Existing load-balancing methods used in data center networks involve some shortcomings such as excessively large decision delays during reactions to microbursts and large overheads involved in active probing. Programmable data planes have provided new opportunities for local decision-making on switches to address these issues. We observe that queue behavior (i.e., queue occupancy, queuing trend, and dequeue time interval) in switches can reflect the current or future congestion degree on a network. Furthermore, following data-driven experiments, we found an accurate fitting function of congestion degree to queue behavior. Thus, we propose an in-network load-balancing scheme based on a programmable switch, called queue-behavior-aware localized load balancing (QALL). In QALL, each switch independently selects egress ports probabilistically according to fine-grained-measured local queue behavior. The key concept of QALL is to take account the evolutionary process of reaching the current queue state into its decision basis for load balancing. Experimental results under actual DCN workloads (including web search and data mining workloads) demonstrate the effectiveness of QALL. In terms of flow completion time, decision delay, network shock, load sharing accuracy, and packet reordering, QALL outperformed recent perpacket (DRILL), per-flowlet (LetFlow and CONGA), and per-flow (ECMP) load balancers, particularly under heavy load. For example, under asymmetrical topology with 90% load level, the flow completion time of QALL was lower than that of ECMP, LetFlow, CONGA, and DRILL by up to 54.7%, 46.5%, 38.9%, and 18.9%, respectively.

In datacenter networks, bandwidth-demanding elephant flows without deadline and delay-sensitive mice flows with strict deadline coexist. They compete with each other for limited network resources, and the effective scheduling of such mix-flows is extremely challenging. We propose a deep reinforcement learning with private link approach (DRL-PLink), which combines the software-defined network and deep reinforcement learning (DRL) to schedule mix-flows. DRL-PLink divides the link bandwidth and establishes some corresponding private-links for different types of flows to isolate them such that the competition among different types of flows can decrease accordingly. DRL is used to adaptively and intelligently allocate bandwidth resources for these private-links. Furthermore, to improve the scheduling policy, DRL-PLink introduces the novel clipped double Q-learning, exploration with noise, and prioritized experience replay technology for DDPG to address function approximation error, to induce lager and more randomness for exploration, as well as more effective and efficient experience replay in DRL respectively. The experiment results under actual datacenter network workloads (including Web search and data mining workload) indicate that DRL-PLink can effectively schedule mix-flows at a small system overhead. Compared with ECMP, pFabric, and Karuna, the average flow completion time of DRL-PLink decreased by 77.79%, 65.61%, and 23.34% respectively, when the deadline meet rate is increased by 16.27%, 0.02%, and 0.836% respectively. Additionally, DRL-PLink can also well achieve load balance between paths.

A variety of network states can better help network operators to manage the whole network. However, the existing network measurement schemes still exhibit some drawbacks, such as excessive bandwidth overhead caused by running packet-level measurement, lack of coverage of variety measurement granularities, and occupying several switch’s memory. This paper presents the FullSight based on the programmable data plane, which provides fine multiple granularities measurement. Based on the programmability of data plane, this paper proposes an intelligent measurement mechanism that can adaptively adjust the measurement frequency according to the network state to greatly reduce the bandwidth overhead of measuring while ensuring a certain measurement accuracy and acceptable processing overhead. Also, the Rotating Memory scheme is proposed to reduce occupying memory of switch when achieving a variety of fine-grained measurements. The simulation results demonstrate the effectiveness of FullSight in terms of the bandwidth overhead reduction, the memory overhead reduction, full coverage of a variety of fine-grained network states. Compared with Netsight, FullSight only suffers from 0. 1% bandwidth overhead which is two orders of magnitude lower than Netsight, and FullSight has taken up no more than 0. 001% memory overhead for different measurement tasks.

Fine-grained, real-time, and accurate monitoring data can better help detect equipment failure and perform traffic engineering. However, existing in-band network telemetry (INT) implementations still exhibit a few drawbacks such as lack of real-time monitoring, relatively high overheads due to per-packet operation, and limited monitoring range. This paper proposes an INT+PDP-based fine-grained real-time telemetry scheme by observing and recording on the programmable data plane (PDP), referred to as O&R. The key idea lies in designing some registers on data plane to observe the states of packets forwarded by it as well as adding a customized header on a normal data packet to record how it is forwarded on its routing path. Except for measuring some conventional performance parameters such as end-to-end delay, jitter, throughput, and packet loss rate, O&R designs a clock offset elimination algorithm to realize the time synchronization of two adjacent switches, based on which we can complete more fine-grained measurement such as queuing delay, processing delay, transmission delay, and propagation delay on any hop. O&R also can measure the queue state that includes real-time queue depth and how many flows share the queue. Extensive experimental results for the K=4 fat-tree data-center network demonstrate the effectiveness of O&R in terms of higher accuracy, better real-time performance, less overheads, and better fine-graining compared to existing schemes. The measurement accuracy of O&R is 46.3% higher than that of INT-like method. The measurement delay of O&R is ~1 ms, while INT-like method needs ~20 ms. The measurement overhead of O&R is only 2.19% of Pingmesh.

In software-defined datacenter networks, there are bandwidth-demanding elephant flows without deadline and delay-sensitive mice flows with strict deadline. They compete with each other for limited network resources, and how to effectively schedule such mix-flow is a huge challenge. We propose DRL-PLink (deep reinforcement learning with private link) that combines software-defined network and deep reinforcement learning (DRL) to schedule mix-flow. It divides the link bandwidth and establishes some corresponding private links for different types of flows respectively to isolate them. DRL is used to adaptively allocate bandwidth resources for these private links. Furthermore, DRL-PLink introduces Clipped Double Q-learning and parameter exploration NoisyNet technology to improve the scheduling policy for overestimated value estimates and action exploration problems in DRL. The simulation results show that DRL-PLink can effectively schedule mix-flow. Compared with ECMP and pFabric, the average flow completion time of DRL-PLink has decreased by 68.87% and 52.18% respectively. At the same time, it maintains a high deadline meet rate (>96.6%) close to pFabric and Karuna very much.