Flowlet-level load balancing has not demonstrated the expected robust capability in RDMA networks due to insufficient flowlets and the adverse effects of PFC. To delve deeper, we conduct measurements at end hosts and perform a detailed analysis of time gaps between packets. Our investigation reveals that in RDMA networks, the number of time gaps exceeding the flowlet timeout is considerably lower than the number in TCP networks. We also identify a stepwise time gap pattern that predicts the occurrence of PFC. Based on these observations, we propose$\text{HF}^{2} \mathrm{T}$, a host-based time gap adjustment method to improve the effectiveness of flowlet-level load balancing in RDMA networks. The core idea involves delaying a minimal number of specific packets at the host, actively extending the time gaps between them, thereby fostering the generation of sufficient flowlets at the switch and enhancing the utilization of equal-cost links. Incorporating an identification algorithm for the time gap pattern that predicts PFC,$\text{HF}^{2} \mathrm{T}$also leverages the time gap extension to reroute traffic away from potential PFC paths in advance, thus mitigating PFC occurrences. The minor cost of delaying a few packets is vastly offset by the benefits of generating flowlets and reducing PFC. We use DPDK to implement a prototype of$\text{HF}^{2} \mathrm{T}$, and through testbed experiments, we demonstrate that$\text{HF}^{2} \mathrm{T}$, serving as a building block for flowlet load balancing, can enhance the throughput of CONGA by 16.82%. The simulation results also show that$\text{HF}^{2} \mathrm{T}$can reduce the average FCT by 16.38% and the 99-percentile FCT by 21.13% compared to the state-of-the-art RDMA load balancing ConWeave.

Modern data centers (DCs) employ various traffic load balancers to achieve high bisection bandwidth. Among them, flowlet switching has shown remarkable performance in both load balancing and upper-layer protocol (e.g., TCP) friendliness. However, flowlet-based load balancers suffer from the inflexibility of flowlet timeout value (FTV) and result in sub-optimal performance under various application workloads. To this end, we propose Halflife, a novel flowlet-based load balancer that leverages fading FTVs to reroute traffic promptly under different workloads without any prior knowledge. Halflife not only balances traffic better, but also avoids the performance degradation caused by frequent oscillation or shifting of lows between paths. Furthermore, Halflife's fading mechanism is not only compatible with most flowlet-based load balancers, such as CONGA and LetFlow, but also improves their performance when leveraging flowlet switching in RDMA network. Through testbed experiments and simulations, we prove that Halflife improves the performance of CONGA and LetFlow by 10% ~ 150%, and it outperforms other load balancers by 30% ~ 200% across most application workloads.

In-Network Computing (INC) has found many applications for performance boosts or cost reduction. However, given heterogeneous devices, diverse applications, and multi-path network typologies, it is cumbersome and error-prone for application developers to effectively utilize the available network resources and gain predictable benefits without impeding normal network functions. Previous work is oriented to network operators more than application developers. We develop ClickINC to streamline the INC programming and deployment using a unified and automated workflow. Click-INC provides INC developers a modular programming abstractions, without concerning to the states of the devices and the network topology. We describe the ClickINC framework, model, language, workflow, and corresponding algorithms. Experiments on both an emulator and a prototype system demonstrate its feasibility and benefits.

Time-Sensitive Networking (TSN) is proposed in recent years to satisfy the strict performance requirements of time-sensitive traffic in a growing number of emerging applications. Even though several traffic scheduling algorithms have been standardized for TSN to pursue this goal, time-sensitive flows may not be forwarded as planned and thus fail to achieve the expected performance in real networks. The fundamental cause lies in the fact that static offline planning cannot adapt to the intrinsic dynamic factors in TSN (e.g., time-synchronization error) at runtime. Hence, next-generation TSN will benefit from a closed-loop design where a performance monitoring system provides feedback of real-time packet-forwarding information. In our research, TSN-Peeper, a light-weight, fast-response and full-coverage TSN performance monitoring system, is designed and evaluated. This paper describes its architecture design and data collection mechanisms that enable timely identification and collection of packet-forwarding misbehavior at low-cost in TSN. TSN-Peeper offloads the misbehavior identification in the switch to relieve the burden on the controller and network bandwidth. To reduce the interruption frequency to the controller, it uses probe packets to collect misbehavior information in aggregation with optimized path planning. To realize controllable reporting delays, it optimizes the sending moments of probe packets according to the flow settings. Experimental results verify that TSN-Peeper offers fast response with low cost while providing full coverage and being scalable.

In-network caching is a basic feature of ICN architecture. Traditional ICN is distributed, which means the locations of content blocks cannot be adjusted precisely. Therefore, the cache allocation in traditional ICN is hard to approach optimization. With the aid of centralized controllers provided by SDN, ICN can manipulate the cache allocation with high flexibility. Heuristic algorithms have been applied to the cache allocation of ICN with centralized controllers but cannot guarantee the feasibility of solutions because of the feature of randomness. This paper proposes a caching strategy named MRPGA based on genetic algorithms. The mechanism of MRPGA guarantees the feasibility of solutions and accelerates convergence. Also, the simulations show that MRPGA figures out a better cache distribution in a shorter time than the genetic algorithm.