Recent advances in networking technologies, such as in-network computing (INC), have demonstrated significant potential for mobile networks. Among these, Random Linear Network Coding (RLNC), a class of forward error correction codes, has proven especially promising for enhancing reliability. RLNC reduces latency by transmitting additional coded packets, making it well-suited for supporting Ultra-Reliable Low Latency Communications (URLLC), a key requirement in nextgeneration mobile networks. To fully harness the benefits of RLNC in mobile networks, it is essential to closely monitor its operations to design effective RLNC schemes. This need arises from key factors, including the dynamic nature of mobile environments and the continuous emergence of new applications. However, achieving this in traditional mobile networks remains challenging due to their closed architectures and vendor lockin. We propose HawkVision, a monitoring solution for RLNC operations that leverages the flexibility of Open Radio Access Network (O-RAN). HawkVision supports monitoring various RLNC schemes, such as Sliding Window and Systematic Block Code, within mobile networks. It uses xApps in O-RAN to observe RLNC behavior in the RAN. We implement HawkVision using FlexRIC, a widely used O-RAN platform, and deploy a Key Performance Metric (KPM) xApp to collect relevant metrics. Testbed results demonstrate HawkVision’s effectiveness in monitoring the operations for different RLNC schemes.

To meet the stringent requirements of emerging 5G use cases that demand high reliability and low latency, the potential of in-network computing is realized by deploying Random Linear Network Coding (RLNC) recoders directly within the network infrastructure. However, the choice of network infrastructure configuration can significantly impact traffic latency as demonstrated in this paper. To investigate this, we compare the performance of RLNC recoder when implemented on two different switches, the Cisco Catalyst 9500 (C9500) and the Cisco Catalyst 9300 (C9300), focusing on reducing one-way delay (OWD). Our results demonstrate that the C9300 significantly outperforms the C9500, with$\mathbf{9 5. 6 \%}$of haptic packets recovered within 70 ms compared to 85.8% for the C9500. Notably, the C9300 consistently minimizes packet loss better, especially in haptic traffic, where the C9300 with RLNC recoder reduces packet loss to nearly zero. Additionally, under stress conditions with a 10 Mbps transmission rate, the C9300 continues to excel in reducing OWD, although the in-network computing performance in both switches exhibits similar reliability challenges. This study focuses on analysing the performance of RLNC recoding in cloudnative$\mathbf{5 G}$systems under various hardware conditions.

Emerging 5G/6G use cases span various industries, necessitating flexible solutions that leverage emerging technologies to meet diverse and stringent application requirements under changing network conditions. The standard 5G RAN packet error handling using retransmission reduces packet loss but can increase transmission delay. Random Linear Network Coding (RLNC) offers an alternative by proactively sending combinations of original packets, thus reducing both delay and packet loss. Previous research typically only simulates the integration of RLNC in 5G but does not demonstrate nor evaluate this integration in real 5G systems. In contrast, we implement and evaluate our approach through measurements with commercially available servers and switches, running the OpenAirInterface (OAI) 5G software stack. We introduce Flexible Network Coding (FlexNC), which enables the flexible fusion of several RLNC protocols. Specifically, FlexNC provides a forwarder that flexibly interfaces with multiple RLNC protocols in a cloud-native (containerized) solution. Network operators can configure FlexNC based on network conditions and application requirements. For boosting network programmability, our Recoder in the Network (RecNet) leverages In-Network Computing (INC) in intermediate network nodes. We have developed an open-source cloud-native (Docker-based) implementation of both FlexNC and RecNet on OAI, including INC for the Recoder on a Cisco Catalyst switch. Measurements for video, haptic, and audio traffic indicate that i.) FlexNC adapts to various application needs in terms of latency and packet loss, and RecNet significantly reduces packet loss for a remote user with minimal increase in delay compared to pure RLNC. To the best of our knowledge, this is the first article to report testbed measurement results for cloud-native network coding in a 5G system, thus creating a baseline for reliable 5G communication.

Edge clouds embrace Network Function Virtualization (NFV) to bring network functions (NFs) closer to end devices. The mobility nature of edge clouds and the emergence of new use cases, such as robot control or metaverse, demand NFs with high resilience. However, numerous NFs like firewalls require stateful processing that depends on historical processing values, referred to as NF states. Consequently, supporting stateful NFs with high resilience necessitates the maintenance of their states, often resulting in significant modification of the NFs. While providing high resilience for NFs, maintaining their processing performance is a critical challenge for existing solutions. Our proposed framework, StateProc, eliminates this concern by enhancing the processing capabilities of NFs through the utilization of NF states for custom processing. Through custom processing for state transfer, the evaluation results show that StateProc significantly improves the state transfer time, up to 93% faster than a notable state-of-the-art framework, without additional processing overhead.

Teleoperation has become mainstream in several applications, such as telesurgery, manufacturing, and construction. The human operator relies on video, audio, and haptic feedback data from the remote site to adjust operations, ensuring safety and collision avoidance. These applications require real-time perception and synchronization of modalities to maintain a high quality of experience, which presents a significant challenge. Human sensitivities are different among modalities, while communication networks rely on statistical multiplexing to maximize bandwidth utilization, often neglecting the specific latency requirements of each modality. We introduce SynCoDel to minimize asynchrony between video and haptic streams by considering event pairs, which are synchronized events. By prioritizing video packets within these pairs, we effectively utilize bandwidth resources initially reserved for high-priority haptic streams. Evaluation on a practical testbed with the programmable data plane and synthetic data shows that SynCoDel reduced asynchrony over half while maintaining low latency for haptic and video streams in congestion scenarios.

Emerging use cases, such as Tactile Internet, typically demand high reliability and low-latency communication. To fulfill these stringent requirements, 5G networks employ re-transmission within the Radio Access Network (RAN) in the event of packet loss, but at the expense of increased latency. An alternative approach to address this challenge involves leveraging Random Linear Network Coding (RLNC) to recover lost packets, thereby eliminating the necessity for retransmission within the RAN. We demonstrate the ability of in-network computing to run RLNC, particularly with the use of RLNC recoder, to enhance reliability and reduce latency within the network.

Network Functions (NFs) in edge clouds are required to provide scalability, fault tolerance, and mobility support. They all require maintaining NF states (i.e., processing results), e.g., for recovery, especially for stateful NFs like firewalls. Even though current solutions provide an alternative to storing states in memory, their design can support only a single requirement, either fault tolerance or scaling. Advocating the versatility, we propose StateOS - an operating system of NF states for user-defined programs supporting different requirements. Additionally, we propose a state transfer scheme, namely Divide-and-Conquer (DAC), to accelerate StateOS. The combination of DAC and StateOS demonstrates its efficiency for all three scenarios: scaling, fault tolerance, and service function chain acceleration.

Emerging network application paradigms, such as the Tactile Internet, re-emphasize the need for different Quality of Service (QoS) levels. Due to the large packet buffers in the underlying network data plane, Active Queue Management (AQM) is generally required to curtail packet latencies for flows requiring high QoS levels. At the same time, programmable data planes, such as P4, enable packet processing at line-speed, albeit with limited packet processing functionalities. However, the existing AQM mechanisms that support QoS differentiation are too complex to readily run on P4, while the existing AQM mechanisms that run on P4 do generally not support effective QoS differentiation. We address this gap by developing to the best of our knowledge the first AQM mechanism that supports effective QoS differentiation while running on P4. Specifically, we propose SP-CoDel, which combines the well-known Controlled Delay (CoDel) AQM mechanism with Static Priority (SP) scheduling for QoS differentiation. Also, we propose RED-SP-CoDel, which adds a RED AQM component to SP-CoDel so as to make the AQM with priorities essentially parameterless. As a community resource contribution, we substantially extend the existing P4Simulator to the novel fused P4-NS3 Simulator so as to enable the evaluation of packet processing mechanisms through the combined functionalities of P4 device emulation and NS3 packet simulation. Evaluations conducted with the P4 reference switch model in the P4-NS3 Simulator indicate that SP-CoDel and RED-SP-CoDel provide high QoS to high-priority data streams, i.e., significantly reduce latency and packet loss compared to CoDel, while effectively mitigating bufferbloat.

Existing Network Function Virtualization (NFV) service placements that reuse already deployed network functions either reuse an entire Service Function Chain (SFC) or only individual network functions while ignoring the chain configuration cost for configuring the SFC traffic steering and ignoring the reliability of the network functions. Also, the Mobile Edge Cloud (MEC) frameworks that are required to implement an NFV service placement should ideally seamlessly cooperate with the various existing NFV Management and Orchestration (MANO) frameworks. However, the existing MEC frameworks lack multi-MANO support. We formulate the novel Subchain-Aware NFV service Placement (SAP) optimization model that accounts for the configuration cost for stitching together reused network functions to an SFC and strives to reuse existing subchains of consecutive network functions (with already deployed SFC traffic steering), while accounting for the recovery cost of network functions with limited reliability. We develop Tabu-SAP, a Tabu search approach to solve the SAP optimization problem. Furthermore, we introduce the novel Automated Provisioning framework for MEC (APMEC) with open-source OpenStack implementation to enable the deployment of Tabu-SAP in real networks; APMEC supports multiple MANOs through a loose coupling MANO-MEC design. Our Tabu-SAP evaluations indicate an around eightfold increase of the number of supported SFCs compared to the state-of-the-art reuse of individual network functions, while substantially reducing the total cost, which includes the chain configuration cost. Also, for long SFCs of seven or more network functions, the Tabu-SAP total cost is less than 10% higher than the optimal solution (which requires over ten times longer execution time).

Live service migration with low service downtime is one of the main challenges in mobile edge cloud (MEC). In other words, it is important to guarantee that users continue to receive good service even if they cross through multiple MEC servers. In this paper, we propose a seamless service migration framework and apply it to an autonomous driving demonstration. We expect this paper to provide a perspective on how MEC can be used for practical autonomous driving in the near future.

It is vital for Tactile Internet to constantly maintain a low-latency control loop between sensors, actuators, and their controlling software applications. Mobile Edge Computing (MEC) is an essential technology that brings the elasticity of cloud computing to run controlling applications at a close proximity to controlled objects, thus reducing latency. To support the mobility of the objects, the underlying network has to be capable of migrating MEC applications seamlessly to guarantee the close proximity. However, it is challenging to migrate application states quickly and flexibly without interrupting the control loop. We propose FAST, a flexible scheme for direct and low-latency state transfer leveraging Software-Defined Networking (SDN). Evaluation results show that compared to state of the art, FAST reduces the service migration time by 80%.

5G ecosystems will benefit significantly from Multi-access Edge Computing (MEC) and Network Function Virtualization (NFV). While NFV allows for dynamical deployment of virtualized network functions, MEC allows applications to be deployed close to mobile users, thus reducing latency. When used together, NFV and MEC bring flexibility and enhanced performance to meet the user's demand. However, to increase service availability and maximize the convenience of MEC users, an MEC framework has to interface with multiple NFV orchestrators to utilize running network functions in an efficient manner. We introduce in this demonstration APMEC, a framework addressing the above two challenges. Via a combination of an interactive GUI, KPI views and a live-demo setup, we will showcase the advanced features of the framework.

Mobile Edge Computing (MEC) and Network Function Virtualization (NFV) are widely considered to be key players in the 5G era. Whereas MEC enables to reduce latency significantly by allowing applications to be deployed close to end users, NFV allows for flexible deployment of virtualized network functions. The performance and flexibility can be improved further by combining MEC and NFV. Existing frameworks for managing and orchestrating MEC applications and NFV are either tightly coupled or completely separated. The former design is inflexible and increases the complexity of one framework, while the latter leads to inefficient use of computation resources. In this paper, we extend our Automated Provisioning Framework for MEC (APMEC), which combines each MEC application and its respective network service (comprising a chain of virtual network functions) in a MEC service. We propose a novel MEC service placement algorithm allowing to reuse a subset of yet underloaded network functions. Our evaluation results, obtained from a testbed implementation and simulations, show that our solution allows to remarkably increase the utilization of network functions and simultaneously reduce routing cost. Specifically, it allows to accept at least 60% more user requests, and at the same time lowering the routing cost by more than 30%, as compared to the baseline approach.