs to copy data to or from shared memory queues. In our verification experiment, these multiple copy operations incur more than 50% CPU consumption and severe performance degradation when the transferred file is larger than 32 KB. This paper adopts a hardware-accelerated solution and proposes HAVS which integrates Intel I/O Acceleration Technology into the VPP network stack to achieve high-performance memory copy offloading. An asynchronous copy architecture is introduced in HAVS to free up CPU resources. Moreover, an abstract memcpy accelerator layer is constructed in HAVS to ease the use of different types of hardware accelerators and sustain high availability with a fault-tolerance mechanism. The comprehensive evaluation shows that HAVS can provide an average 50%-60% throughput improvement over the original VPP stack when accelerating the nginx and SPDK iSCSI target application.

In this paper, we present RECANS, a low-latency NFV state management framework that supports elastic scaling of NF chains while meeting the increasingly stringent NFV performance requirements. Its design builds on the insight that an instance may be scaled to a local server or a remote server, and we thus adopt a hierarchical state-sharing approach that exploits shared memory and low-latency network (RDMA) features to realize state sharing in a server and across servers. Globally, all state data are divided into partitions spread across NF servers, and we reorganize the partitions dynamically through state migration implemented with carefully chosen RDMA primitives. Locally, each partition is maintained by a node store, which uses shared memory to share state with NFs in the same server and thus avoids remote accesses. Being aware of NF chaining, RECANS also adopts specially designed flow tables that support chain-wide batch operations. Our evaluation shows that RECANS has a submillisecond peak latency during scaling events, and outperforms traditional hash tables by 4.78 times for normal state access throughput in NF chains.