VNF performance modelling : from stand-alone to chained topologies

One of the main incentives for deploying network functions on a virtualized or cloud-based infrastructure, is the ability for on-demand orchestration and elastic resource scaling following the workload demand. This can also be combined with a multi-party service creation cycle: the service provider sources various network functions from different vendors or developers, and combines them into a modular network service. This way, multiple virtual network functions (VNFs) are connected into more complex topologies called service chains. Deployment speed is important here, and it is therefore beneficial if the service provider can limit extra validation testing of the combined service chain, and rely on the provided profiling results of the supplied single VNFs. Our research shows that it is however not always evident to accurately predict the performance of a total service chain, from the isolated benchmark or profiling tests of its discrete network functions. To mitigate this, we propose a two-step deployment workflow: First, a general trend estimation for the chain performance is derived from the stand-alone VNF profiling results, together with an initial resource allocation. This information then optimizes the second phase, where online monitored data of the service chain is used to quickly adjust the estimated performance model where needed. Our tests show that this can lead to a more efficient VNF chain deployment, needing less scaling iterations to meet the chain performance specification, while avoiding the need for a complete proactive and time-consuming VNF chain validation

High performance network function virtualization for user-oriented services

The Network Function Virtualization (NFV) paradigm proposes to transform those network functions today running on dedicated and often closed appliances (e.g., firewall, wan accelerator) into pure software images, called Virtual Network Functions (VNFs), which can be consolidated and executed on high-volume standard servers. In this context, this dissertation focuses on the possibility of enabling each single end user (and not only network operators) to set up network services by means of NFV, allowing him to custoimize the set of services that are active on his Internet connection. This goal mainly requires to address flexibility and performance issues. Regarding to the former, it is important: (i) to support services including both network (e.g., firewall) and cloud (e.g., storage server) applications; (ii) to allow the user to define the service with an intuitive and high-level abstraction, hiding infrastructure-layer details. Instead, with respect to performance, multiple software-based services operating on the user's traffic should not introduce penalties in the user’s Internet experience. This dissertation solves the above issues by proposing a number of improvements in the context of Network Function Virtualization, both in terms of high level models and architectures to define and instantiate network services, and in terms of mechanisms to efficiently interconnect VNFs. Experimental results demonstrate that the goal of allowing end users to deploy services operating on their own traffic is feasible without impacting the Internet experience

A Framework for eBPF-Based Network Functions in an Era of Microservices

By moving network functionality from dedicated hardware to software running on end-hosts, Network Functions Virtualization (NFV) pledges the benefits of cloud computing to packet processing. While most of the NFV frameworks today rely on kernel-bypass approaches, no attention has been given to kernel packet processing, which has always proved hard to evolve and to program. In this article, we present Polycube, a software framework whose main goal is to bring the power of NFV to in-kernel packet processing applications, enabling a level of flexibility and customization that was unthinkable before. Polycube enables the creation of arbitrary and complex network function chains, where each function can include an efficient in-kernel data plane and a flexible user-space control plane with strong characteristics of isolation, persistence, and composability. Polycube network functions, called Cubes, can be dynamically generated and injected into the kernel networking stack, without requiring custom kernels or specific kernel modules, simplifying the debugging and introspection, which are two fundamental properties in recent cloud environments. We validate the framework by showing significant improvements over existing applications, and we prove the generality of the Polycube programming model through the implementation of complex use cases such as a network provider for Kubernetes

Improving intrusion detection systems using data mining techniques

Recent surveys and studies have shown that cyber-attacks have caused a lot of damage to organisations, governments, and individuals around the world. Although developments are constantly occurring in the computer security field, cyber-attacks still cause damage as they are developed and evolved by hackers. This research looked at some industrial challenges in the intrusion detection area. The research identified two main challenges; the first one is that signature-based intrusion detection systems such as SNORT lack the capability of detecting attacks with new signatures without human intervention. The other challenge is related to multi-stage attack detection, it has been found that signature-based is not efficient in this area. The novelty in this research is presented through developing methodologies tackling the mentioned challenges. The first challenge was handled by developing a multi-layer classification methodology. The first layer is based on decision tree, while the second layer is a hybrid module that uses two data mining techniques; neural network, and fuzzy logic. The second layer will try to detect new attacks in case the first one fails to detect. This system detects attacks with new signatures, and then updates the SNORT signature holder automatically, without any human intervention. The obtained results have shown that a high detection rate has been obtained with attacks having new signatures. However, it has been found that the false positive rate needs to be lowered. The second challenge was approached by evaluating IP information using fuzzy logic. This approach looks at the identity of participants in the traffic, rather than the sequence and contents of the traffic. The results have shown that this approach can help in predicting attacks at very early stages in some scenarios. However, it has been found that combining this approach with a different approach that looks at the sequence and contents of the traffic, such as event- correlation, will achieve a better performance than each approach individually