Current NFVs assign packets to CPU cores at flow granularity, where each flow is pinned to a single CPU. This approach is efficient under most scenarios but has exposed limitations when handling elephant flows. These “heavy hitters” overwhelm single cores, creating bottlenecks that affect overall throughput and degrade service quality. As networks scale to higher-speed links and core-rich CPUs, these imbalances become more severe. In this paper, we propose ParaFlowO, an architecture that Parallelizes processing elephant Flows across multiple CPU cores while preserving in-Order delivery. ParaFlowO breaks elephant flows into flowlets and dynamically rotates them across multiple cores. It integrates a lightweight reordering mechanism to preserve packet order and controls parallelism to mitigate contention on shared state. Preliminary evaluations show that ParaFlowO offers a practical solution to mixed-grained parallelism in stateful middleboxes.

Load balancers (LBs) are crucial in cloud environments, ensuring workload scalability. They route packets destined for a service (identified by a virtual IP address, or VIP) to a group of servers designated to deliver that service, each with its direct IP address (DIP). Consequently, LBs significantly impact the performance of cloud services and the experience of tenants. Many academic studies focus on specific issues such as designing new load balancing algorithms and developing hardware load balancing devices to enhance the LB's performance, reliability, and scalability. However, we believe this approach is not ideal for cloud data centers for the following reasons: (i) the increasing demands of users and the variety of cloud service types turn the LB into a bottleneck; and (ii) continually adding machines or upgrading hardware devices can incur substantial costs. In this paper, we propose the Next Generation Load Balancer (NGLB), designed to bypass the TCP connection datapath from the LB, thereby eliminating latency overheads and scalability bottlenecks of traditional cloud LBs. The LB only participates in the TCP connection establishment phase. The three key features of our design are: (i) the introduction of anactive address learningmodel to redirect traffic and bypass the LB, (ii) amulti-tenant isolationmechanism for deployment within multi-tenant Virtual Private Cloud networks, and (iii) a distributed flow control method, known ashierarchical connection cleaner, designed to ensure the availability of backend resources. The evaluation results demonstrate that NGLB reduces latency by 16% and increases nearly 3× throughput. With the same LB resources, NGLB improves 10× rate of new connection establishment. More importantly, five years of operational experience has proven NGLB's stability for high-bandwidth services.