Popularity-based congestion control in named data networking

RaceCC: A rapidly converging explicit congestion control for datacenter networks

In this paper we explore congestion control solutions in a network running the Named Data Networking (NDN) protocol. Since requested data can be retrieved from either original producers or in-network caches, the end-to-end connection oriented TCP congestion control solutions, such as BBR (Bottleneck Bandwidth and Round-trip propagation time) do not work well for NDN transport, especially in wireless networks. We propose a BBR-guided congestion control, BBR-CD, for bulk data fetching by a group of users. BBR-CD applies RTT filtering and interest scheduling to improve BBR's efficiency in NDN. Our evaluation shows that BBR-CD achieves much higher application goodput compared with ndncatchunks which implements the TCP AIMD and CUBIC congestion control algorithms. Moreover, compared with one-flow-per-file SIRC, an NDN congestion control algorithm that paces interests based on inter-data-gap, BBR-CD provides better fairness and bandwidth utilization. Finally, BBR-CD outperforms a direct adoption of the original BBR to NDN, suggesting that our changes to BBR are effective.

Despite many years of improvements to it, TCP still suffers from an unsatisfactory performance. For services dominated by short flows (e.g., web search and e-commerce), TCP suffers from the flow startup problem and cannot fully utilize the available bandwidth in the modern Internet: TCP starts from a conservative and static initial window (IW, 2-4 or 10), while most of the web flows are too short to converge to the best sending rate before the session ends. For services dominated by long flows (e.g., video streaming and file downloading), the congestion control (CC) scheme manually and statically configured might not offer the best performance for the latest network conditions. To address these two challenges, we propose TCP-RL, which uses reinforcement learning (RL) techniques to dynamically configure IW and CC in order to improve the performance of TCP flow transmission. Basing on the latest network conditions observed at the server side of a web service, TCP-RL dynamically configures a suitable IW for short flows through group-based RL, and dynamically configures a suitable CC scheme for long flows through deep RL. Our extensive experiments show that for short flows, TCP-RL can reduce the average transmission time by about 23%; and for long flows, compared with the performance of 14 CC schemes, TCP-RL's performance ranks top 5 for about 85% of the 288 given static network conditions, whereas for about 90% of conditions, its performance drops by less than 12% compared with that of the best-performing CC schemes for the same network conditions.

In Named Data Networking (NDN), content can be retrieved from multiple sources through multiple path, which makes the traditional end-to-end congestion control mechanisms unsuitable for NDN. A lot of works have been done to achieve the congestion control by limiting the transmission rate of interest packets, but most of them overlooked the intelligent forwarding feature in NDN. In this paper, we propose a ratebased, multipath-aware congestion control algorithm MNRCP, which is inspired by Rate Control Protocol (RCP). MNRCP calculates the transmission rate for every flow and feedbacks it to the consumer to control the sending rate of interest packets. In order to fully exploit the features of multiple data sources, in-network caching and multipath forwarding and adapt to the practical scenarios that interest packets requesting the same data can arrive from different interfaces, we propose a novel multipath forwarding strategy and a multipath feedback mechanism to achieve fairness and network utilization. In addition, simulation results of our algorithm in ndnSIM2.3 show that compared with other congestion control mechanisms, MNRCP can achieve higher total throughput while ensuring fairness. According to the available bandwidth of the path, MNRCP also can achieve the optimal traffic split ratio, and thus performs better in load balancing.

Network congestion causes severe transmission performance loss. Congestion control is the key to achieve low latency, high bandwidth and high stability. Due to the high regularity of topologies, datacenter networks contain abundant resources of equal-cost paths inherently and have huge potential for high-bandwidth transmission. Taking in-network telemetry (INT) information as the congestion signal, congestion control algorithms can monitor the network based on precise link load data. Hence the INT-based algorithm is the focus of recent high-precision congestion control research. However, existing INT-based congestion control algorithms handle the single-path transmission between the sender and the receiver, which cannot make full use of the abundant equal-cost paths resources in the network. In this paper, we make the first attempt to propose a multi-path INT-based congestion control algorithm, called MPICC. Compared with the most advanced INT-based congestion control algorithm HPCC, our method realizes multi-path coordinated transmission of data packets in datacenter networks. We conduct extensive experiments under various workloads to evaluate the performance of MPICC. The experimental results show that under the condition of ensuring high throughput of end hosts, MPICC is able to use multi-path to transmit data packets, which greatly reduces the flow completion time (FCT) of the flow in the network. Specifically, under three common datacenter workloads, Cache Follower, Web Search and Web Server, MPICC reduces the average flow completion time of the network by 20.1%, 14.4%, and 39.5%, respectively, and shortens the 97th-percentile flow completion time by 39.9%, 18.9% and 57.9%, respectively.KeywordsCongestion controlIn-network telemetry (INT)Multi-pathDatacenter networksProgrammable switch

Information-Centric Networking (ICN) is a new communication paradigm that replaces the host addresses by the name of content; Named Data Networking (NDN) is a promising ICN architecture that has attracted research attention in recent years. NDN is a receiver-driven architecture implements pull-based communication in the form of one-interest-one-data. This model poses different challenges, especially from the transport layer perspective. In contact to IP-based networks where the congestion is handled in an end-to-end manner, NDN cannot apply the same concept, while most of the existing solutions are based on hop-by-hop connection. In this paper, we present a new congestion control mechanism for NDN based on link quality estimation. We focus our efforts to provide fast data transmission, decrease packet dropping rate, and maximize the link utilization. The simulation results show that our solution outperforms the NDN schemes in terms of throughput and drop packets.

In data center networks, various types of traffic with strict performance requirements operate simultaneously, necessitating effective isolation and scheduling through priority queues. However, most switches support only around ten priority queues. Virtual priority can address this limitation by emulating multi-priority queues on a single physical queue, but existing solutions often require complex switch-level scheduling and hardware changes. Our key insight is that virtual priority can be achieved by carefully managing bandwidth contention in a physical queue, which is traditionally handled by congestion control (CC) algorithms. Hence, the virtual priority mechanism needs to be tightly coupled with CC. In this paper, we propose PrioPlus, a CC enhancement algorithm that can be integrated with existing congestion control schemes to enable virtual priority transmission. PrioPlus assigns specific delay ranges to different priority levels, ensuring that flows transmit only when the delay is within the assigned range, effectively meeting virtual priority requirements. Compared to Swift CC with physical priority queues, PrioPlus provides strict priority for high-priority flows without impacting performance sensibly. Meanwhile, it benefits low-priority flows from 25% to 41% as its priority-aware design enhances CC's ability to fully utilize available bandwidth once higher-priority traffic completes. As a result, in coflow and model training scenarios, PrioPlus improves job completion times by 21% and 33%, respectively, compared to Swift with physical priority queues.

The Internet offers a large number of applications and services that we use on a daily basis. These widely used applications are hosted on large-scale, high-performance computing systems called data centers. The performance of TCP is inefficient in many-to-one communication, which is a common traffic pattern in data center networks. This many-to-one communication causes significant packet losses followed by timeouts, which consequently results in throughput collapse in data center networks; this problem is known as TCP Incast. In this paper, we present a queue length feedback-based solution to mitigate TCP Incast. The scheme has two parts: i) a novel queue length-based congestion parameter, which accurately measures congestion along the path from source to destination, and ii) a congestion control scheme that effectively uses the new congestion parameter to prevent throughput collapse due to Incast traffic patterns. Results are compared with TCP and DCTCP, the two most common transport protocols deployed in data center networks. The results show that the proposed scheme minimizes packet drops and achieves high utilization and burst tolerance.

Named data networking (NDN) as a promising future network architecture has been widely considered as a very appropriate network protocol for multihop wireless network (MWN). In named-data MWN, congestion control, forwarding strategy, and power control are three coupled critical issues. There are already extensive works about cross-layer design for MWN with IP and they have demonstrated the advantage of cross-layer design. Enlightened by cross-layer designs for MWN with IP, in this paper, the joint congestion control, forwarding strategy, and power control problem for named-data MWN are studied, and it is modeled as a named-data network utility maximization (NDNUM) problem. In our NDNUM, we maximize data receiving rate related network utility minus power consumption, while consider NDN’s communication characteristics that are different from IP. Then a distributed iterative algorithm based on subgradient method is proposed in order to solve NDNUM. To the best of our knowledge, our algorithm is the first cross-layer design for congestion control and power control for named-data MWN. We show that network utility maximization not only can be used to facilitate cross-layer design for networks with end-to-end protocols but also can be used to design cross-layer mechanisms for information-centric network. From simulation, our algorithm outperforms existing congestion control mechanism in network throughput, resource utilization efficiency, and stabilization PIT size.

Named data Network also called NDN, is a new paradigm for the future internet architecture that focuses on content-based rather than host-based communications. This paradigm has new features such as receiver-driven, one-Interest-one-Data, in-network caching, multi-source and multi-path, which posed new challenges for congestion control. As a result, the present research on congestion control in NDN paradigm is focused primarily on how to take advantage of these new features. The current document examines different methods and schemes that was proposed in hop-by-hop congestion control category. It provides an evaluation between them according to their congestion detection, as well as their advantages and disadvantages.

Ahstract-RDMA over Converged Ethernet (RoCE) has gained significant attraction for datacenter networks due to its compatibility with conventional Ethernet-based fabric. However, the RDMA protocol is efficient only on (nearly) lossless networks, emphasizing the vital role of congestion control on RoCE networks. Unfortunately, the native RoCE congestion control scheme, based on Priority Flow Control (PFC), suffers from many drawbacks such as unfairness, head-of-line-blocking, and deadlock. Therefore, in recent years many schemes have been proposed to provide additional congestion control for RoCE networks to minimize PFC drawbacks. However, these schemes are proposed for general datacenter environments. In contrast to the general datacenters that are built using commodity hardware and run general-purpose workloads, high-performance distributed training platforms deploy high-end accelerators and network components and exclusively run training workloads using collectives (All-Reduce, All-To-All) communication libraries for communication. Furthermore, these platforms usually have a private network, separating their communication traffic from the rest of the datacenter traffic. Scalable topology-aware collective algorithms are inherently designed to avoid incast patterns and balance traffic optimally. These distinct features necessitate revisiting previously proposed congestion control schemes for general-purpose datacenter environments. In this paper, we thoroughly analyze some of the state-of-the-art RoCE congestion control schemes (DCQCN, DCTCP, TIMELY, and HPCC) vs. PFC when running on distributed training platforms. Our results indicate that pre-viously proposed RoCE congestion control schemes have little impact on the end-to-end performance of training workloads, motivating the necessity of designing an optimized, yet low-overhead, congestion control scheme based on the characteristics of distributed training platforms and workloads.

Named data networking (NDN) supports receiver-driven content retrieval with stateful forwarding (pending interest table (PIT)/forwarding information base) and in-network caching, yet many deployments still rely on classical cryptography that may be undermined by quantum-capable adversaries. Despite NDN’s built-in data authenticity via producer signatures, quantum-enabled attacks become plausible once RSA/ECC-based trust anchors are weakened by Shor-type attacks. Existing quantum-security efforts for NDN largely focus on post-quantum replacements of signatures or key exchange, but do not address the tension between confidentiality-preserving payload protection and NDN’s in-network processing (forwarding/caching decisions) in a quantum setting. This paper presents a quantum-enhanced NDN architecture that preserves NDN’s content-centric semantics—naming, PIT-driven forwarding, and cache-assisted dissemination of verifiable control objects—while delivering privacy-sensitive payloads as quantum homomorphic encryption (QHE)-encrypted quantum states over an underlying quantum network. QHE provides end-to-end confidentiality and enables authorized operations on encrypted quantum payloads without exposing plaintext. To make the design compatible with NDN forwarding, the proposed protocol encrypts the payload while keeping name-based forwarding semantics intact, and supports ciphertext-domain processing for selected policy functions. To protect provenance and integrity of NDN metadata, routing instructions, and session state, we incorporate quantum digital signatures (QDS) bound to NDN names and session identifiers. The main contributions are: (i) a QHE+QDS layered protection model tailored to NDN’s Interest and Data exchange; (ii) a session workflow that couples proactive entanglement distribution with NDN-style stateful signaling for efficient establishment and recovery; and (iii) a security analysis clarifying the achieved guarantees and the remaining trust assumptions. Security analysis indicates robustness against quantum man-in-the-middle and tampering attacks, with modest overhead on NDN control traffic.

The objective of congestion control is to maximize network bandwidth utilization and minimize the average flow completion time in data center networks. Key performance indicators required to achieve this objective are high throughput and low packet latency. Existing approaches have proposed various methods to control packet delivery based on various network events or parameters such as packet loss, bottleneck bandwidth, RTT, and queue length. However, these methods often result in suboptimal packet transmission states that compromise throughput or latency. We provide a method to overcome this drawback. To this end, we introduce a congestion control method called PRTT (Predicted RTT) that leverages predicted RTT for congestion control. By using accurately predicted RTT values, PRTT dynamically adjusts the packet delivery rate to control the number of in-flight packets. This enables the transmission to approach states where the buffer holds only a few packets while fully utilizing the link bandwidth. Experimental results show that PRTT achieves higher throughput, lower latency, and shorter flow completion times than other state-of-the-art methods, particularly under bursty traffic. These results demonstrate that PRTT offers a promising solution for congestion control.

The most important design goal in Optical Burst Switching (OBS) networks is to reduce burst loss resulting from resource contention. Especially, the higher the congestion degree in the network is, the higher the burst loss rate becomes. The burst loss performance can be improved by employing an appropriate congestion control. In this paper, to actively avoid contentions, we propose a dynamic load-aware congestion control scheme that operates based on the highest (called ‘peak load’) of the loads of all links over the path between each pair of ingress and egress nodes in an OBS network. We also propose an algorithm that dynamically determines a load threshold for adjusting burst sending rate, according to the traffic load in a network. Further, a simple signalling method is developed for our proposed congestion control scheme. The proposed scheme aims to (1) reduce the burst loss rate in OBS networks and (2) maintain reasonable throughput and fairness. Simulation results show that the proposed scheme reduces the burst loss rate significantly, compared to existing OBS protocols (with and without congestion control), while maintaining reasonable throughput and fairness. Simulation results also show that our scheme keeps signalling overhead due to congestion control at a low level.

Deeper investigation is needed for several open issues related to congestion and admission control within the field of the high speed B‐ISDN network with ATM protocol. The system must be more effective in resource management and robust enough to cope with changes in traffic environment, which nowadays is characterised by sophisticated multimedia services. In this paper a new admission and congestion control strategy is proposed. It originates from the Fast Buffer Reservation (FBR) scheme, a virtual circuit acceptance algorithm proposed by Prof. Turner, and enhances its effectiveness, thus resulting in a complete approach for bandwidth management. The scheme is called Modified FBR (M‐FBR). The use of the FBR method, in fact, when based on the powerful Excess Probability Criterion (EPC), seemed to be impeded by the occurrence of lock out problems. By adopting a new kind of service at the buffer exit, the new mechanism offers the required flexibility and robustness, as well as ease of control, while preserving burst integrity and exhibiting a high burst‐served to burst‐sent ratio.