Fernando M. V. Ramos

Software-Defined Networking: A Comprehensive Survey

The Internet has led to the creation of a digital society, where (almost) everything is connected and is accessible from anywhere. However, despite their widespread adoption, traditional IP networks are complex and very hard to manage. It is both difficult to configure the network according to predefined policies, and to reconfigure it to respond to faults, load, and changes. To make matters even more difficult, current networks are also vertically integrated: the control and data planes are bundled together. Software-defined networking (SDN) is an emerging paradigm that promises to change this state of affairs, by breaking vertical integration, separating the network’s control logic from the underlying routers and switches, promoting (logical) centralization of network control, and introducing the ability to program the network. The separation of concerns, introduced between the definition of network policies, their implementation in switching hardware, and the forwarding of traffic, is key to the desired flexibility: by breaking the network control problem into tractable pieces, SDN makes it easier to create and introduce new abstractions in networking, simplifying network management and facilitating network evolution. In this paper, we present a comprehensive survey on SDN. We start by introducing the motivation for SDN, explain its main concepts and how it differs from traditional networking, its roots, and the standardization activities regarding this novel paradigm. Next, we present the key building blocks of an SDN infrastructure using a bottom-up, layered approach. We provide an in-depth analysis of the hardware infrastructure, southbound and northbound application programming interfaces (APIs), network virtualization layers, network operating systems (SDN controllers), network programming languages, and network applications. We also look at cross-layer problems such as debugging and troubleshooting. In an effort to anticipate the future evolution of this new paradigm, we discuss the main ongoing research efforts and challenges of SDN. In particular, we address the design of switches and control platforms with a focus on aspects such as resiliency, scalability, performance, security, and dependabilityVas well as new opportunities for carrier transport networks and cloud providers. Last but not least, we analyze the position of SDN as a key enabler of a software-defined

Towards secure and dependable software-defined networks

Software-defined networking empowers network operators with more flexibility to program their networks. With SDN, network management moves from codifying functionality in terms of low-level device configurations to building software that facilitates network management and debugging. By separating the complexity of state distribution from network specification, SDN provides new ways to solve long-standing problems in networking --- routing, for instance --- while simultaneously allowing the use of security and dependability techniques, such as access control or multi-path. However, the security and dependability of the SDN itself is still an open issue. In this position paper we argue for the need to build secure and dependable SDNs by design. As a first step in this direction we describe several threat vectors that may enable the exploit of SDN vulnerabilities. We then sketch the design of a secure and dependable SDN control platform as a materialization of the concept here advocated. We hope that this paper will trigger discussions in the SDN community around these issues and serve as a catalyser to join efforts from the networking and security & dependability communities in the ultimate goal of building resilient control planes.

On the Feasibility of a Consistent and Fault-Tolerant Data Store for SDNs

Maintaining a strongly consistent network view in a Software Defined Network has been usually proclaimed as a synonym of low performance. We disagree. To support our view, in this paper we argue that with the use of modern distributed systems techniques it is possible to build a strongly consistent, fault-tolerant SDN control framework that achieves acceptable performance. The central element of our architecture is a highly-available, strongly consistent data store. We describe a prototype implementation of a distributed controller architecture integrating the Floodlight controller with a data store implemented using a state-of-the-art replication algorithm. We evaluate the feasibility of the proposed design by analyzing the workloads of real SDN applications (a learning switch, a load balancer and a device manager) and showing that the data store is capable of handling them with adequate performance.

On the Design of Practical Fault-Tolerant SDN Controllers

The increase in the number of SDN-based deployments in production networks is triggering the need to consider fault-tolerant designs of controller architectures. Commercial SDN controller solutions incorporate fault tolerance, but there has been little discussion in the SDN community on the design of such systems and the tradeoffs involved. To fill this gap, we present a by-construction design of a fault-tolerant controller, and materialize it by proposing and formalizing a practical architecture for small to medium-sized networks. A central component of our design is a replicated shared database that stores all network state. Contrary to the more common primary-backup approaches, the proposed design guarantees a smooth transition in case of failures and avoids the need of an additional coordination service. Our preliminary results show that the performance of our solution fulfils the demands of the target networks. We hope this work to be a first step in what we consider a necessary discussion on how to build robust SDNs.

Channel smurfing: Minimising channel switching delay in IPTV distribution networks

One of the major concerns of IPTV network deployment is channel switching (or zapping) delay. This delay can add up to two seconds or more, and its main culprits are synchronisation and buffering. By analysing an extensive dataset — comprising 255 thousand users, 150 TV channels, and covering a 6-month period — we have observed that most channel switching events are linear: it is very common the user switching up or down to the next TV channel. This fact led us to the proposal, in this paper, of a simple mechanism to reduce channel switching delay. Our proposal is to send the neighbouring channels (i.e., channels adjacent to the requested one) to the Set Top Box (STB) during zapping periods. If the user switches to any of these channels the switching latency is virtually eliminated, not affecting therefore users experience. Notwithstanding the simplicity of this scheme, trace-driven simulations show that the zapping delay can be virtually eliminated for a significant percentage of channel switching requests. As an example, by sending the previous and the next channel concurrently with the requested one, for only one minute after a zapping event, switching delay is eliminated for around 45% of all channel switching requests. Furthermore, this simple scheme has a performance close to that of an ideal predictor, while the increase of bandwidth utilisation in the access link is negligible.

Reducing energy consumption in IPTV networks by selective pre-joining of channels

IPTV services are the fastest growing television services in the world today. This is a bandwidth intensive service, requiring low latency and tight control of jitter. To guarantee the quality of service required, service providers opt to multicast all TV channels at all times to everywhere. However, a significant number of channels are rarely watched, so this method is provably resource- and energy-inefficient. In this paper, we argue that the expected increase in quantity and quality of TV channels will become a serious issue, both in terms of bandwidth and energy costs. To overcome this problem, we propose a dynamic scheme that pre-joins only a selection of TV channels. This scheme was evaluated by means of trace-driven simulations using a large dataset from a commercial nationwide IPTV service. The dataset comprises 255 thousand users, 150 TV channels, and covers a 6-month period. We show that by using our scheme IPTV service providers can save a considerable amount of bandwidth while affecting only a very small number of TV channel switching requests. To understand how these bandwidth savings are translated in energy savings, we developed a power consumption model for network equipment based on real measurements. The main conclusions are that while today the bandwidth savings will have reduced impact in energy consumption in the core network, with the introduction of very high definition channels this impact will become significant, justifying the use of resource-efficient distribution schemes such as the one proposed.

Mitigating IPTV zapping delay

Zapping delay, the latency experienced by users when switching between TV channels, is one of the most severe problems affecting IPTV deployment. Synchronization and buffering of video streams can cause channel change delays of several seconds. In this article we analyze the root causes of the problem and survey some of the most relevant techniques proposed to mitigate it.

On the Feasibility of Byzantine Fault-Tolerant MapReduce in Clouds-of-Clouds

MapReduce is a framework for processing large data sets largely used in cloud computing. MapReduce implementations like Hadoop can tolerate crashes and file corruptions, but there is evidence that general arbitrary faults do occur and can affect the correctness of job executions. Furthermore, many individual cloud outages have been reported, raising concerns about depending on a single cloud. We present a MapReduce runtime that tolerates arbitrary faults and runs in a set of clouds at a reasonable cost in terms of computation and execution time. The main challenge is to avoid sending through the internet the huge amount of data that would normally be exchanged between map and reduce tasks.

Relative Delay Estimator for SCTP-Based Concurrent Multipath Transfer

By identifying the shortcomings of using RTT to evaluate the quality of different paths in a multipath scenario, we propose a Relative Delay Estimator (RDE) to compare the relative one way delay of different paths without clock synchronisation. This estimator enables the comparison and selection of the best forward and backward paths, in terms of delay. As an initial application of RDE, we design a novel retransmission policy (NcRDE). The main novelty of this policy is that, from the multiple paths available, the path chosen for retransmission is according to the value of one way delay. We also present an extension to this scheme that takes path failures into account (PF-NcRDE). Simulation results show that, when compared with recently proposed retransmission policies, NcRDE can improve throughput when the different paths have different forward and backward delays. Also, in case of path failure PF-NcRDE enhances the performance significantly over NcRDE.

Medusa: An Efficient Cloud Fault-Tolerant MapReduce

Applications such as web search and social networking have been moving from centralized to decentralized cloud architectures to improve their scalability. MapReduce, a programming framework for processing large amounts of data using thousands of machines in a single cloud, also needs to be scaled out to multiple clouds to adapt to this evolution. The challenge of building a multi-cloud distributed architecture is substantial. Notwithstanding, the ability to deal with the new types of faults introduced by such setting, such as the outage of a whole datacenter or an arbitrary fault caused by a malicious cloud insider, increases the endeavor considerably. In this paper we propose Medusa, a platform that allows MapReduce computations to scale out to multiple clouds and tolerate several types of faults. Our solution fulfills four objectives. First, it is transparent to the user, who writes her typical MapReduce application without modification. Second, it does not require any modification to the widely used Hadoop framework. Third, the proposed system goes well beyond the fault-tolerance offered by MapReduce to tolerate arbitrary faults, cloud outages, and even malicious faults caused by corrupt cloud insiders. Fourth, it achieves this increased level of fault tolerance at reasonable cost. We performed an extensive experimental evaluation in the ExoGENI testbed, demonstrating that our solution significantly reduces execution time when compared to traditional methods that achieve the same level of resilience.