Search

BCube is a well-known recursively defined network structure. It provides multiple low-diameter paths and good fault-tolerance for data center networks (DCNs). Its distributed routing algorithm, BCube Source Routing (BSR), can be deployed rapidly and conveniently to build multiple parallel path sets. But in the worst case, BSR may suffer from flow collisions and waste $50\%$ of the capacity of each BCube link. In this paper, to fully use those redundant links and to improve BCube load balancing, we supplement the BCube topology with a central master computer and we design two centralized dynamic parallel flow scheduling algorithms: CDPFS and CDPFSMP, for single-path and multi-path respectively. We focus on finding the least congested path for each flow by analyzing the information about the state of the global network. Furthermore, we allocate those paths to each flow in parallel. The simulation result shows that our proposed algorithms take advantage of BCube structure and deliver high-performance solutions for load balancing problems.

Routing protocol for Low Power Lossy Network (RPL) is a proactive network layer routing protocol. It is suitable for Internet of Things (IoT). It is also a source routing protocol. It works by creating a tree based network topology called Directed Acyclic Graph (DAG). Based on the position of the nodes from the root, rank value is allocated for each node. Rank is a unique value given to all the nodes in the DAG. It increases in the downward direction from the root. This value is used for prevention of loops, formation and maintenance of topology. Certain malignant nodes use this rank value and introduce a false routing by forcing the neighbors to route the packets through them. The consequences of this attack are more destructive in nature. It decreases throughput and increases delay in communication. Since the devices in IoT are resources constrained, there is an urge to address rank attack immediately. In this paper, a thorough review on the effects of rank attack and its countermeasures are presented.

Many organizations nowadays have multiple sites at different geographic locations. Typically, transmitting massive data among these sites relies on the interconnection service offered by ISPs. Segment Routing over IPv6 (SRv6) is a new simple and flexible source routing solution which could be leveraged to enhance interconnection services. Compared to traditional technologies, e.g., physical leased lines and MPLS-VPN, SRv6 can easily enable quick-launched interconnection services and significantly benefit from traffic engineering with SRv6-TE. To parse the SRv6 packet headers, however, hardware support and upgrade are needed for the conventional routers of ISP. In this paper, we study the problem of SRv6 incremental deployment to provide a more balanced interconnection service from a traffic engineering view. We formally formulate the problem as an SRID problem with integer programming. After transforming the SRID problem into a graph model, we propose two greedy methods considering short-term and long-term impacts with reinforcement learning, namely GSI and GLI. The experiment results using a public dataset demonstrate that both GSI and GLI can significantly reduce the maximum link utilization, where GLI achieves a saving of 59.1% against the default method.

The strong increase in the traffic volume and sudden changes in recent years have led to the evolution of carrier-grade network architectures, culminating in the current 5G generation. Increasing cell density due to traffic evolution towards xURLLC represents a new investment in the backhaul infrastructure expansion. Thus, fiber cable deployments become unsustainable in terms of urban site availability and monetary costs. This poses a use-case opportunity for line-of-sight wireless links, especially on mmWave technologies. However, the technology is lossy, while also suffering from loss of line-of-sight, making it unreliable. The work in this dissertation focuses on mitigating those problems in mmWave links, as part of 5G backhaul infrastructures and further generation of cellular communications. This is done through two different perspectives. The first perspective addresses the backhaul reliability with adaptive forwarding of 5G QoS flows, based on link quality assertion. To achieve this, a monitoring solution based on In-band Network Telemetry was designed, based on programming pipeline implementation. In the data plane, L2 forwarding and bridging, as well as SR-MPLS routing was implemented, using a forwarding pipeline in P4, that incorporates the telemetry model. Regarding the control plane, an ONOS application was written to manage the pipeline execution, while also receiving information from N1 and N2 5G logical interfaces, from UE and PDU session contexts. With this, labeled path routes and data flows are created and optimized, adapting flow paths to link quality conditions, enforcing 5QI constraints, and achieving xURLLC services. The experiments performed have shown that degraded links are avoided, and flow paths meet QoS conditions. The second perspective is performed at the transport layer, where improvements over mmWave channels are made through the use of an Adaptive and Causal Random Linear Network Coding (AC-RLNC) algorithm, on top of PHY and MAC layer coding schemes, to achieve xURLLC services. The solution is then compared to RLNC rateless codes. For this purpose, a simulator was developed for the algorithm execution, using a real mmWave channel data transfer model. Data point collection is performed regarding its normalized throughput, and means and maximum in-order delivery delay measurements. Results have demonstrated that it is possible to obtain ultra-reliable and low latency services with adaptive network coding above mmWave links. O aumento repentino no volume de tráfego e mudanças significativas nos padrões de utilização de rede, nos últimos anos, promoveram a evolução de arquitecturas de rede backhaul, culminando com as atuais infra-estruturas 5G. O aumento da densidade de small cells, devido à evolução do tráfego para xURLLC, remete para investimentos na expansão do backhaul da rede. Desta forma, a instalação de cablagem de fibra torna-se insustentável no que diz respeito à disponibilidade urbana e custos monetários. Isto promove a consideração ao uso de ligações de rádio Gigabit ponto a ponto, nomeadamente as tecnologias mmWave. Estas ligações possuem problemas no que diz respeito à sua fiabilidade por natureza, e ao mesmo tempo, sofrem de problemas de perda da linha de vista. O trabalho desta dissertação foca-se na mitigação destas limitações com a utilização de ligações mmWave como parte de infra-estruturas de backhaul 5G. Isto é feito em duas perspetivas diferentes. A primeira perspectiva visa melhorar a fiabilidade do backhaul através do encaminhamento adaptável de fluxos de QoS 5G, com base na qualidade do canal. Posto isto, foi concebida uma solução baseada em telemetria in-band, focado em pipelines programáveis. No plano de dados, forwarding e bridging na camada 2, assim como o encaminhamento SR-MPLS foram implementados, usando a linguagem P4, que incorpora o modelo de telemetria. No plano de controlo foi desenvolvida uma aplicação ONOS para gerir a execução da pipeline, que, recebendo informação das interfaces lógicas N1 e N2 5G para controlo da sessão da UE e PDU, gere e otimiza rotas de encaminhamento por labels MPLS nos switches programáveis, que recebem fluxos de dados QoS, adaptando as rotas de fluxo em tempo real considerando a qualidade das ligações de rede, e impondo restrições 5QI para obter serviços xURLLC. As experiências realizadas demonstram que as ligações não fiáveis são evitadas, e os fluxos de dados 5G satisfazem as condições de QoS na rede de backhaul, end-to-end. A segunda perspectiva é realizada ao nível da camada de transporte, em que as melhorias ao mmWave são feitas através da utilização de um algoritmo de codificação RLNC adaptável e causal (AC-RLNC) por cima de esquemas de codificação na camada PHY e MAC, com o fim de obter serviços xURLLC. A solução é comparada com codificações rateless RLNC. Para isto, foi desenvolvido um simulador de execução dos algoritmos sobre um modelo real de um canal mmWave. A recolha de pontos de dados é feita, incluindo throughput normalizado, média e máximo atraso na entrega ordenada de pacotes. Os resultados demonstraram que é possível obter uma largura de banda ultra-confiável e serviços de baixa latência com adaptive network coding nas ligações mmWave. Mestrado em Engenharia de Computadores e Telemática

In multi-radio wireless mesh networks, one node is eligible to transmit packets over multiple channels to different destination nodes simultaneously. This feature of multi-radio wireless mesh network makes high throughput for the network and increase the chance for multi path routing. This is because the multiple channel availability for transmission decreases the probability of the most elegant problem called as interference problem which is either of interflow and intraflow type. For avoiding the problem like interference and maintaining the constant network performance or increasing the performance the WMN need to consider the packet aggregation and packet forwarding. Packet aggregation is process of collecting several packets ready for transmission and sending them to the intended recipient through the channel, while the packet forwarding holds the hop-by-hop routing. But choosing the correct path among different available multiple paths is most the important factor in the both case for a routing algorithm. Hence the most challenging factor is to determine a forwarding strategy which will provide the schedule for each node for transmission within the channel. In this research work we have tried to implement two forwarding strategies for the multi path multi radio WMN as the approximate solution for the above said problem. We have implemented Global State Routing (GSR) which will consider the packet forwarding concept and Aggregation Aware Layer 2 Routing (AAL2R) which considers the both concept i.e. both packet forwarding and packet aggregation. After the successful implementation the network performance has been measured by means of simulation study.

Segment Routing (SR) is a new routing paradigm based on source routing and provide traffic engineering (TE) capabilities in IP network. By extending interior gateway protocol(IGP), SR can be easily applied to IP network. However, upgrading current IP network to a full SR one can be costly and difficult. Hybrid IP/SR network will last for some time. Aiming at the low flexibility problem of static TE policies in the current SR networks, this paper proposes a Deep Reinforcement Learning (DRL) based TE scheme. The proposed scheme employs multi-path transmission and use DRL to dynamically adjust the traffic splitting ratio among different paths based on the network traffic distribution. As a result, the network congestion can be mitigated and the performance of the network is improved. Simulation results show that our proposed scheme can improve the throughput of the network by up to 9% than existing schemes.

Zaradi večanja števila uporabnikov in višanja nazivnih hitrosti dostopovnega omrežja, se operaterji soočajo z večanjem prometa v jedrnem omrežju. Za zagotavljanje delovanja vseh storitev, se operaterji jedrnih omrežji spogledujejo s tehnikami prometnega inženiringa, s katerimi želijo poenostaviti upravljanje omrežja in zvišati kakovost storitve. Fleksibilni algoritem je nadgradnja prometnega inženiringa, z vpeljavo usmerjevalnega protokola segmentnega usmerjanja, ki v operaterskem jedru nadomesti protokol distribucije label. Z vpeljavo segmentnega usmerjanja, ki omogoča razširljivost, želimo poenostaviti upravljanje prometa v omrežju in omogočiti prometni inženiring. Z vpeljavo segmentnega usmerjanja na usmerjevalnikih v laboratorijskem omrežju sem preizkusil delovanje protokola usmerjanja in prometnega inženiringa. Ugotovil sem, da nova arhitektura usmerjanja omogoča optimizacijo omrežja in je hkrati fleksibilna pri podpiranju novih načinov uporabe. Fleksibilni algoritem prometnega inženiringa predstavlja tudi določene omejitve, ki sem jih zbral v izsledkih, to pa zahteva nadaljnji razvoj in testiranje pred vpeljavo v omrežja. Due to the increase in the number of users and the increase in speeds of the access network, operators are faced with increasing traffic in the core network. To ensure the operation of all services, the core network operators are considering traffic engineering techniques that aim to establish network management and increase the quality of services. The flexible algorithm is an upgrade to traffic engineering, with the introduction of the segment routing protocol, which replaces the label distribution protocol in the core network. By introducing segment routing, we aim to simplify network traffic management and enable traffic engineering. By implementing segment routing on the routers in the lab environment, I tested the performance of the routing protocol and traffic engineering. I found that the new routing architecture allows for network optimization while being flexible in supporting new use cases. The flexible traffic engineering algorithm presents certain limitations that I gathered in the findings. It requires further development before being implemented in production networks.

Dynamic opportunistic channel access with software-defined radio at a network layer in distributed cognitive IoT introduces a concurrent channel selection along with end-to-end route selection for application data transmission. State-of-the-art cognitive IoT big data-based routing protocols are not explored in terms of how the spectrum management is being coordinated with the network layer for concurrent channel route selection during end-to-end channel route discovery for data transmission of IoT and big data applications. In this paper, a reactive big data-based “cognitive dynamic source routing protocol” is proposed for cognitive-based IoT networks to concurrently select the channel route at the network layer from source to destination. Experimental results show that the proposed protocol cognitive DSR with concurrent channel route selection criteria is outperformed. This will happen when it is compared with the existing distributed cognitive DSR with independent channel route application data transmission.

This article demonstrates the design, analysis, implementation, and operation of a 1-Tb/s carrier-class programmable data-plane SDN system. The system is based on the principles of programmable data planes and is implemented using a recent software-defined networks (SDNs) manifestation, called bitstream, and FPGAs. It supports P4 parsing to facilitate a 1-Tb/s-capable SDN, carrier-class network element. We discuss how bitstream fits in a service provider network architecture. A node-level FPGA implementation using a reconfigurable match-tables technique is presented. We show that bitstream brings programmability to the data plane without the need for large custom silicon. A controller that interoperates with P4 is developed. A terabit carrier-class SDN node that demonstrates layer-0 to layer-4 protocols is built and shown in a test bed. We show FPGA-level, controller-level, and service-level operations. A management plane for carrier-class performance measurements and provisioning is showcased. A qualitative and quantitative study benchmarking this article versus other approaches is put forth. We show benchmarking for different services, such as equal-cost multiple paths and sub-50 ms restoration from the test-bed implementation.

Recently, the Vehicular Ad hoc Networks (VANETs) have been gaining a lot of importance to improve road safety to ensure the comfort of car drivers. These networks need an infrastructure that is robust with channels of communication that are efficient and effective protocols of routing. The VANETs have become a very challenging area of research since they have certain specificities in identifying ideal strategies for routing and making sure the challenge of quality of service is met. There is a Quadratic Assignment Problem (QAP), which is another challenging issue in combinatorial optimization. For obtaining results that are satisfactory within a reasonable time frame, several heuristic algorithms are applied. One among them is the algorithm known as the Simulated Annealing (SA). The proposed algorithm called the slow heat SA is based on Road-Based using Vehicular Traffic-Reactive (RBVT-R) and modified SA. The results of the experiment have proved that the proposed method achieves better performance in terms of packet delivery ratio and the end to end delay when compared to the Geographic Source Routing (GSR), RBVT-R, and SA routing protocol.

The paradigm of Network Function Virtualization (NFV) requires the underlying networks to be able to route traffic through dynamically deployed nodes. The research presented here demonstrates the feasibility of using segment routing (SR), a modern incarnation of the source routing paradigm, and suitable SDN controllers to accomplish the traffic steering goal. Specifically, we focused on IPv4 SR and built a proof of concept SRMPLS network using Juniper vQFX routers with custom VNFs. The Juniper NorthStar SDN controller was used to build SR- MPLS LSPs via the Path Computation Element Communication Protocol (PCEP). We validated the operation of our proof of concept in two scenarios: VNFs reinstantiation and service chain creation and support. In both cases traffic can be successfully steered through the functions, hence demonstrating for the first time the feasibility of adoption of the proposed technologies.

The multi-tenancy concept in cloud data center (DC) networks paves the way towards advancements and innovation in the underlying infrastructure such as network virtualization. Multicast routing is essential in leveraging multi-tenancy to its full potential. However, traditional IP multicast routing is not suitable for DC networks due to the need to support a massive amount of multicast groups and hosts. State-of-the-art DC multicast routing approaches aim to overcome these scalability issues by, for instance, taking advantage of the symmetry of DC topologies and the programmability of DC switches to compactly encode multicast group information inside packets, thereby reducing the overhead resulting from the need to store the states of flows at the network switches. Although these approaches scale well with the number of multicast groups, they do not perform well with group sizes and, as a result, yield substantial traffic control overhead and network congestion. In this article, we present Bert, a scalable source-initiated DC multicast routing approach that scales well with both the number and size of multicast groups through the clustering of multicast group members where each cluster employs its own forwarding rules. Compared to the state-of-the-art approach, Bert yields much less traffic control overhead by significantly reducing packet header sizes and eliminating switch memory usage across the switches.

Segment Routing (SR) is a source routing paradigm which is widely used in Traffic Engineering (TE). By using SR, a node steers a packet through an ordered list of instructions called segments. By some extensions of interior gateway protocol, SR can be applied to IP/MPLS or IPv6 network without signal protocol. SR over IPv6 (SRv6) is attracting wide attention because of its interoperation ability with IPv6. However, upgrading the existing IPv6 network directly to a full SRv6 one can be difficult, because large-scale equipment replacement or software upgrade may cause economic and technical problems. TE in partially deployed SR network is becoming a hot research topic. In this paper, we propose the TE algorithm Weight Adjustment-SRTE (WA-SRTE) in partially deployed SRv6 network, in which SRv6 capable nodes are dispersedly deployed. Our objective is to minimize the network’s maximum link utilization. WA-SRTE converts the TE problem into a Deep Reinforcement Learning problem and optimizes the OSPF weight, SRv6 node deployment and traffic paths simultaneously. Besides, traffic variation is also considered and we use a representative Traffic Matrix (TM) to epitomize the traffic characteristics over a period of time. Experiments demonstrate that with 20% to 40% of the SRv6 nodes deployed, we can achieve TE performance as good as in a full SR network for the experiment topologies. The results with WA remarkably outperform the results without it. Our algorithm also gets near-optimal results with changing traffic.

In VANETs, vehicles need to rely on other vehicles for any real-time applications. Therefore the cooperation of vehicles is needed for relaying the messages in successful manner. Most often this is not possible since there is a possibility for some vehicle to act selfishly and thereby they fail to forward messages. Moreover, during the routing process, the prior information about the neighbours will help in the selection of best data forwarding nodes. In this paper, we have proposed Similarity-based Trustworthy Routing algorithm which incorporates social features for performing trustworthy routing by choosing the best forwarder. To further improve the updating process of trust value, we have incorporated two basic approaches, one based on direct Acknowledgment during Encounter Strategy (AES) and the other one based on Game-theoretic Broadcasting Strategy (GTBS). The adaption of AES and GTBS enhances the security of the data traffic in VANETs by intelligently identifying the malicious nodes. This work identifies and overcomes the vulnerabilities of different kind of attacks with minimum overhead in terms of transmission. Moreover, the solutions provide significant performance improvement in terms of packet delivery ratio even in the presence of misbehaving nodes. Our results show, how our methods outperform others, by comparing with other existing approaches like dLife and Epidemic Routing protocols.

Drivers can be provided with several beneficial services associated with video streaming in a vehicular ad-hoc network (VANET). Given the dynamic topology and high mobility of VANETs, a single path cannot support the required quality of service (QoS). To maximize global QoS metrics, a two-path model is proposed based on a disjoint algorithm to forward sub-streams over diverse paths from the transmitter to the receiver vehicle. In this solution, the video information spread in separate paths is categorized based on their priority. For this purpose, the protocol for transmitting each kind of video data should be selected cautiously. The present study aims to propose an ant colony optimization-based technique to establish the primary and secondary paths and enhance the QoS of routing paths. To achieve this goal, the QoS routing issue is formulated mathematically as a problem of constrained optimization. Moreover, to achieve high-quality video streaming, inter-frames are transmitted over the user datagram protocol and intra-frames are transmitted over the transmission control protocol (TCP). TCP transmission delays are also minimized using a TCP-ETX algorithm for selecting appropriate paths. According to the simulation results, the proposed two-path solution can be used to improve the quality of video streaming and to enhance the performance in terms of end-to-end delay, packet delivery ratio, and overhead. In this way, the proposed method can outperform several prominent routing algorithms such as adaptive QoS-based routing for VANETs, geographic source routing (GSR), intersection-based geographical routing protocol, and efficient GSR.

Accurate link latency information is essential for various traffic engineering problems, such as routing design and network diagnosis. However, due to the routing constraints, link latency measurement is still a challenging problem in hybrid software-defined networks (SDNs) and little literature has been found in this field. Due to cost, a hybrid SDN composed of conventional routers and SDN switches will exist for some time to come, which shows the necessity and the urgency to address this problem. In this paper, we investigate this link latency measurement problem in two different scenarios: 1) the conventional routers can only support the shortest path routing protocol, and 2) the conventional routers can support source routing protocol. For both of these scenarios, we show how to deploy a minimum number of monitors and how to construct measurement paths between monitors to measure all the link latencies. Several algorithms are presented to solve these problems and the evaluations on different topologies prove the superiority of the proposed methods.

The rapid advances in the wireless communication industry have paved the way for the enhancement of wireless mobile ad-hoc networks (MANETs) to support various domains including civilian environments, emergency operations, and military affairs. Source routing in MANETs is subject to some issues such as changes in the network topology, which lead to frequent link breakages that may increase the requests of route discoveries. Thus, this paper aims to enhance on-demand source routing protocols by proposing two mechanisms, a zone-based route discovery mechanism (ZRDM) and a link failure prediction mechanism (LFPM). ZRDM aims to control the flooding of route requests, and LFPM aims to avoid route breakages caused by node mobility. The performance of the proposed mechanisms was evaluated using network simulator 3 in terms of normalized routing load, average end-to-end delay, and packet delivery ratio. The experimental results showed that the proposed mechanisms outperform well-known mechanisms such as the dynamic source routing (DSR) protocol, reliable DSR, and zone-based DSR and segment-based DSR.

This paper propose a novel secure routing mechanism called Spatial and Energy Aware Trusted Dynamic Distance Source Routing (SEAT-DSR) algorithm for enhancing the network life time of wireless sensor networks. Here, the spatial information, energy level, and the effectiveness of data quality are equalized by the Quality of Service (QoS) based energy aware routing algorithms. In addition to this approach, a standard clustering algorithm is also incorporates for grouping the wireless sensor nodes based on the trust score, spatial information, energy level and the distance between the nodes. In this SEAT-DSR is also capable of making decision over the evaluation metrics that are decided and expressed the QoS. Moreover, a new hierarchical trust mechanism is also introduced in this model which adopts multi-attributes of many wireless sensor nodes according to the data communication speed, data size, energy consumption, and the recommendation. This new hierarchical trust method relies over an improved the sliding window time by considering the presence of various attacks frequency to identify the attackers by discovering their anomalous behaviour. The proposed SEAT-DSR is evaluated by conducting many experiments in a simulation environment that creates by using Network Simulator-2 (NS2). The experimental results of the proposed algorithm are proved that the average packet transfer rate is increased drastically than the existing secure routing methodologies.

Network-on-Chip (NoC) is widely used as an efficient communication architecture in multi-core and many-core System-on-Chips (SoCs). However, the shared communication resources in NoCs, e.g., channels, buffers, and routers might be used to conduct attacks compromising the security of NoC-based SoCs. Almost all of the proposed encryption-based protection methods in the literature need to leave some parts of the packet unencrypted to allow the routers to process/forward packets accordingly. This uncovers the source/destination information of the packet to malicious routers, which can be used in various attacks. In this paper, we propose the idea of secure anonymous routing with minimal hardware overhead to hide the source/destination information while exchanging secure information over the network. The proposed method uses a novel source-routing algorithm that works with encrypted destination addresses and prevents malicious routers from discovering the source/destination of secure packets. To support our proposal, we have designed and implemented a new NoC architecture that works with encrypted addresses. The conducted hardware evaluations show that the proposed security solution combats the security threats at an affordable cost of 1% area and 10% power overheads chip-wide.

Segment routing network is the evolution and innovation of IP routing technology. With the development of mobile services and cloud computing, the segment routing technology based on IPv6 has attracted more attentions. Segment Routing over IPv6 (SRv6) is a source routing solution for IPv6 networks. SRv6 has the ability of service chain and network programmability, which can strengthen the advantages of traffic engineering and cloud network to realize various innovative applications. This paper mainly utilizes SRv6 technology to implement a Service Function Chain (SFC) combined with network security applications. Experimental results show that using SRv6 to guide the SFC path for malicious attack detection and traffic restriction can effectively improve IPv6 network security and service quality.

Segment Routing (SR) leverages source routing. A node steers a packet through a controlled set of instructions, called segments, by prepending the packet with an SR header. A segment can represent any instruction topological or service-based. SR allows to enforce a flow through any topological path while maintaining per-flow state only at the ingress node of the SR domain. The Segment Routing architecture can be directly applied to the MPLS dataplane with no change on the forwarding plane. It requires a minor extension to the existing link- state routing protocols. This document illustrates the application of Segment Routing to solve the BGP Egress Peer Engineering (BGP-EPE) requirement. The SR-based BGP-EPE solution allows a centralized (Software Defined Network, SDN) controller to program any egress peer policy at ingress border routers or at hosts within the domain.

Lightning Network, the off-chain solution to Bitcoin, uses a combination of source routing, layered encryption and conditional commitments to perform anonymous and atomic multi-hop off-chain payments. The three predominant routing clients used in Lightning - LND, c- Lightning and Eclair - differ in their cost functions for adding channels to a payment path and hence result in differ paths. At the moment, there is no detailed study on how the different path selections affect the quality of the payment in terms of effectiveness and efficiency. Such an evaluation is needed to i) allow users to make informed choices of which client to use, and ii) enable developers of new routing algorithms to take lessons learned from the current designs into account. In this work, we analyze and compare the three predominant routing clients used by Lightning in terms of the path length, maximal delays a payment might experience, fee borne by the sender and success rate of multi-hop transactions. Based on our experiments and results, we recommend LND for users desiring payments with short paths and high success rates, c- Lightning for users desiring paths with low maximal latencies, and Eclair for those looking for cheap paths in terms of fees paid to the nodes that route payments as intermediaries. We further give guidelines for the design of future routing algorithms based on our insights gained from Lightning's current routing.