New concepts for traffic, resource and mobility management in software-defined mobile networks

The evolution of mobile telecommunication networks is accompanied by new demands for the performance, portability, elasticity, and energy efficiency of network functions. Network Function Virtualization (NFV), Software Defined Networking (SDN), and cloud service technologies are claimed to be able to provide most of the capabilities. However, great leap forward will only be achieved if resource, traffic, and mobility management methods of mobile network services can efficiently utilize these technologies. This paper conceptualizes the future requirements of mobile networks and proposes new concepts and solutions in the form of Software-Defined Mobile Networks (SDMN) leveraging SDN, NFV and cloud technologies. We evaluate the proposed solutions through testbed implementations and simulations. The results reveal that our proposed SDMN enhancements supports heterogeneity in wireless networks with performance improvements through programmable interfaces and centralized control

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

Journal of Telecommunications and Information Technology, 2018, nr 1

kwartalni

FatPaths: Routing in Supercomputers and Data Centers when Shortest Paths Fall Short

We introduce FatPaths: a simple, generic, and robust routing architecture that enables state-of-the-art low-diameter topologies such as Slim Fly to achieve unprecedented performance. FatPaths targets Ethernet stacks in both HPC supercomputers as well as cloud data centers and clusters. FatPaths exposes and exploits the rich ("fat") diversity of both minimal and non-minimal paths for high-performance multi-pathing. Moreover, FatPaths uses a redesigned "purified" transport layer that removes virtually all TCP performance issues (e.g., the slow start), and incorporates flowlet switching, a technique used to prevent packet reordering in TCP networks, to enable very simple and effective load balancing. Our design enables recent low-diameter topologies to outperform powerful Clos designs, achieving 15% higher net throughput at 2x lower latency for comparable cost. FatPaths will significantly accelerate Ethernet clusters that form more than 50% of the Top500 list and it may become a standard routing scheme for modern topologies

A quantitative survey of the power saving potential in IP-Over-WDM backbone networks

The power consumption in Information and Communication Technologies networks is growing year by year; this growth presents challenges from technical, economic, and environmental points of view. This has lead to a great number of research publications on "green" telecommunication networks. In response, a number of survey works have appeared as well. However, with respect to backbone networks, most survey works: 1) do not allow for an easy cross validation of the savings reported in the various works and 2) nor do they provide a clear overview of the individual and combined power saving potentials. Therefore, in this paper, we survey the reported saving potential in IP-over-WDM backbone telecommunication networks across the existing body of research in that area. We do this by mapping more than ten different approaches to a concise analytical model, which allows us to estimate the combined power reduction potential. Our estimates indicate that the power reduction potential of the once-only approaches is 2.3x in a Moderate Effort scenario and 31x in a Best Effort scenario. Factoring in the historic and projected yearly efficiency improvements ("Moore's law") roughly doubles both values on a ten-year horizon. The large difference between the outcome of Moderate Effort and Best Effort scenarios is explained by the disparity and lack of clarity of the reported saving results and by our (partly) subjective assessment of the feasibility of the proposed approaches. The Moderate Effort scenario will not be sufficient to counter the projected traffic growth, although the Best Effort scenario indicates that sufficient potential is likely available. The largest isolated power reduction potential is available in improving the power associated with cooling and power provisioning and applying sleep modes to overdimensioned equipment

The Strict-Sense Nonblocking Multirate l

This paper considers the nonblocking conditions for a multirate logd(N,0,p) switching network at the connection level. The necessary and sufficient conditions for the discrete bandwidth model, as well as sufficient and, in particular cases, also necessary conditions for the continuous bandwidth model, were given. The results given for dn-1/2f0≥f1+1 in the discrete bandwidth model are the same as those proposed by Hwang et al. (2005); however, in this paper, these results were extended to other values of f0, f1, and d. In the continuous bandwidth model for B+b>1, the results given in this paper are also the same as those by Hwang et al. (2005); however, for B+b≤1, it was proved that a smaller number of vertically stacked logdN switching networks are needed

Rethinking Software Network Data Planes in the Era of Microservices

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

Online learning on the programmable dataplane

This thesis makes the case for managing computer networks with datadriven methods automated statistical inference and control based on measurement data and runtime observations—and argues for their tight integration with programmable dataplane hardware to make management decisions faster and from more precise data. Optimisation, defence, and measurement of networked infrastructure are each challenging tasks in their own right, which are currently dominated by the use of hand-crafted heuristic methods. These become harder to reason about and deploy as networks scale in rates and number of forwarding elements, but their design requires expert knowledge and care around unexpected protocol interactions. This makes tailored, per-deployment or -workload solutions infeasible to develop. Recent advances in machine learning offer capable function approximation and closed-loop control which suit many of these tasks. New, programmable dataplane hardware enables more agility in the network— runtime reprogrammability, precise traffic measurement, and low latency on-path processing. The synthesis of these two developments allows complex decisions to be made on previously unusable state, and made quicker by offloading inference to the network. To justify this argument, I advance the state of the art in data-driven defence of networks, novel dataplane-friendly online reinforcement learning algorithms, and in-network data reduction to allow classification of switchscale data. Each requires co-design aware of the network, and of the failure modes of systems and carried traffic. To make online learning possible in the dataplane, I use fixed-point arithmetic and modify classical (non-neural) approaches to take advantage of the SmartNIC compute model and make use of rich device local state. I show that data-driven solutions still require great care to correctly design, but with the right domain expertise they can improve on pathological cases in DDoS defence, such as protecting legitimate UDP traffic. In-network aggregation to histograms is shown to enable accurate classification from fine temporal effects, and allows hosts to scale such classification to far larger flow counts and traffic volume. Moving reinforcement learning to the dataplane is shown to offer substantial benefits to stateaction latency and online learning throughput versus host machines; allowing policies to react faster to fine-grained network events. The dataplane environment is key in making reactive online learning feasible—to port further algorithms and learnt functions, I collate and analyse the strengths of current and future hardware designs, as well as individual algorithms