BPFabric: Data Plane Programmability for Software Defined Networks

In its current form, OpenFlow, the de facto implementation of SDN, separates the network’s control and data planes allowing a central controller to alter the matchaction pipeline using a limited set of fields and actions. To support new protocols, forwarding logic, telemetry, monitoring or even middlebox-like functions the currently available programmability in SDN is insufficient. In this paper, we introduce BPFabric, a platform, protocol, and language-independent architecture to centrally program and monitor the data plane. BPFabric leverages eBPF, a platform and protocol independent instruction set to define the packet processing and forwarding functionality of the data plane. We introduce a control plane API that allows data plane functions to be deployed onthe-fly, reporting events of interest and exposing network internal state. We present a raw socket and DPDK implementation of the design, the former for large-scale experimentation using environment such as Mininet and the latter for high-performance low-latency deployments. We show through examples that functions unrealisable in OpenFlow can leverage this flexibility while achieving similar or better performance to today’s static design

GNFC: Towards Network Function Cloudification

An increasing demand is seen from enterprises to host and dynamically manage middlebox services in public clouds in order to leverage the same benefits that network functions provide in traditional, in-house deployments. However, today's public clouds provide only a limited view and programmability for tenants that challenges flexible deployment of transparent, software-defined network functions. Moreover, current virtual network functions can't take full advantage of a virtualized cloud environment, limiting scalability and fault tolerance. In this paper we review and evaluate the current infrastructural limitations imposed by public cloud providers and present the design and implementation of GNFC, a cloud-based Network Function Virtualization (NFV) framework that gives tenants the ability to transparently attach stateless, container-based network functions to their services hosted in public clouds. We evaluate the proposed system over three public cloud providers (Amazon EC2, Microsoft Azure and Google Compute Engine) and show the effects on end-to-end latency and throughput using various instance types for NFV hosts

Roaming Edge vNFs using Glasgow Network Functions

While the network edge is becoming more important for the provision of customized services in next generation mobile networks, current NFV architectures are unsuitable to meet the increasing future demand. They rely on commodity servers with resource-hungry Virtual Machines that are unable to provide the high network function density and mobility requirements necessary for upcoming wide-area and 5G networks. In this demo, we showcase Glasgow Network Functions (GNF), a virtualization framework suitable for next generation mobile networks that exploits lightweight network functions (NFs) deployed at the edge and transparently following users' devices as they roam between cells

Arbitrary Packet Matching in OpenFlow

OpenFlow has emerged as the de facto control protocol to implement Software-Defined Networking (SDN). In its current form, the protocol specifies a set of fields on which it matches packets to perform actions, such as forwarding, discarding or modifying specific protocol header fields at a switch. The number of match fields has increased with every version of the protocol to extend matching capabilities, however, it is still not flexible enough to match on arbitrary packet fields which limits innovation and new protocol development with OpenFlow. In this paper, we argue that a fully flexible match structure is superior to continuously extending the number of fields to match upon. We use Berkeley Packet Filters (BPF) for packet classification to provide a protocol-independent, flexible alternative to today’s OpenFlow fixed match fields. We have implemented a prototype system and evaluated the performance of the proposed match scheme, with a focus on the time it takes to execute and the memory required to store different match filter specifications. Our prototype implementation demonstrates that line-rate arbitrary packet classification can be achieved with complex BPF programs

Distributed Network Anomaly Detection on an Event Processing Framework

Network Intrusion Detection Systems (NIDS) are an integral part of modern data centres to ensure high availability and compliance with Service Level Agreements (SLAs). Currently, NIDS are deployed on high-performance, high-cost middleboxes that are responsible for monitoring a limited section of the network. The fast increasing size and aggregate throughput of modern data centre networks have come to challenge the current approach to anomaly detection to satisfy the fast growing compute demand. In this paper, we propose a novel approach to distributed intrusion detection systems based on the architecture of recently proposed event processing frameworks. We have designed and implemented a prototype system using Apache Storm to show the benefits of the proposed approach as well as the architectural differences with traditional systems. Our system distributes modules across the available devices within the network fabric and uses a centralised controller for orchestration, management and correlation. Following the Software Defined Networking (SDN) paradigm, the controller maintains a complete view of the network but distributes the processing logic for quick event processing while performing complex event correlation centrally. We have evaluated the proposed system using publicly available data centre traces and demonstrated that the system can scale with the network topology while providing high performance and minimal impact on packet latency

Container-based network function virtualization for software-defined networks

Today's enterprise networks almost ubiquitously deploy middlebox services to improve in-network security and performance. Although virtualization of middleboxes attracts a significant attention, studies show that such implementations are still proprietary and deployed in a static manner at the boundaries of organisations, hindering open innovation. In this paper, we present an open framework to create, deploy and manage virtual network functions (NF)s in OpenFlow-enabled networks. We exploit container-based NFs to achieve low performance overhead, fast deployment and high reusability missing from today's NFV deployments. Through an SDN northbound API, NFs can be instantiated, traffic can be steered through the desired policy chain and applications can raise notifications. We demonstrate the systems operation through the development of exemplar NFs from common Operating System utility binaries, and we show that container-based NFV improves function instantiation time by up to 68% over existing hypervisor-based alternatives, and scales to one hundred co-located NFs while incurring sub-millisecond latency

Enhancing programmability for adaptive resource management in next generation data centre networks

Recently, Data Centre (DC) infrastructures have been growing rapidly to support a wide range of emerging services, and provide the underlying connectivity and compute resources that facilitate the "*-as-a-Service" model. This has led to the deployment of a multitude of services multiplexed over few, very large-scale centralised infrastructures. In order to cope with the ebb and flow of users, services and traffic, infrastructures have been provisioned for peak-demand resulting in the average utilisation of resources to be low. This overprovisionning has been further motivated by the complexity in predicting traffic demands over diverse timescales and the stringent economic impact of outages. At the same time, the emergence of Software Defined Networking (SDN), is offering new means to monitor and manage the network infrastructure to address this underutilisation. This dissertation aims to show how measurement-based resource management can improve performance and resource utilisation by adaptively tuning the infrastructure to the changing operating conditions. To achieve this dynamicity, the infrastructure must be able to centrally monitor, notify and react based on the current operating state, from per-packet dynamics to longstanding traffic trends and topological changes. However, the management and orchestration abilities of current SDN realisations is too limiting and must evolve for next generation networks. The current focus has been on logically centralising the routing and forwarding decisions. However, in order to achieve the necessary fine-grained insight, the data plane of the individual device must be programmable to collect and disseminate the metrics of interest. The results of this work demonstrates that a logically centralised controller can dynamically collect and measure network operating metrics to subsequently compute and disseminate fine-tuned environment-specific settings. They show how this approach can prevent TCP throughput incast collapse and improve TCP performance by an order of magnitude for partition-aggregate traffic patterns. Futhermore, the paradigm is generalised to show the benefits for other services widely used in DCs such as, e.g, routing, telemetry, and security

Distributed, multi-level network anomaly detection for datacentre networks

Over the past decade, numerous systems have been proposed to detect and subsequently prevent or mitigate security vulnerabilities. However, many existing intrusion or anomaly detection solutions are limited to a subset of the traffic due to scalability issues, hence failing to operate at line-rate on large, high-speed datacentre networks. In this paper, we present a two-level solution for anomaly detection leveraging independent execution and message passing semantics. We employ these constructs within a network-wide distributed anomaly detection framework that allows for greater detection accuracy and bandwidth cost saving through attack path reconstruction. Experimental results using real operational traffic traces and known network attacks generated through the Pytbull IDS evaluation framework, show that our approach is capable of detecting anomalies in a timely manner while allowing reconstruction of the attack path, hence further enabling the composition of advanced mitigation strategies. The resulting system shows high detection accuracy when compared to similar techniques, at least 20% better at detecting anomalies, and enables full path reconstruction even at small-to-moderate attack traffic intensities (as a fraction of the total traffic), saving up to 75% of bandwidth due to early attack detection

An Inter-domain Collaboration Scheme to Remedy DDoS Attacks in Computer Networks

Distributed Denial-of-Service (DDoS) attacks continue to trouble network operators and service providers, and with increasing intensity. Effective response to DDoS can be slow (because of manual diagnosis and interaction) and potentially self-defeating (as indiscriminate filtering accomplishes a likely goal of the attacker), and this is the result of the discrepancy between the service provider's flow-based, application-level view of traffic and the network operator's packet-based, network-level view and limited functionality. Furthermore, a network required to take action may be in an Autonomous System (AS) several AS-hops away from the service, so it has no direct relationship with the service on whose behalf it acts. This paper presents Antidose, a means of interaction between a vulnerable peripheral service and an indirectly related AS that allows the AS to confidently deploy local filtering with discrimination under the control of the remote service. We implement the core filtering mechanism of Antidose, and provide an analysis of it to demonstrate that conscious attacks against the mechanism will not expose the AS to additional attacks. We present a performance evaluation to show that the mechanism is operationally feasible in the emerging trend of operators' willingness to increase the programmability of their hardware with SDN technologies such as OpenFlow, as well as to act to mitigate attacks on downstream customers

Technology-related disasters:a survey towards disaster-resilient software defined networks

Resilience against disaster scenarios is essential to network operators, not only because of the potential economic impact of a disaster but also because communication networks form the basis of crisis management. COST RECODIS aims at studying measures, rules, techniques and prediction mechanisms for different disaster scenarios. This paper gives an overview of different solutions in the context of technology-related disasters. After a general overview, the paper focuses on resilient Software Defined Networks