Explicit multipath congestion control for data center networks

Data center networks (DCNs) are at the forefront of a radical transformation, fueled by the rapid rise of artificial intelligence (AI), cloud-native adoption, and enterprise digitalization, which together place unprecedented demands on their design and performance. From healthcare AI and intelligent transportation to business analytics, modern DCNs are expected to support a diverse and evolving mix of bandwidth-demanding applications, each with distinct quality of service (QoS) requirements. Yet, most state-of-the-art DCN solutions rely on oversimplified DCN data traffic models, failing to address the required application-level differentiation in terms of end-to-end (E2E) latency demands. In response, we propose ALAS-DCN, a novel, to our knowledge, application-aware, low-latency, scalable DCN architecture, based on optical packet switching in the intra-rack and inter-rack domains that is both designed and rigorously evaluated using commercial QoS specifications and realistic traffic patterns of different applications coexisting within the same DCN. ALAS-DCN integrates three hierarchical optical sub-networks: intra-rack, inter-rack, and bridge, each governed by a custom wavelength division multiplexing (WDM) medium access control (MAC) protocol that dynamically prioritizes critical traffic packets, according to the specific application E2E latency demands. Unlike prior approaches, our evaluation leverages realistic workload traces generated by established traffic generators, capturing the application diversity within modern data center (DC) environments. Extensive experimental simulations across five representative DC categories, each hosting a different mix of modern applications, demonstrate that ALAS-DCN consistently achieves up to 95% throughput efficiency and sub-µs latency for high priority traffic across a variety of applications, consistently meeting their differentiated latency constraints. Scalable up to 20,000 servers, ALAS-DCN redefines the DCN as not just a transport layer but an intelligent enabler of next-generation application performance.

A survey on architectures and energy efficiency in Data Center Networks

Many applications in public clouds require a high fan-in, many-to-one type of data communication (known as TCP incast) in modern Data Center Networks (DCNs). Such communication could cause severe incast congestion in switches and result in TCP throughput collapse, substantially degrading the application performance. The root cause of throughput collapse is the Retransmission Timeouts (RTO) due to packet losses in congested switches. Tenants in public clouds can opt to use a variety of TCP versions. However, the existing solutions rely on modifications of TCP protocols and specific techniques from switches, and thus these existing solutions are not always feasible for public clouds. In this paper, we are inspired by the emerging virtualization and network softwarization technologies to develop a novel scheme called Retransmission timeout Avoidance by Packet Intelligent Discarding (RAPID) using software switches. RAPID considers the number of packets of each incast flow, buffered in the switch to selectively discard some packets, and ensures that the Fast Retransmission/Fast Recovery rather than RTO is invoked at the sender(s) in response to packet loss. Thus, the long idle period of a timeout and the throughput drop are avoided. We prove that, given a predetermined minimum switch buffer space, dedicated to the incast application, RAPID can prevent RTO in all the incast senders. We also present a low-complexity heuristic version of RAPID named RAPID-ED, which combines the principles of RAPID and early detection and is extremely easy to implement on today's software switches. We evaluate the two proposed schemes in a data center network testbed built on NS-3 simulator. The simulation results confirm the theoretical expectation, and show that the RAPID and RAPID-ED perform very well to prevent RTO of TCP incast flows and hence the throughput collapse. Compared with other incast solutions, RAPID and RAPID-ED do not modify TCP protocols and therefore are more suitable in public clouds.

In data center networks (DCNs), the flow size is a crucial factor to schedule the traffic, as small flows are usually latency-sensitive (e.g., web search from users) while large flows are not (e.g., backup between two sites). Some existing flow scheduling schemes try to schedule flows based on the prior knowledge of flows, such as flow size and deadline. However, it may be unpractical to obtain the prior information in some scenarios. In this paper, we propose START to minimize the flow completion time (FCT) and guarantee the priority of small flows in DCNs. We control the priorities of packets by leveraging the multiple priority queues based on the amount of data a flow has sent. Switches schedule flows according to their priorities. In addition, to schedule the flows more intelligently, we adjust the threshold of each priority dynamically according to the network loads. A prototype of START has been implemented with ns2 simulations. START is compatible with legacy TCP/IP stacks and deployable with existing commodity switches. The results show that START significantly outperforms existing flow scheduling schemes DCTCP and L2DCT up to 40% and 45% for small flows in changing network status. In some scenarios, START can even achieve better performance than pFabric.

In modern data center networks (DCNs), failures of network devices always occur and it is difficult to localize these failures. Our key observation is that latency data can reflect and profile network status. We can use this information to resolve issues like network failure localization.In this paper, we present NetCruiser, a system that is able to localize failures by learning from latency data. It can both measure and collect latency data to monitor the status of the whole network and pinpoint which switch or router encounters a failure. And we design a data structure to handle these latency data. With the construction of this data structure, we build a machine learning model to infer where issue occurs. Therefore, by the usage of this system, it answers the question about which switch encounters a failure in network. Our experimental evaluation has validated both the efficiency and effectiveness of our approach. Our system can be widely applied to both inter-DC network and intra-DC network.

To meet the high performance requirement of real time and critical applications in industrial Internet of Things, modern lossless ethernet data center networks (DCNs) deployed with remote direct memory access and priority-based flow control (PFC) are dedicated to delivering low latency and high bandwidth. However, existing load balancing schemes either lack sub-round-trip-time congestion sensing or fail to accurately detect and reroute flows that cause congestion in PFC-enabled lossless DCNs. Therefore, we propose LBoDSN, an in-network load balancing for lossless DCNs using direct switch notification (DSN) for fast congestion flow detection, to address above challenges. LBoDSN tracks the evolution of ingress queue lengths at destination switches to anticipate the initiation of PFC pause, precisely identifies congested flows before PFC pause, and then sends DSNs to source switches to perform rerouting. After rerouting, the congestion notification packet associated with the previous path is selectively discarded to improve transmission performance. Experiments under realistic workloads reveal that, LBoDSN outperforms CONGA by 13%–65%, and 25%–80% in average and tail Flow Completion Times (FCTs), respectively. Compared to ConWeave, LBoDSN achieves approximately 9% improvement in both average and tail FCTs, while reducing switch queue consumption for reordering.

The Spanning Tree Protocol (STP), standardized in IEEE 802.1D, has served as the foundational loop-prevention mechanism in bridged Ethernet networks for over three decades. Despite its widespread adoption, STP exhibits significant limitations with respect to convergence time, bandwidth utilization, and scalability that are increasingly incompatible with the demands of modern enterprise and data center networks. This paper presents a structured comparative analysis of five major Layer 2 protocols in the STP lineage: STP (IEEE 802.1D), Rapid Spanning Tree Protocol (RSTP, IEEE 802.1w), Multiple Spanning Tree Protocol (MSTP, IEEE 802.1s), Shortest Path Bridging (SPB, IEEE 802.1aq), and Transparent Interconnection of Lots of Links (TRILL, RFC 6325). The analysis is conducted along four primary dimensions: convergence time, scalability, operational complexity, and security considerations. Drawing exclusively on peer-reviewed literature, IETF/IEEE standards, and published operational benchmarks, the paper concludes with a structured evaluation of the relevance of these protocols in Software-Defined Networking (SDN) environments and modern Ethernet fabric architectures, proposing a conceptual decision framework for protocol selection.

Middleboxes have become an integral part of modern enterprise and data center networks. They are used for realizing various performance and security objectives. Most middlboxes (e.g., firewalls, Intrusion Detection Systems (IDSs), Network Address Translators (NATs), etc.) are dedicated hardware appliances. However, recent advancements in cloud and virtualization technologies have fueled the concept of Virtual Middleboxes or Virtual Network Functions (VNFs) along with a new research field known as Network Function Virtualization (NFV). This area of research has gained a lot of traction from both industry and academia. Although much progress has been made in NFV technology, a crucial component for realizing the primary objective of NFV is still missing – a management and orchestration [7] system that conforms to the principles of NFV: open source, open API and standardized software solutions. Without this feature, network operators may end-up with the same situation of vendor lock-in as with proprietary hardware middleboxes. A number of recent proposals like Stratos [8], OpenNF [9], and Split/Merge [10], strive to fulfill the requirements for VNF management and orchestration. However, they propose incompatible northbound APIs. What is really needed is a standardized API that is flexible enough to express a wide range of NFV management and orchestration operations. History shows that standardization efforts usually take a long time and often are futile. Hence, we take a different approach, and propose to use an existing, well known, standardized interface for NFV management and orchestration: the Linux file system interface.

An energy-efficient virtual machine placement and route scheduling scheme in data center networks

Modern datacenter networks still operate through fragmented workflows in which predictive maintenance, intrusion detection, root cause analysis (RCA), and remediation are studied separately and deployed through loosely coupled tooling. This paper presents a unified AI system for autonomous self-healing datacenter networks that connects four stages: temporal failure prediction, drift-adaptive intrusion detection, topology-aware RCA, and safety-gated recovery with counterfactual validation. The architecture combines streaming telemetry, network-flow analytics, graph reasoning, and a topology digital twin inside a single operational loop. The system is formalized as a constrained sequential decision problem over telemetry, flows, topology, and policy constraints, and is evaluated through staged module validation plus a trace-driven closed-loop emulation. Because no public benchmark spans all four stages jointly, the empirical evidence combines public telemetry and flow datasets, streaming emulation, packet-capture replay, topology-grounded recovery traces, and a synthetic end-to-end incident timeline that makes the module handoff contract explicit. Across failure-prediction benchmarks, the temporal sequence model reaches F1 scores of 0.3737 on optical zero-shot hard-failure evaluation and 0.4677 on Cisco BGP failure prediction within a 60-second warning window. In intrusion detection, the drift-adaptive hybrid improves weighted F1 from 61.35% to 68.69% on full CICIDS2017 cross-dataset transfer without retraining the base detectors and reaches 98.05% weighted F1 in a packet-capture replay case study. For RCA, topology-aware reasoning reaches 0.8380 target-localization F1 with 1.0000 hidden-target accuracy and 0.9394 temporal RCA accuracy at 5.2 s mean detection delay. In the recovery twin, gated actions improve mean reachability from 0.9740 to 1.0000, achieve 0.8182 recovery success, and block 100% of mismatched unsafe actions. The results show that prediction, detection, diagnosis, and remediation can be organized into a reproducible closed loop for next-generation self-healing datacenter networks.

Modern datacenter networks are facing various challenges, e.g., highly dynamic workloads, congestion, topology asymmetry. ECMP, as a traditional load balancing mechanism which is widely used in today’s datacenters, can balance load poorly and lead to congestion. Variety of load balancing schemes are proposed to address the problems of ECMP. However, these traditional schemes usually make load balancing decision only based on network knowledge for a snapshot or a short time past. In this paper, we propose a Reinforcement Learning (RL) based approach, called RILNET (ReInforcement Learning NETworking), aiming at load balancing for datacenter networks. RILNET employs RL to learn a network and control it based on the learned experience. To achieve a higher granularity of control, RILNET is constructed to route flowlet rather than flows. Moreover, RILNET makes routing decisions for aggregation flows (an aggregation flow is a flow set that includes all flows flowing from the same source edge switch to the same destination edge switch) instead of a single flow. In order to test performance of RILNET, we propose a flow-level simulation and a packet-level simulation, and the both results show that RILNET can balance traffic load much more effectively than ECMP and another load balancing solution, i.e., DRILL. Compared with DRILL, RILNET outperforms DRILL in data loss and maximal link delay. Specifically, the maximal link data loss and the maximal link delay of RILNET are 44.4% and 25.4% smaller than DRILL, respectively.

Reducing traffic burstiness for MPTCP in data center networks

Software-defined networks (SDNs) have the capabilities of controlling the efficient movement of data flows through a network to fulfill sufficient flow management and effective usage of network resources. Currently, most data center networks (DCNs) suffer from the exploitation of network resources by large packets (elephant flow) that enter the network at any time, which affects a particular flow (mice flow). Therefore, it is crucial to find a solution for identifying and finding an appropriate routing path in order to improve the network management system. This work proposes a SDN application to find the best path based on the type of flow using network performance metrics. These metrics are used to characterize and identify flows as elephant and mice by utilizing unsupervised machine learning (ML) and the thresholding method. A developed routing algorithm was proposed to select the path based on the type of flow. A validation test was performed by testing the proposed framework using different topologies of the DCN and comparing the performance of a SDN-Ryu controller with that of the proposed framework based on three factors: throughput, bandwidth, and data transfer rate. The results show that 70% of the time, the proposed framework has higher performance for different types of flows.

With the advent of software-defined networking (SDN) and OpenFlow switch protocol, routing and scheduling in modern data center networks is increasingly performed at the level of flows. A flow is a particular set of application traffic between two endpoints that receive the same forwarding decisions. As a consequence of the shift towards centralized flow-based control, efficient algorithms for scheduling and routing of flows have gained prominent importance. On the other hand, communication in datacenter applications, such as MAPREDUCE, and SPARK, often consists of multiple parallel data flows. Traditional methods to optimize flow metrics do not perform well since the flow abstraction is agnostic to application-level requirements, ignoring the fact that subsets of flows usually share the same performance goal. Chowdhury and Stoica introduced the concept ofcoflow as a networking abstraction that can capture the application-level semantics. In this dissertation, we design and analyze approximation algorithms for flow and coflow scheduling over various network models. In particular, we present (1) approximation algorithms for the problem of scheduling coflows over a general network topology with the goal of minimizing weighted average completion time; (2) improved algorithms for scheduling optical flows and coflows on a path; (3) a bi-criteria approximation algorithm for minimizing average response time of unit-size flows over a switch; (4) experimental work conducted to evaluate the practical performance of our algorithms and heuristics using our flow-level simulators.

Chapter 16 - Hypervisors, Virtualization, and Networking