eHardware/software (hw/sw) combined systems are necessary to meet modern clouds’ requirements for processing huge amounts of network traffic by efficiently offloading large flows to hardware. However, existing hw/sw flow offloading systems typically perform traffic statistics collection and large flow selection within a time window—a time-window-based approach. Their offloading decision of large flows issynchronizedin the unit of a time window, which is mismatched to the asynchronous and dynamic nature of each flow’s sending rate. Additionally, the flow measurement and selection for large flows are decoupled in these solutions, leading to memory and CPU inefficiency. In this paper, we introduce TAO, a novel solution to the hw/sw combined flow offloading problem byasynchronouslyselecting and offloading flows based on flow table entries. TAO can reactfasterto the rapid dynamics of flows by taking actions at each table entry and ismore efficientby coupling flow measurement and selection into the entry. We have implemented a full-fledged TAO prototype based on the P4 switch and DPDK. Testbed results demonstrate that TAO can offload ∼16% more traffic to hardware, outperforming existing solutions by achieving 42× lower memory overhead. Meanwhile, it reduces software CPU utilization by 66.7% and cuts tail forwarding latency by 95.59% compared to state-of-the-art methods.

RDMA, as a cutting-edge networking technology, has gained extensive adoption in large-scale data centers due to its exceptional characteristics, such as low and stable latency, high throughput and low CPU utilization. However, due to the limited on-chip memory of the RDMA Network Interface Cards (RNIC), commercial RDMA usually only supports single-path transmission and cannot fully utilize the rich parallel paths within the DCN, resulting in insufficient bandwidth utilization. Multipath transmission can improve bandwidth utilization, but the out-of-order (OoO) packets it brings negatively impacts the performance of RNICs. Recent works have attempted to address this issue by using bitmaps to record OoO packets to better support multipath transmission. However, these approaches either consume excessive memory for maintaining bitmaps, leading to poor connection scalability, or introduce high latency in bitmap sharing. Consequently, implementing efficient packet reordering in RDMA remains a challenge.

Large-scale cloud applications today are often deployed using multiple containers, and a container overlay network is the de facto method to provide connectivity among these containers. However, the existing tunneling-based overlay network incurs significant performance overhead due to the need of transformation for every packet. Recent workSlim, through manipulating connection-level meta-data, allows containers to use host OS sockets directly thus they can achieve good performance without extra packet tunneling. Nevertheless, the connection setup is significantly slowed down, which requires an extra round-trip communication between both sides to pass the mapping information of the host OS socket and the container socket. This greatly hurts the performance of many cloud applications that must process short connections at high speed. We proposeSlimFast, a low-overhead container overlay network which provides a fast connection setup.SlimFastdirectly uses the host OS socket for container communication asSlim. However,SlimFastneeds no extra communication during connection setup. We reserve a dedicated host port for the container network and use socket mapping table to locally find the right container socket during connection setup. We implementSlimFastwhich is compatible with existing container applications. Experiments show that,SlimFastcan improve the connection setup time by about 2.1x compared withSlim, meanwhile maintaining low-overhead during data transmission asSlim. This brings significant performance improvement to real applications. Particularly, testbed results show thatSlimFastimproves the throughput of Nginx proxy and Memcached by about 0.9x and 2.2x, respectively.