Deployment of 100Gigabit Ethernet (GbE) links challenges the packet processing limits of commodity hardware used for Network Functions Virtualization (NFV). Moreover, realizing chained network functions (i.e., service chains) necessitates the use of multiple CPU cores, or even multiple servers, to process packets from such high speed links. Our system Metron jointly exploits the underlying network and commodity servers' resources: (i) to offload part of the packet processing logic to the network, (ii) by using smart tagging to setup and exploit the affinity of traffic classes, and (iii) by using tag-based hardware dispatching to carry out the remaining packet processing at the speed of the servers' cores, with zero inter-core communication. Moreover, Metron transparently integrates, manages, and load balances proprietary "blackboxes"together with Metron service chains. Metron realizes stateful network functions at the speed of 100GbE network cards on a single server, while elastically and rapidly adapting to changing workload volumes. Our experiments demonstrate that Metron service chains can coexist with heterogeneous blackboxes, while still leveraging Metron's accurate dispatching and load balancing. In summary, Metron has (i) 2.75-8× better efficiency, up to (ii) 4.7× lower latency, and (iii) 7.8× higher throughput than OpenBox, a state-of-the-art NFV system. © 2021 Owner/Author.

Recent control plane solutions in a software-defined network (SDN) setting assume physically distributed but logically centralized control instances: a distributed control plane (DCP). As networks become more heterogeneous with increasing amount and diversity of network resources, DCP deployment strategies must be both fast and flexible to cope with varying network conditions whilst fulfilling constraints. However, many approaches are too slow for practical applications and often address only bandwidth or delay constraints, while control-plane reliability is overlooked and control-traffic routability is not guaranteed. We demonstrate the capabilities of our optimization framework [1]-[3] for fast deployment of DCPs, guaranteeing routability in line with control service reliability, bandwidth and latency requirements. We show that our approach produces robust deployment plans under changing network conditions. Compared to state of the art solvers, our approach is magnitudes faster, enabling deployment of DCPs within minutes and seconds, rather than days and hours.

Current trends strongly indicate a transition towards large-scale programmable networks with virtual network functions. In such a setting, deployment of distributed control planes will be vital for guaranteed service availability and performance. Moreover, deployment strategies need to be completed quickly in order to respond flexibly to varying network conditions. We propose an effective optimization approach that automatically decides on the needed number of controllers, their locations, control regions, and traffic routes into a plan which fulfills control flow reliability and routability requirements, including bandwidth and delay bounds. The approach is also fast: The algorithms for bandwidth and delay bounds can reduce the running time at the level of 50x and 500x, respectively, compared to state-of-the-art and direct solvers such as CPLEX. Altogether, our results indicate that computing a deployment plan adhering to predetermined performance requirements over network topologies of various sizes can be produced in seconds and minutes, rather than hours and days. Such fast allocation of resources that guarantees reliable connectivity and service quality is fundamental for elastic and efficient use of network resources.

Existing approaches to solving combinatorial optimization problems on graphs suffer from the need to engineer each problem algorithmically, with practical problems recurring in many instances. The practical side of theoretical computer science, such as computational complexity, then needs to be addressed. Relevant developments in machine learning research on graphs are surveyed for this purpose. We organize and compare the structures involved with learning to solve combinatorial optimization problems, with a special eye on the telecommunications domain and its continuous development of live and research networks.

Streaming high-quality video over dynamic radio networks is challenging. Dynamic adaptive streaming over HTTP (DASH) is a standard for delivering video in segments, and adapting its quality to adjust to a changing and limited network bandwidth. We present a machine learning-based predictive pre-fetching and caching approach for DASH video streaming, implemented at the multi-access edge computing server. We use ensemble methods for machine learning (ML) based segment request prediction and an integer linear programming (ILP) technique for pre-fetching decisions. Our approach reduces video segment access delay with a cache-hit ratio of 60% and alleviates transport network load by reducing the backhaul link utilization by 69%. We validate the ML model and the pre-fetching algorithm, and present the trade-offs involved in pre-fetching and caching for resource-constrained scenarios.

5G promises to usher in the industrial 4.0 era. In that era, intricately managed autonomous industrial sites with for example remotely controller equipment and autonomous units promise previously unseen levels of efficiency. Although such scenarios are elusive, they come with strict long-since established safety requirements. To uphold such requirements, intelligent industrial 5G networks, that actively take into account prevailing conditions and dynamics of the workers on the site, the equipment, and the network, are needed. Little is known about the dynamics of actual industrial 5G networks and the interplay between network performance and QoE. In this paper, as a step towards intelligent industrial 5G networks, we measure network performance for an industrial 5G network, and conduct QoE experiments with remote controlled industrial equipment on an operational site. The results revealed unexpected relationships between QoE and network performance that shows how important domain-specific knowledge is when researching intelligent industrial 5G networks.

We propose a novel distributed leader election algorithm to deal with the controller and control service availability issues in programmable networks, such as Software Defined Networks (SDN) or programmable Radio Access Network (RAN).  Our approach can deal with a wide range of network failures, especially intermittent network partitions, where splitting and merging of a network repeatedly occur. 

In contrast to traditional leader election algorithms that mainly focus on the (eventual) consensus on one leader, the proposed algorithm aims at optimizing control service availability, stability and reducing the controller state synchronization effort during intermittent network partitioning situations. To this end, we design a new framework that enables dynamic leader election based on real-time estimates acquired from statistical monitoring. With this framework, the proposed leader election algorithm has the capability of being flexibly configured to achieve different optimization objectives, while adapting to various failure patterns. Compared with two existing algorithms,  our approach can significantly reduce the synchronization overhead (up to 12x) due to controller state updates, and maintain up to twice more nodes under a controller.

Managing and balancing load in distributed systems remains a challenging problem in resource management, especially in networked systems where scalability concerns favour distributed and dynamic approaches. Distributed methods can also integrate well with centralised control paradigms if they provide high-level usage statistics and control interfaces for supporting and deploying centralised policy decisions. We present a general method to compute target values for an arbitrary metric on the local system state and show that autonomous rebalancing actions based on the target values can be used to reliably and robustly improve the balance for metrics based on probabilistic risk estimates. To balance the trade-off between balancing efficiency and cost, we introduce 2 methods of deriving rebalancing actuations from the computed targets that depend on parameters that directly affects the trade-off. This enables policy level control of the distributed mechanism based on collected metric statistics from network elements. Evaluation results based on cellular radio access network simulations indicate that load balancing based on probabilistic overload risk metrics provides more robust balancing solutions with fewer handovers compared to a baseline setting based on average load.

For large-scale programmable networks, flexible deployment of distributed control planes is essential for service availability and performance. However, existing approaches only focus on placing controllers whereas the consequent control traffic is often ignored. In this paper, we propose a black-box optimization framework offering the additional steps for quantifying the effect of the consequent control traffic when deploying a distributed control plane. Evaluating different implementations of the framework over real-world topologies shows that close to optimal solutions can be achieved. Moreover, experiments indicate that running a method for controller placement without considering the control traffic, cause excessive bandwidth usage (worst cases varying between 20.1%-50.1% more) and congestion, compared to our approach.

In this paper we present Metron, a Network Functions Virtualization (NFV) platform that achieves high resource utilization by jointly exploiting the underlying network and commodity servers’ resources. This synergy allows Metron to: (i) offload part of the packet processing logic to the network, (ii) use smart tagging to setup and exploit the affinity of traffic classes, and (iii) use tag-based hardware dispatching to carry out the remaining packet processing at the speed of the servers’ fastest cache(s), with zero inter-core communication. Metron also introduces a novel resource allocation scheme that minimizes the resource allocation overhead for large-scale NFV deployments. With commodity hardware assistance, Metron deeply inspects traffic at 40 Gbps and realizes stateful network functions at the speed of a 100 GbE network card on a single server. Metron has 2.75-6.5x better efficiency than OpenBox, a state of the art NFV system, while ensuring key requirements such as elasticity, fine-grained load balancing, and flexible traffic steering.