Recursive layering (reusing the same protocol stack across layers) is re-emerging in 5G-Advanced and 6G–from network slicing to O-RAN (Open Radio Access Network) disaggregation-yet we still lack a quantitative understanding of how bottlenecks propagate across these stacked control/data loops. We model such systems with the Performance Evaluation Process Algebra (PEPA) and take the Recursive InterNetwork Architecture (RINA) as a canonical example. Eight scenarios capture typical wireless deployments: single- and multi-layer stacks, shared-buffer contention, feedback-controlled retransmission, and soft-failure repair. Exact steady-state analysis of the resulting continuous-time Markov chains yields, for instance, a 55% throughput collapse when the bottleneck shifts from the lower to the upper layer, and quantifies how buffer overflows propagate upward. The study also exposes two methodological limits: (i) state-space size (number of possible system states) grows super-linearly (36 to 1296 states with one extra layer), exhausting mainstream PEPA tools, and (ii) exponential timing distorts closed-loop dynamics by up to 18% versus phasetype approximations. These findings signal the need for scalable, non-Markovian formalisms—or hybrid analytic/simulation workflows—to keep pace with 6G recursion.

Technological advancements have provided wireless links with very high data rate capacity for 5G/6G mobile networks and WiFi 6, which will be widely deployed by 2025. However, the capacity can have substantial fluctuations, violating the assumption at the transport layer that the capacity is (almost) steady. In this paper, we present a general and efficient, yet deployable solution to this problem through a novel design empowered with a rich theory, allowing a significantly improved experience in using new technologies, especially mobile cellular services. We employ the well-known theory of food-chain models in biology, where a bottleneck link can be modeled as prey, while flows are predators. We extend this model to a chain of predators and preys to form a multi-hop congestion controller, called LGCC. Through simulation evaluation with real-life 5G traces we show the effectiveness of LGCC, compared with the state-of-the-art ABC (Accel-Brake Control). Our results show an order of magnitude bottleneck queuing delay decrease, with only a small decrease in throughput because LGCC tries to never exceed link capacities. LGCC’s design can additionally open a new paradigm in stable multi-hop congestion control and flow aggregation.

To fight global warming, carbon emissions must urgently be reduced. In this paper, we look at opportunities to do so by diminishing the operational energy usage of an important always-on element of the Internet: server systems. Our measurements from a server host indicate that there is probably not much to be gained by making a significant software change - but, depending on the system, a simple, easily overlooked configuration update may make a difference at no performance cost. This difference is small per server (in the order of an LED light bulb). Given the multitude of permanently operational servers in the world, it may however be significant at scale.

Deploying a new network architecture in the Internet requires changing some, but not necessarily all elements between communicating applications. One way to achieve gradual deployment is a proxy or gateway which "translates" between the new architecture and TCP/IP. We present such a proxy, called "Performance Enhancing Proxy for Deploying Network Architectures (PEP-DNA)", which allows TCP/IP applications to benefit from advanced features of a new network architecture without having to be redeveloped. Our proxy is a kernel-based Linux implementation which can be installed wherever a translation needs to occur between a new architecture and TCP/IP domains. We discuss the proxy operation in detail and evaluate its efficiency and performance in a local testbed, demonstrating that it achieves high throughput with low additional latency overhead. In our experiments, we use the Recursive InterNetwork Architecture (RINA) and Information-Centric Networking (ICN) as examples, but our proxy is modular and flexible, and hence enables realistic gradual deployment of any new "clean-slate" approaches.

Reliable consistent communication over millimeter-wave (mmWave) channels is a challenging problem due to their sensitivity to blocking of Line of Sight connections. MmWave is a key building block in 5G and future generation cellular networks, making solutions to this problem space important. Our aim is to use predictive control to manage and simultaneously use multiple available mmWave paths to achieve reliable consistent communication (i.e., steady transmission rate with low delay) with a multipath proxy. To this end we investigate transient solutions of Markov Modulated Fluid Queue models (MMFQ), apt because the mmWave blocking has been modeled with Markovian models. We propose a combination of models that can be solved using newly proposed matrix analytic techniques in a timely enough manner for use in real-time control. This gives us a prediction of either proxy queue distributions or probabilities of reaching proxy buffer levels over a short time horizon, enabling the proxy to make preemptive path decisions to maintain a desired Quality of Service. A proof of concept simulation study demonstrates the efficacy of our proposed MMFQ-based predictive approach over either static or purely reactive control approaches.

RINA, the Recursive InterNetwork Architecture, is a novel “back to basics” type approach to networking. The recursive nature of RINA calls for radically different approaches to how networking is performed. It shows great potential in many aspects, e.g. by simplifying management and providing better security. However, RINA has not been explored for congestion control yet. In this paper, we take first steps to investigate how congestion control can be performed in RINA, and demonstrate that it can be very efficient because it is applied close to where the problem happens, and through its recursive architecture, interesting effects can be achieved. We also show how easily congestion control can be combined with routing, enabling a straightforward implementation of in-network resource pooling.

Datacenter transport has attracted much recent interest, however, most proposed improvements require changing the datacenter fabric, which hinders their applicability and deployability over commodity hardware. In this paper, we present a novel congestion controller, Logistic Growth Control (LGC), for datacenters which does not require changes to the datacenter fabric. LGC uses a similar ECN marking as in DCTCP, but adapts to congestion using the logistic growth function. This function has been proven to have nice characteristics including stability, convergence, fairness, and scalability, which are very appealing for congestion control. As a result, our LGC mechanism operates in the datacenter network in a more stable and fair manner, leading to less queuing and latency. LGC also behaves better than DCTCP, and it converges to the fair share of the bottleneck link capacity irrespective of the Round-Trip-Time (RTT). We discuss the stability and fairness of LGC using a fluid model, and show its performance improvement with simulations.

Fairness is an important property of computer networks and systems. In a wide range of these systems such as distributed multi-hop wireless networks, multihomed networks, and cloud computing, each user may be allocated a number of system resources; this resembles a many-to-many relationship between the sets of users and resources, which raises the problem of system-wide fair resource allocation. In this paper, we assume that each user/node can be allocated a number of resources, which could be either in its neighborhood or far from it. As a key difference with previous works and through incorporating the concept of “flow”, we model near/far resources allocated to nodes. To attain fair flow control in such systems, first we model this situation by introducing a new multi-server system called multi-ring, in which a server that represents a resource, can serve only a subset of either neighboring or far nodes in the system. Then, we define a centralized optimization problem to attain weighted proportional fairness among all nodes meaning that the sum of allocated capacities from all servers to each node (while considering its flow) is fair. We evaluate fairness properties of multi-ring networks and provide conditions on system parameters under which a system can have a fair resource allocation. Moreover, we present a distributed method to attain fairness in distributed environments, and its stability/convergence is evaluated by non-linear discrete dynamical systems. We present conditions under which the system is stable, and through numerical analysis, we show how to obtain stable system parameters for large systems. The effectiveness of the presented method is studied through extensive numerical evaluation. Results show the success of our method in attaining fairness for various topologies and system parameters, and confirm our stability analysis. A number of systems with fairness issues are also studied as potential applications of our model.

Maintaining efficiency and fairness is a challenging problem in distributed systems and networks. In this paper, we focus on distributed multi-server systems and networks in which each user may be allocated resources by different servers. Reemphasizing polling systems as abstractions of resource sharing systems, in this paper, first we introduce a multi-server polling system in which each server (resource) can poll (be allocated to) only a subset of queues (users) in the system to model a wide range of multi-server systems such as multihomed networks and cloud computing. Then, to obtain a fair resource allocation vector to queues, a network utility maximization problem with a general utility function is defined. Depending on the type of the utility function, the presented scheme can attain different kinds of fairness such as weighted proportional and max-min fairness. Although maintaining fairness is important in many applications, providing efficiency is also crucial. Hence, we present an efficient algorithm to convert the obtained fair resource allocation vector into a Markovian routing matrix to determine the polling order of queues. This algorithm is capable of improving performance measures such as delay variance and mitigating short-term unfairness by minimizing the probability of consecutive polling of the same queue. Two distributed schemes are presented to obtain fairness and efficiency in even highly dynamic and distributed environments. The effectiveness of the presented schemes is also studied through simulation and numerical evaluation. Our results show their success in attaining fairness and efficiency in dynamic multi-server distributed systems and networks.

Recent advancements in wireless protocols and technologies such as IEEE 802.11n enhances communication systems in terms of offering high physical rates that are well-suited for multimedia and bandwidth-hungry applications. Since efficiency at the medium access control (MAC) layer decreases with increasing the physical rate, few researches have used aggregation, as a compensatory method, to improve efficiency. They have, however, underscored scalability in terms of parameters such as physical rate or number of users. Thus, providing a more scalable MAC protocol has become as issue of paramount concern. In this paper, we propose the Dual-channel Token-based MAC (DT-MAC) protocol that can provide scalability and improve efficiency especially for a large number of users and high physical rates. Then, DT-MAC is analytically evaluated in saturated conditions, and a predictable and optimal bandwidth allocation scheme to sub-channels is proposed. Simulation results for various parameters show that DT-MAC can approximately improve throughput 68% and decrease transmission delay and jitter nearly 90% compared with IEEE 802.11n for a dense high-data-rate single-hop network and operates well under low load.