Scalability and Resilience Analysis of Software-Defined Networking

Software-defined Networking (SDN) ist eine moderne Architektur für Kommunikationsnetze, welche entwickelt wurde, um die Einführung von neuen Diensten und Funktionen in Netzwerke zu erleichtern. Durch eine Trennung der Weiterleitungs- und Kontrollfunktionen sind nur wenige Kontrollelemente mit Software-Updates zu versehen, um Veränderungen am Netz vornehmen zu können. Allerdings wirft die Netzstrukturierung von SDN neue Fragen bezüglich Skalierbarkeit und Ausfallsicherheit auf, welche in dezentralen Netzstrukturen nicht auftreten. In dieser Arbeit befassen wir uns mit Fragestellungen zu Skalierbarkeit und Ausfallsicherheit in Bezug auf Unicast- und Multicast-Verkehr in SDN-basierten Netzen. Wir führen eine Komprimierungstechnik für Routingtabellen ein, welche die Skalierungsproblematik aktueller SDN Weiterleitungsgeräte verbessern soll und ermitteln ihre Effizienz in einer Leistungsbewertung. Außerdem diskutieren wir unterschiedliche Methoden, um die Ausfallsicherheit in SDN zu verbessern. Wir analysieren sie auf öffentlich zugänglichen Netzwerken und benennen Vor- und Nachteile der Ansätze. Abschließend schlagen wir eine skalierbare und ausfallsichere Architektur für Multicast-basiertes SDN vor. Wir untersuchen ihre Effizienz in einer Leistungsbewertung und zeigen ihre Umsetzbarkeit mithilfe eines Prototypen.Software-Defined Networking (SDN) is a novel architecture for communication networks that has been developed to ease the introduction of new network services and functions. It leverages the separation of the data plane and the control plane to allow network services to be deployed solely in software. Although SDN provides great flexibility, the applicability of SDN in communication networks raises several questions with regard to scalability and resilience against network failures. These concerns are not prevalent in current decentralized network architectures. In this thesis, we address scalability and resilience issues with regard to unicast and multicast traffic for SDN-based networks. We propose a new compression method for inter-domain routing tables to address hardware limitations of current SDN switches and analyze its effectiveness. We propose various resilience methods for SDN and identify their key performance indicators in the context of carrier-grade and datacenter networks. We discuss the advantages and disadvantages of these proposals and their appropriate use cases. Finally, we propose a scalable and resilient software-defined multicast architecture. We study the effectiveness of our approach and show its feasibility using a prototype implementation

Software-Driven and Virtualized Architectures for Scalable 5G Networks

In this dissertation, we argue that it is essential to rearchitect 4G cellular core networks–sitting between the Internet and the radio access network–to meet the scalability, performance, and flexibility requirements of 5G networks. Today, there is a growing consensus among operators and research community that software-defined networking (SDN), network function virtualization (NFV), and mobile edge computing (MEC) paradigms will be the key ingredients of the next-generation cellular networks. Motivated by these trends, we design and optimize three core network architectures, SoftMoW, SoftBox, and SkyCore, for different network scales, objectives, and conditions. SoftMoW provides global control over nationwide core networks with the ultimate goal of enabling new routing and mobility optimizations. SoftBox attempts to enhance policy enforcement in statewide core networks to enable low-latency, signaling-efficient, and customized services for mobile devices. Sky- Core is aimed at realizing a compact core network for citywide UAV-based radio networks that are going to serve first responders in the future. Network slicing techniques make it possible to deploy these solutions on the same infrastructure in parallel. To better support mobility and provide verifiable security, these architectures can use an addressing scheme that separates network locations and identities with self-certifying, flat and non-aggregatable address components. To benefit the proposed architectures, we designed a high-speed and memory-efficient router, called Caesar, for this type of addressing schemePHDComputer Science & EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/146130/1/moradi_1.pd

Review of Path Selection Algorithms with Link Quality and Critical Switch Aware for Heterogeneous Traffic in SDN

Software Defined Networking (SDN) introduced network management flexibility that eludes traditional network architecture. Nevertheless, the pervasive demand for various cloud computing services with different levels of Quality of Service requirements in our contemporary world made network service provisioning challenging. One of these challenges is path selection (PS) for routing heterogeneous traffic with end-to-end quality of service support specific to each traffic class. The challenge had gotten the research community\u27s attention to the extent that many PSAs were proposed. However, a gap still exists that calls for further study. This paper reviews the existing PSA and the Baseline Shortest Path Algorithms (BSPA) upon which many relevant PSA(s) are built to help identify these gaps. The paper categorizes the PSAs into four, based on their path selection criteria, (1) PSAs that use static or dynamic link quality to guide PSD, (2) PSAs that consider the criticality of switch in terms of an update operation, FlowTable limitation or port capacity to guide PSD, (3) PSAs that consider flow variabilities to guide PSD and (4) The PSAs that use ML optimization in their PSD. We then reviewed and compared the techniques\u27 design in each category against the identified SDN PSA design objectives, solution approach, BSPA, and validation approaches. Finally, the paper recommends directions for further research

Energy-Aware Routing in Software-Defined Network using Compression

International audienceSoftware-defined Networks (SDN) is a new networking paradigm enabling innovation through network programmability. Over past few years, many applications have been built using SDN such as server load balancing, virtual-machine migration, trac engineering and access control. In this paper, we focus on using SDN for energy-aware routing (EAR). Since trac load has a small influence on the power consumption of routers, EAR allows putting unused links into sleep mode to save energy. SDN can collect trac matrix and then computes routing solutions satisfying QoS while being minimal in energy consumption. However, prior works on EAR have assumed that the SDN forwarding table switch can hold an infinite number of rules. In practice, this assumption does not hold since such flow tables are implemented in Ternary Content Addressable Memory (TCAM) which is expensive and power-hungry. We consider the use of wildcard rules to compress the forwarding tables. In this paper, we propose optimization methods to minimize energy consumption for a backbone network while respecting capacity constraints on links and rule space constraints on routers. In details, we present two exact formulations using Integer Linear Program (ILP) and introduce ecient heuristic algorithms. Based on simulations on realistic network topologies, we show that using this smart rule space allocation, it is possible to save almost as much power consumption as the classical EAR approach

Flow Delegation: Flow Table Capacity Bottleneck Mitigation for Software-defined Networks

This dissertation introduces flow delegation, a novel concept to deal with flow table capacity bottlenecks in Software-defined Networks (SDNs). Such bottlenecks occur when SDN switches provide insufficient flow table capacity which can lead to performance degradation and/or network failures. Flow delegation addresses this well-known problem by automatically relocating flow rules from a bottlenecked switch to neighboring switches with spare capacity. Different from existing work, this new approach can be used on-demand in a transparent fashion, i.e., without changes to the network applications or other parts of the infrastructure. The thesis presents a system design and architecture capable of dealing with the numerous practical challenges associated with flow delegation, introduces suitable algorithms to efficiently mitigate bottlenecks taking future knowledge and multiple objectives into account and studies feasibility, performance, overhead, and scalability of the new approach covering different scenarios

A Fast Compiler for NetKAT

High-level programming languages play a key role in a growing number of networking platforms, streamlining application development and enabling precise formal reasoning about network behavior. Unfortunately, current compilers only handle "local" programs that specify behavior in terms of hop-by-hop forwarding behavior, or modest extensions such as simple paths. To encode richer "global" behaviors, programmers must add extra state -- something that is tricky to get right and makes programs harder to write and maintain. Making matters worse, existing compilers can take tens of minutes to generate the forwarding state for the network, even on relatively small inputs. This forces programmers to waste time working around performance issues or even revert to using hardware-level APIs. This paper presents a new compiler for the NetKAT language that handles rich features including regular paths and virtual networks, and yet is several orders of magnitude faster than previous compilers. The compiler uses symbolic automata to calculate the extra state needed to implement "global" programs, and an intermediate representation based on binary decision diagrams to dramatically improve performance. We describe the design and implementation of three essential compiler stages: from virtual programs (which specify behavior in terms of virtual topologies) to global programs (which specify network-wide behavior in terms of physical topologies), from global programs to local programs (which specify behavior in terms of single-switch behavior), and from local programs to hardware-level forwarding tables. We present results from experiments on real-world benchmarks that quantify performance in terms of compilation time and forwarding table size

Towards Scalable Network Traffic Measurement With Sketches

Driven by the ever-increasing data volume through the Internet, the per-port speed of network devices reached 400 Gbps, and high-end switches are capable of processing 25.6 Tbps of network traffic. To improve the efficiency and security of the network, network traffic measurement becomes more important than ever. For fast and accurate traffic measurement, managing an accurate working set of active flows (WSAF) at line rates is a key challenge. WSAF is usually located in high-speed but expensive memories, such as TCAM or SRAM, and thus their capacity is quite limited. To scale up the per-flow measurement, we pursue three thrusts. In the first thrust, we propose to use In-DRAM WSAF and put a compact data structure (i.e., sketch) called FlowRegulator before WSAF to compensate for DRAM\u27s slow access time. Per our results, FlowRegulator can substantially reduce massive influxes to WSAF without compromising measurement accuracy. In the second thrust, we integrate our sketch into a network system and propose an SDN-based WLAN monitoring and management framework called RFlow+, which can overcome the limitations of existing traffic measurement solutions (e.g., OpenFlow and sFlow), such as a limited view, incomplete flow statistics, and poor trade-off between measurement accuracy and CPU/network overheads. In the third thrust, we introduce a novel sampling scheme to deal with the poor trade-off that is provided by the standard simple random sampling (SRS). Even though SRS has been widely used in practice because of its simplicity, it provides non-uniform sampling rates for different flows, because it samples packets over an aggregated data flow. Starting with a simple idea that independent per-flow packet sampling provides the most accurate estimation of each flow, we introduce a new concept of per-flow systematic sampling, aiming to provide the same sampling rate across all flows. In addition, we provide a concrete sampling method called SketchFlow, which approximates the idea of the per-flow systematic sampling using a sketch saturation event