Extending multivariate time series forecasting to resource-constrained edge devices is essential for enabling intelligent and sustainable IoT services. A common scenario involves vertically partitioned data across devices, where each device must forecast its own variables while benefiting from others’ information. This paper studies a resource-efficient solution for this scenario based on vertical federated learning (VFL). Prior VFL frameworks are designed for situations where only one party holds the labels and would struggle to meet the demand of the targeted scenario, as storage resources usage would increase dramatically with the number of devices. Going beyond VFL, we design multivariate vertical federated learning (MVFL) as a novel federated learning framework, where we separate communication features and local features in an embedded feature space. This design enables MVFL to utilize storage and communication resources more efficiently by eliminating redundant models. On four real-world benchmarks, MVFL outperforms the VFL approach in both efficiency and accuracy, demonstrating its practical value for distributed IoT systems.

Modern telco networks have experienced a significant paradigm shift in the past decade, thanks to the proliferation of network softwarization. Despite the benefits of softwarized networks, the constituent software data planes cannot always guarantee predictable performance due to resource contentions in the underlying shared infrastructure. Performance predictions are thus paramount for network operators to fulfill Service-Level Agreements (SLAs), especially in high-speed regimes (e.g., Gigabit or Terabit Ethernet). Existing solutions heavily rely on in-band feature collection, which imposes non-trivial engineering and data-path overhead. This paper proposes a non-invasive performance prediction approach, which complements state-of-the-art solutions by measuring and analyzing low-level features ubiquitously available in the network infrastructure. Accessing these features does not hamper the packet data path. Our approach does not rely on prior knowledge of the input traffic, VNFs’ internals, and system details. We show that (i) low-level hardware features exposed by the NFV infrastructure can be collected and interpreted for performance issues, (ii) predictive models can be derived with classical ML algorithms, (iii) and can be used to predict performance impairments in real NFV systems accurately. Our code and datasets are publicly available1.

The last decade has witnessed the rise of Network Function Virtualization (NFV). Despite its benefits, resource allocation and traffic scheduling are still challenging. A practical issue is where to place Virtual Network Functions (VNFs) in the network to sustain long-term optimal objectives and when to reallocate resources given the dynamics of the substrate network. Most prior works either consider static settings or work in reactive fashions. This paper proposes a dual-window algorithm for proactive service redeployment and traffic routing. Specifically, our algorithm employs an entropy measure to gauge the uncertainty in the substrate network and cognitively updates the service redeployment interval to avoid unnecessary data collection overhead. Our algorithm is lightweight, intuitive, requires no offline training, and achieves the best overall effectiveness and efficiency compared with three state-of-the-art solutions.

Emerging applications and technologies such as vehicle-to-everything (V2X), edge-computing, and artificial intelligence have emphasized the demand for low-latency and deterministic communication. Although the 5G network has taken several efforts to fulfill this demand, such as with 5G-Time-Sensitive Networking (TSN) integration and DetNet, these efforts must be significantly expanded in 6G to fully achieve end-to-end deterministic communication. In this paper, we explore the problem of programmable deterministic communication in the new architecture of 6G. To deal with this problem, we rely on TSN, which has been proven to be a promising solution for deterministic communication. We take V2X as a use case, then investigate the two greatest challenges of this use case: low-latency communication and programmable network management for deterministic communication. To deal with these challenges, we introduce two solutions: (i) TSN low-latency scheduling supported by Multi-Agent Deep Reinforcement Learning, and (ii) programmable network management supported by SDN and joint cloud-infrastructure control. For each solution, detailed architecture and functionality design are presented. We show their high feasibility, applicability, and potentialities through comprehensive definitions, detailed explanations and in-depth qualitative analysis.

Processing packets in batches is a common technique in high-speed software routers to improve routing efficiency and increase throughput. With the growing popularity of novel paradigms such as Network Function Virtualization, advocating for the replacement of hardware-based networking modules towards software-based network functions deployed on commodity servers, we observe that batching techniques have been successfully implemented to reduce the HW/SW performance gap. As batch creation and management is at the very core of high-speed packet processors, it provides a significant impact to the overall packet processing capabilities of the system, affecting latency, throughput, CPU utilization and power consumption. It is commonly accepted to adopt a fixed maximum batching size (usually in the range between 32 and 512) to optimize for the worst case scenario (i.e. minimum-size packets at full bandwidth capacity). Such approach may result in a loss of efficiency despite a 100% utilization of the CPU. In this work we explore the possibilities of enhancing the runtime batch creation in VPP, a popular software router based on the Intel DPDK framework. Instead of relying on the automatic batch creation, we apply machine learning techniques to optimize the batching size for lower CPU-time and higher power efficiency in average scenarios, while maintaining its high performance in the worst case.

Due to the intrinsically inefficient service provisioning in traditional networks, Network Function Virtualization (NFV) keeps gaining attention from both industry and academia. By replacing the purpose-built, expensive, proprietary network equipment with software network functions consolidated on commodity hardware, NFV envisions a shift towards a more agile and open service provisioning paradigm. During the last few years, a large number of NFV platforms have been implemented in production environments that typically face critical challenges, including the development, deployment, and management of Virtual Network Functions (VNFs). Nonetheless, just like any complex system, such platforms commonly consist of abounding software and hardware components and usually incorporate disparate design choices based on distinct motivations or use cases. This broad collection of convoluted alternatives makes it extremely arduous for network operators to make proper choices. Although numerous efforts have been devoted to investigating different aspects of NFV, none of them specifically focused on NFV platforms or attempted to explore their design space. In this paper, we present a comprehensive survey on the NFV platform design. Our study solely targets existing NFV platform implementations. We begin with a top-down architectural view of the standard reference NFV platform and present our taxonomy of existing NFV platforms based on what features they provide in terms of a typical network function life cycle. Then we thoroughly explore the design space and elaborate on the implementation choices each platform opts for. We also envision future challenges for NFV platform design in the incoming 5G era. We believe that our study gives a detailed guideline for network operators or service providers to choose the most appropriate NFV platform based on their respective requirements. Our work also provides guidelines for implementing new NFV platforms.

Software switches are increasingly used in network function virtualization (NFV) to route traffic between virtualized network functions (VNFs) and physical network interface cards (NICs). Understanding of alternative switch designs remains deficient, however, in the absence of a comprehensive, comparative performance analysis. In this paper, we propose a methodology intended to be fair and use it to compare the performance of seven state-of-the-art software switches. We first explore their respective design spaces and then compare their performance under four representative test scenarios. Each scenario corresponds to a specific case of routing NFV traffic between NICs and/or VNFs. Our experimental results show that no single software switch prevails in all scenarios. It is therefore important to choose the one that is best adapted to a given use-case. The presented results and analysis bring a better understanding of design tradeoffs and identify potential bottlenecks that limit the performance of software switches.

Network Functions Virtualization (NFV) is among the latest network revolutions, bringing flexibility and avoiding network ossification. At the same time, all-software NFV implementations on commodity hardware raise performance issues with respect to ASIC solutions. To address these issues, numerous software acceleration frameworks for packet processing have appeared in the last few years. Common among these frameworks is the use of batching techniques. In this context, packets are processed in groups as opposed to individually, which is required at high-speed to minimize the framework overhead, reduce interrupt pressure, and leverage instruction-level cache hits. Whereas several system implementations have been proposed and experimentally benchmarked, the scientific community has so far only to a limited extent attempted to model the system dynamics of modern NFV routers exploiting batching acceleration. In this paper, we fill this gap by proposing a simple generic model for such batching-based mechanisms, which allows a very detailed prediction of highly relevant performance indicators. These include the distribution of the processed batch size as well as queue size, which can be used to identify loss-less operational regimes or quantify the packet loss probability in high-load scenarios. We contrast the model prediction with experimental results gathered in a high-speed testbed including an NFV router, showing that the model not only correctly captures system performance under simple conditions, but also in more realistic scenarios in which traffic is processed by a mixture of functions.

Interest in software networking has grown significantly since the introduction of Network Function Virtualization (NFV). Software switches are used in NFV to steer traffic between different virtualized network functions and physical Network Interface Cards (NICs). It is becoming more and more important to objectively evaluate and compare the performance of the multiple alternative implementations that have recently been proposed. A comprehensive performance analysis is still missing for two main reasons: (i) the amount of time required to configure and compare all such tools is enormous; (ii) it is very difficult to define a proper methodology to compare different solutions in a fair manner. In this paper we propose a methodology based on four simple yet representative test scenarios used to evaluate the performance of software switches. We apply this methodology to measure throughput and latency metrics for 6 state-of-the-art software switches namely, OVS-DPDK, snabb, BESS, FastClick, VPP and netmap VALE. Our work constitutes a first step to building a better understanding of design tradeoffs and identifying performance bottlenecks.

The ongoing network softwarization trend holds the promise to revolutionize network infrastructures by making them more flexible, reconfigurable, portable, and more adaptive than ever. Still, the migration from hard-coded/hard-wired network functions toward their software-programmable counterparts comes along with the need for tailored optimizations and acceleration techniques so as to avoid or at least mitigate the throughput/latency performance degradation with respect to fixed function network elements. The contribution of this paper is twofold. First, we provide a comprehensive overview of the host-based network function virtualization (NFV) ecosystem, covering a broad range of techniques, from low-level hardware acceleration and bump-in-the-wire offloading approaches to high-level software acceleration solutions, including the virtualization technique itself. Second, we derive guidelines regarding the design, development, and operation of NFV-based deployments that meet the flexibility and scalability requirements of modern communication networks.

In the last few years, several software-based solutions have been proved to be very efficient for high-speed packet processing, traffic generation, and monitoring, and can be considered valid alternatives to expensive and non-flexible hardware-based solutions. In this paper, we first benchmark heterogeneous design choices for software-based packet monitoring systems in terms of achievable performance and required resources (i.e., the number of CPU cores). Building on this extensive analysis we design FloWatcher-DPDK, a DPDK-based high-speed software traffic monitor we provide to the community as an open source project. In a nutshell, FloWatcher-DPDK provides tunable fine-grained statistics at packet and flow levels. Experimental results demonstrate that FloWatcher-DPDK sustains per-flow statistics with 5-nines precision at high-speed (e.g., 14.88 Mpps) using a limited amount of resources. Finally, we showcase the usage of FloWatcher-DPDK by configuring it to analyze the performance of two open source prototypes for stateful flow-level end-host and in-network packet processing.

Packet classification is an important part of many networking devices, such as routers and firewalls. Software-defined networking (SDN) heavily relies on online packet classification which must efficiently process two different streams: incoming packets to classify and rules to update. This rules out many offline packet classification algorithms that do not support fast updates. We propose a novel online classification algorithm, TupleMerge (TM), derived from tuple space search (TSS), the packet classifier used by Open vSwitch (OVS). TM improves upon TSS by combining hash tables which contain rules with similar characteristics. This greatly reduces classification time preserving similar performance in updates. We validate the effectiveness of TM using both simulation and deployment in a full-fledged software router, specifically within the vector packet processor (VPP). In our simulation results, which focus solely on the efficiency of the classification algorithm, we demonstrate that TM outperforms all other state of the art methods, including TSS, PartitionSort (PS), and SAX-PAC. For example, TM is 34% faster at classifying packets and 30% faster at updating rules than PS. We then experimentally evaluate TM deployed within the VPP framework comparing TM against linear search and TSS, and also against TSS within the OVS framework. This validation of deployed implementations is important as SDN frameworks have several optimizations such as caches that may minimize the influence of a classification algorithm. Our experimental results clearly validate the effectiveness of TM. VPP TM classifies packets nearly two orders of magnitude faster than VPP TSS and at least one order of magnitude faster than OVS TSS.

Internet users are interested in content regardless of its location; however, the current client/server architecture still requires requests to be directed to a specific server. Information-centric networking (ICN) is a recent vein that relaxes this requirement through the use of name-based forwarding, where forwarding decisions are based on content names instead of IP addresses. Despite previous name-based forwarding strategies have been proposed, almost none have actually built a content router. To fill this gap, in this paper we design and prototype a content router called Caesar for high-speed forwarding on content names. Caesar introduces several innovative features, including (i) a longest-prefix matching algorithm based on a novel data structure called prefix Bloom filter; (ii) an incremental design which allows for easy integration with existing protocols and network equipment;(iii) a forwarding scheme where multiple line cards collaborate in a distributed fashion; and (iv) support for offloading packet processing to graphics processing units (GPUs). We build Caesar as an enterprise router, and show that every line card sustains up to 10 Gbps using a forwarding table with more than 10 million content prefixes. Distributed forwarding allows the forwarding table to grow even further, and to scale linearly with the number of line cards at the cost of only a few microseconds in the packet processing latency. GPU offloading, in turn, trades off a few milliseconds of latency for a large speedup in the forwarding rate.