With the increase of diversity in application preferences and networks, existing congestion control algorithms (CCAs) do not accommodate this complicated reality. Previous classic CCAs are designed for a specific domain with fixed rules, failing to adapt to such diversities. Recently surged learning-based CCAs have great potential in adaptability and flexibility but are not practical due to unsatisfying performance on convergence, fairness, overhead, consistency and safety assurance. In this paper, we propose Libra, a unified congestion control framework, that can empower these properties by combining the wisdom of classic and reinforcement learning (RL)-based CCAs. Extensive evaluation of Libra’s Linux kernel implementations on both live Internet and emulated networks shows performance improvement under dynamic networks (e.g.,$1.2\times $throughput than Orca on average). At the same time, Libra can flexibly satisfy different application needs, reduce the running overhead by at most$0.88\times $and perform good fairness and convergence properties, well-fitting our theoretical analysis.

Multipath TCP (MPTCP) is a burgeoning transport protocol which enables the server to transmit the traffic across multiple network interfaces in parallel. Classic MPTCPs have good friendliness and practicality such as relatively low overhead, but are hard to achieve consistent high-throughput and adaptability, especially for the ability to flexibly balance the congestion among different subpaths. In contrast, learning-based MPTCPs can essentially achieve consistent high-throughput and adaptability, but have poor friendliness and practicality. In this paper, we proposed MPLibra, a combined multipath congestion control framework that can complement the advantages of classic MPTCPs and learning-based MPTCPs together. MPLibra periodically leverages both classic MPTCPs and learning-based MPTCPs to make decisions and select the better one based on real-time network feedbacks. Extensive simulations on NS3 show that MPLibra can achieve good performance and outperform state-of-the-art MPTCPs under different network conditions. MPLibra improves the throughput by 40.5% and reduces the file download time by 29.94% compared with LIA, achieves good friendliness and balances congestion timely. What’s more, on the basis of MPLibra, we propose MPLibra+ which adds a safety module and an optimized packet scheduler and is the upgrade version of MPLibra. MPLibra+ has better ability to cope with untrained network environment and achieve better performance on heterogeneous scenarios compared with MPLibra.

Data centers heavily rely on scale-out architectures like fat-tree, BCube and VL2 to accommodate a large number of commodity servers. Since the traditional electrical network is demand-oblivious and cannot perfectly respond to the bursty traffic generated by big data applications, a growing trend is to design demand-aware topologies via introducing the converters with adjustable optical links, instead of adding more wiring links. However, little is known today about how to fully exploit the potential of the flexibility from the converters: the joint optimization on adjusting the optical links inside the converters and the routing in the whole network remains algorithmically challenging. In this paper, we design a set of customized converters for Diamond, VL2 and BCube topologies and initiate the optimization study in hybrid switching data center networks. As a case study, we introduce demand-aware load balancing problem (DLBP), i.e., a joint optimization on the physical layer (how the optical links interconnect inside the converter) and the network layer (how to determine the route especially for elephant flows in the whole network). We prove that DLBP is not only NP-hard, but also$\rho $-inapproximation. Accordingly, we design two algorithms: the first one is an intuitive greedy algorithm and the second one uses reinforcement learning to improve upon the solution of the first one. Trace-driven evaluations show that our algorithms can reduce the traffic congestion by 12% on average.

With the increase of diversity in application needs and networks, existing congestion control algorithms (CCAs) do not accommodate this complicated reality. Previous classic CCAs are designed for a specific domain with fixed rules, failing to adapt to such diversities. Recently surged learning-based CCAs have great potential in adaptability and flexibility but are not practical due to unsatisfying performance on convergence, fairness, overhead and safety assurance. In this paper, we propose Libra, a unified congestion control framework, which empowers flexibility, adaptability, and practicality, by combining the wisdom of classic and reinforcement learning (RL)-based CCAs. Extensive evaluation of Libra's Linux kernel implementations on both live Internet and emulated networks shows performance improvement under dynamic networks (e.g., 1.2x throughput than Orca on average). At the same time, Libra can flexibly satisfy different application needs, reduce the running overhead by at most 0.92x and perform good fairness and convergence properties, well-fitting our theoretical analysis.

Multipath TCP (MPTCP) is a burgeoning transport protocol which enables the server to split the traffic across multiple network interfaces. Classic MPTCPs have good friendliness and practicality such as relatively low overhead, but are hard to achieve consistent high-throughput and adaptability, especially for the ability of flexibly balancing congestion among different paths. In contrast, learning-based MPTCPs can essentially achieve consistent high-throughput and adaptability, but have poor friendliness and practicality. In this paper, we proposed MPLibra, a combined multipath congestion control framework that can complement the advantages of classic MPTCPs and learning-based MPTCPs. Extensive simulations on NS3 show that MPLibra can achieve good performance and outperform state-of-the-art MPTCPs under different network conditions. MPLibra improves the throughput by 40.5% and reduces the file download time by 47.7% compared with LIA, achieves good friendliness and balances congestion timely.