Ultra-reliable Low-latency, Energy-efficient and Computing-centric Software Data Plane for Network Softwarization

Network softwarization plays a significantly important role in the development and deployment of the latest communication system for 5G and beyond. A more flexible and intelligent network architecture can be enabled to provide support for agile network management, rapid launch of innovative network services with much reduction in Capital Expense (CAPEX) and Operating Expense (OPEX). Despite these benefits, 5G system also raises unprecedented challenges as emerging machine-to-machine and human-to-machine communication use cases require Ultra-Reliable Low Latency Communication (URLLC). According to empirical measurements performed by the author of this dissertation on a practical testbed, State of the Art (STOA) technologies and systems are not able to achieve the one millisecond end-to-end latency requirement of the 5G standard on Commercial Off-The-Shelf (COTS) servers. This dissertation performs a comprehensive introduction to three innovative approaches that can be used to improve different aspects of the current software-driven network data plane. All three approaches are carefully designed, professionally implemented and rigorously evaluated. According to the measurement results, these novel approaches put forward the research in the design and implementation of ultra-reliable low-latency, energy-efficient and computing-first software data plane for 5G communication system and beyond

iOn-Profiler: intelligent Online multi-objective VNF Profiling with Reinforcement Learning

Leveraging the potential of Virtualised Network Functions (VNFs) requires a clear understanding of the link between resource consumption and performance. The current state of the art tries to do that by utilising Machine Learning (ML) and specifically Supervised Learning (SL) models for given network environments and VNF types assuming single-objective optimisation targets. Taking a different approach poses a novel VNF profiler optimising multi-resource type allocation and performance objectives using adapted Reinforcement Learning (RL). Our approach can meet Key Performance Indicator (KPI) targets while minimising multi-resource type consumption and optimising the VNF output rate compared to existing single-objective solutions. Our experimental evaluation with three real-world VNF types over a total of 39 study scenarios (13 per VNF), for three resource types (virtual CPU, memory, and network link capacity), verifies the accuracy of resource allocation predictions and corresponding successful profiling decisions via a benchmark comparison between our RL model and SL models. We also conduct a complementary exhaustive search-space study revealing that different resources impact performance in varying ways per VNF type, implying the necessity of multi-objective optimisation, individualised examination per VNF type, and adaptable online profile learning, such as with the autonomous online learning approach of iOn-Profiler.Comment: 22 pages, 12 figures, 8 tables, journal article pre-print versio

Machine learning for Quality of Experience in real-time applications

L'abstract è presente nell'allegato / the abstract is in the attachmen

Analyzing challenging aspects of IPv6 over IPv4

The exponential expansion of the Internet has exhausted the IPv4 addresses provided by IANA. The new IP edition, i.e. IPv6 introduced by IETF with new features such as a simplified packet header, a greater address space, a different address sort, improved encryption, powerful section routing, and stronger QoS. ISPs are slowly seeking to migrate from current IPv4 physical networks to new generation IPv6 networks. ‎The move from actual IPv4 to software-based IPv6 is very sluggish, since billions of computers across the globe use IPv4 addresses. The configuration and actions of IP4 and IPv6 protocols are distinct. Direct correspondence between IPv4 and IPv6 is also not feasible. In terms of the incompatibility problems, all protocols can co-exist throughout the transformation for a few years. Compatibility, interoperability, and stability are key concerns between IP4 and IPv6 protocols. After the conversion of the network through an IPv6, the move causes several issues for ISPs. The key challenges faced by ISPs are packet traversing, routing scalability, performance reliability, and protection. Within this study, we meticulously analyzed a detailed overview of all aforementioned issues during switching into ipv6 network

Rethinking Routing and Peering in the era of Vertical Integration of Network Functions

Content providers typically control the digital content consumption services and are getting the most revenue by implementing an all-you-can-eat model via subscription or hyper-targeted advertisements. Revamping the existing Internet architecture and design, a vertical integration where a content provider and access ISP will act as unibody in a sugarcane form seems to be the recent trend. As this vertical integration trend is emerging in the ISP market, it is questionable if existing routing architecture will suffice in terms of sustainable economics, peering, and scalability. It is expected that the current routing will need careful modifications and smart innovations to ensure effective and reliable end-to-end packet delivery. This involves new feature developments for handling traffic with reduced latency to tackle routing scalability issues in a more secure way and to offer new services at cheaper costs. Considering the fact that prices of DRAM or TCAM in legacy routers are not necessarily decreasing at the desired pace, cloud computing can be a great solution to manage the increasing computation and memory complexity of routing functions in a centralized manner with optimized expenses. Focusing on the attributes associated with existing routing cost models and by exploring a hybrid approach to SDN, we also compare recent trends in cloud pricing (for both storage and service) to evaluate whether it would be economically beneficial to integrate cloud services with legacy routing for improved cost-efficiency. In terms of peering, using the US as a case study, we show the overlaps between access ISPs and content providers to explore the viability of a future in terms of peering between the new emerging content-dominated sugarcane ISPs and the healthiness of Internet economics. To this end, we introduce meta-peering, a term that encompasses automation efforts related to peering – from identifying a list of ISPs likely to peer, to injecting control-plane rules, to continuous monitoring and notifying any violation – one of the many outcroppings of vertical integration procedure which could be offered to the ISPs as a standalone service

Toward a Real-Time TCP SYN Flood DDoS Mitigation Using Adaptive Neuro-Fuzzy Classifier and SDN Assistance in Fog Computing

The growth of the Internet of Things (IoT) has recently impacted our daily lives in many ways. As a result, a massive volume of data are generated and need to be processed in a short period of time. Therefore, a combination of computing models such as cloud computing is necessary. The main disadvantage of the cloud platform is its high latency due to the centralized mainframe. Fortunately, a distributed paradigm known as fog computing has emerged to overcome this problem, offering cloud services with low latency and high-access bandwidth to support many IoT application scenarios. However, attacks against fog servers can take many forms, such as distributed denial of service (DDoS) attacks that severely affect the reliability and availability of fog services. To address these challenges, we propose mitigation of fog computing-based SYN Flood DDoS attacks using an adaptive neuro-fuzzy inference system (ANFIS) and software defined networking (SDN) assistance (FASA). The simulation results show that the FASA system outperforms other algorithms in terms of accuracy, precision, recall, and F1-score. This shows how crucial our system is for detecting and mitigating TCP-SYN floods and DDoS attacks

Scalable Emulator for Software Defined Networks

Since its inception, Software Defined Network (SDN) has made itself a very appealing architecture for both Data Center and Wide Area networks by offering more automated control through programmability and simplified network operations and management with its centralized control plane. However, extensive rollout of SDN in the production environment requires thorough validation. Thus, there is a compelling need for SDN emulators that facilitate experimenting with new SDN-based technologies (e.g., SDN-based routing and traffic engineering schemes). Valuable insights on these technologies can be gained from real trace-driven experiments on an emulator platform. Accordingly, network operators can gain confidence in these technologies without jeopardizing their infrastructures and businesses. Mininet, the de facto standard SDN emulator, allows users to emulate an OpenFlow-based SDN on a single server. Due to the physical resource limitations of a single machine, Mininet fails to scale with large network size and high traffic volume. To address these limitations, we developed Distributed OpenFlow Testbed (DOT), a highly scalable emulator for SDN. DOT distributes the emulated network across multiple physical machines to scale with large network sizes and high traffic volumes. It also provides guaranteed compute and network resources for the emulated components (i.e., switches, hosts and links). Moreover, DOT can emulate a wider range of network services compared to other publicly available SDN emulators and simulators