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In-band Network Telemetry (INT) is gaining traction as an advanced network monitoring approach. Despite a few recent initiatives to orchestrate the collection of in-band network statistics, state-of-the-art approaches fall short when it comes to efficiently collect telemetry items while subjected to real-world constraints. In this letter, we propose Probe Planning for In-Band Network Telemetry (P2INT) to coordinate how probing packets are generated and routed to ensure that all links are covered so that the required in-band network telemetry data is collected. We theoretically formalize the problem as a Integer Linear Programming model and propose an efficient mathematical programming-based heuristic to solve it. Our results show that P2INT outperforms the closest contender by a factor of up to 6x concerning the number of probing cycles generated.

Existing network virtualization systems share a few characteristics, namely they target one data center of a single operator and only offer traditional networking services. As such, their support for critical applications that need to be deployed across multiple trust domains, while enforcing diverse security requirements, is limited. This paper enhances the state-of-the-art by presenting a multi-cloud network virtualization system, allowing the provision of virtual networks of containers. Our solution enables a provider to enrich its network substrate with public and private cloud-based resources, increasing flexibility and the range of supplied services. One challenging aspect that we tackle is the embedding of virtual network requests to the substrate infrastructure, as existing work is unfit to a modern data center context, scales poorly or does not consider the security of virtual resources. We propose a scalable heuristic that considers security as a first-class citizen and is specifically tailored to a hybrid multi-cloud domain. We evaluate our algorithm with large-scale simulations that consider realistic network topologies and our prototype in a substrate composed of one private data center and two public clouds. The system scales well for networks of thousands of switches employing diverse topologies and improves on the virtual network acceptance ratio, provider revenue, and embedding delays. Our results show that the acceptance ratios are less than 1% from the optimal and that the system can provision a 10 thousand container virtual network in approximately 2 minutes.

Software-defined networking (SDN) decouples the control and data planes of traditional networks, logically centralizing the functional properties of the network in the SDN controller. While this centralization brought advantages such as a faster pace of innovation, it also disrupted some of the natural defenses of traditional architectures against different threats. The literature on SDN has mostly been concerned with the functional side, despite some specific works concerning non-functional properties such as security or dependability. Though addressing the latter in an ad-hoc, piecemeal way may work, it will most likely lead to efficiency and effectiveness problems. We claim that the enforcement of non-functional properties as a pillar of SDN robustness calls for a systemic approach. We further advocate, for its materialization, the reiteration of the successful formula behind SDN: ‘logical centralization’. As a general concept, we propose anchor , a subsystem architecture that promotes the logical centralization of non-functional properties. To show the effectiveness of the concept, we focus on security in this article: we identify the current security gaps in SDNs and we populate the architecture middleware with the appropriate security mechanisms in a global and consistent manner. Essential security mechanisms provided by anchor include reliable entropy and resilient pseudo-random generators, and protocols for secure registration and association of SDN devices. We claim and justify in the article that centralizing such mechanisms is key for their effectiveness by allowing us to define and enforce global policies for those properties; reduce the complexity of controllers and forwarding devices; ensure higher levels of robustness for critical services; foster interoperability of the non-functional property enforcement mechanisms; and promote the security and resilience of the architecture itself. We discuss design and implementation aspects, and we prove and evaluate our algorithms and mechanisms, including the formalisation of the main protocols and the verification of their core security properties using the T amarin prover.

Network virtualization allows multiple tenant networks to coexist on a shared infrastructure. Core to its realization is the embedding of virtual networks onto the underlying substrate. Existing approaches are not suitable for cloud environments as they lack a fundamental requirement: elasticity. To address this issue we explore the capacity of flexibly changing the topology of a virtual network by proposing an embedding solution that adds elasticity to the tenant’s virtual infrastructures. For this purpose, we introduce four primitives to tenants’ virtual networks – including scale in and scale out – and propose new algorithms to materialize them. The main challenge is to enable these new services while maximizing resource efficiency and without impacting service quality. Instead of further improving existing online embedding algorithms – always limited by the inability to predict future demand – we follow a different approach. Specifically, we leverage network migration for our embedding procedures and to introduce a new reconfiguration primitive for the infrastructure provider. As migration introduces network churn, our solution uses this technique judiciously, to limit the impact to running services. Our solution improves on network efficiency over the state-of-the-art, while reducing the migration footprint by at least one order of magnitude.

MapReduce is a popular distributed data-processing system for analyzing big data in cloud environments. This platform is often used for critical data processing, e.g., in the context of scientific or financial simulation. Unfortunately, there is accumulating evidence of severe problems - including arbitrary faults and cloud outages - affecting the services that run atop cloud services. Faced with this challenge, we have recently explored multicloud solutions to increase the resilience and availability of MapReduce. Based on this experience, we present system design guidelines that allow to scale out MapReduce computation to multiple clouds in order to tolerate arbitrary and malicious faults, as well as cloud outages. Crucially, the techniques we introduce have reasonable cost and do not require changes to MapReduce or to the users’ code, enabling immediate deployment.

The Internet today is mainly used for distributing content, in a fundamental departure from its original goal of enabling communication between endpoints. As a response to this change, Named Data Networking (NDN) is a new architecture rooted on the concept of naming data, in contrast to the original paradigm based on naming hosts. This radical architectural shift results in packet processing in NDN to differ substantially from IP. As a consequence, current network equipment cannot be seamlessly extended to offer NDN data-plane functions. To address this challenge, available NDN router solutions are usually software-based, and even the highly-optimised designs tailored to specific hardware platforms present limited performance, hindering adoption. In addition, these tailor-made solutions are hardly reusable in research and production networks. The emergence of programmable switching chips and of languages to program them, like P4, brings hope for the state of affairs to change. In this paper, we present the design of an NDN router written in P4. We improve over the state-of-the-art solution by extending the NDN functionality, and by addressing its scalability limitations. A preliminary evaluation of our open-source solution running on a software target demonstrates its feasibility.

Security is an increasingly fundamental requirement in Software-Defined Networking (SDN). However, the pace of adoption of secure mechanisms has been slow, which we estimate to be a consequence of the performance overhead of traditional solutions and of the complexity of their support infrastructure. To address these challenges we propose KISS, a secure SDN control plane communications architecture that includes innovative solutions in the context of key distribution and secure channel support. Core to our contribution is the integrated device verification value (iDVV), a deterministic but indistinguishablefrom-random secret code generation protocol that allows local but synchronized generation/verification of keys at both ends of the control channel, even on a per-message basis. We show that our solution, while offering the same security properties, outperforms reference alternatives, with performance improvements up to 30% over OpenSSL, and improvement in robustness based on a code footprint one order of magnitude smaller.

MapReduce is a framework for processing large data sets much used in the context of cloud computing. MapReduce implementations like Hadoop can tolerate crashes and file corruptions, but not arbitrary faults. Unfortunately, there is evidence that arbitrary faults do occur and can affect the correctness of MapReduce job executions. Furthermore, many outages of major cloud offerings have been reported, raising concerns about the dependence on a single cloud. In this paper we propose a novel execution system that allows to scale out MapReduce computations to a cloud-of-clouds and tolerate arbitrary faults, malicious faults, and cloud outages. Our system, Chrysaor, is based on a fine-grained replication scheme that tolerates faults at the task level. Our solution has three important properties: it tolerates the above-mentioned classes of faults at reasonable cost; it requires minimal modifications to the users’ applications; and it does not involve changes to the Hadoop source code.We performed an extensive evaluation of our system in Amazon EC2, showing that our fine-grained solution is efficient in terms of computation by recovering only faulty tasks. This is achieved without incurring a significant penalty for the baseline case (i.e., without faults) in most workloads.

Existing multi-tenant network virtualization platforms have so far focused on the offer of conventional Networking services by a single cloud provider. As such, they face limitations in terms of security and dependability, both in terms of the infrastructure itself and of the services offered to its customers. To address these challenges we present the design and implementation of Sirius, a network virtualization platform for multi-cloud environments. Contrary to existing solutions, Sirius considers not only connectivity and performance, but also security and dependability as first class citizens, leveraging from a substrate infrastructure composed of both public clouds and private data centers.

Recent years have seen a trend towards decentralisation - from initiatives on decentralized web to decentralized network infrastructures. In this position paper, we present an architectural vision for decentralising cloud service infrastructures. Our vision is on community cloud infrastructures on top of decentralised access infrastructures i.e. community networks, using resources pooled from the community. Our architectural vision considers some fundamental challenges of integrating the current state of the art virtualisation technologies such as Software Defined Networking (SDN) into community infrastructures which are highly unreliable. Our proposed design goal is to include lightweight network and processing virtualization with fault tolerance mechanisms to ensure sufficient level of reliability to support local services.

Modern network virtualization platforms enable users to specify custom topologies and arbitrary addressing schemes for their virtual networks. These platforms have, however, been targeting the data center of a single provider, which is insufficient to support (critical) applications that need to be deployed across multiple trust domains, while enforcing diverse security requirements. This paper addresses this limitation by presenting a novel solution for the central resource allocation problem of network virtualization -- the virtual network embedding, which aims to find efficient mappings of virtual network requests onto the substrate network. We improve over the state-of-the-art by considering security as a first-class citizen of virtual networks, while enhancing the substrate infrastructure with resources from multiple cloud providers. Our solution enables the definition of flexible policies in three core elements: on the virtual links, where alternative security compromises can be explored (e.g., encryption); on the virtual switches, supporting various degrees of protection and redundancy if necessary; and on the substrate infrastructure, extending it across multiple clouds, including public and private facilities, with their inherently diverse trust levels associated. We propose an optimal solution to this problem formulated as a Mixed Integer Linear Program (MILP). The results of our evaluation give insight into the trade-offs associated with the inclusion of security demands into network virtualization. In particular, they provide evidence that enhancing the user's virtual networks with security does not preclude high acceptance rates and an efficient use of resources, and allows providers to increase their revenues., Comment: 1. Paper title changed to be more suitable; 2. Rewrite Introduction (section 1), Secure Virtual Network Embedding Problem (section 3) and MILP Formulation (Section 5) to make it clearer; 3. Figure 3 inserted to help in explanation on the main and backup VNE mappings; 4. Added references on Related Work (Section 7)

Scalable and fault-tolerant distributed Software-Defined Networking (SDN) controllers usually give up strong consistency for the network state, adopting instead the more efficient eventually consistent storage model. This decision is mostly due to the performance overhead of the strongly consistent replication protocols (e.g., Paxos, RAFT), which limits the responsiveness and scalability of network applications. Unfortunately, this lack of consistency leads to a complex programming model for network applications and can lead to network anomalies. In this paper we show how the lack of control plane consistency can lead to network problems and propose a distributed SDN control plane architecture to address this issue. Our modular architecture is supported by a fault-tolerant data store that provides the strong consistency properties necessary for transparent distribution of the control plane. In order to deal with the fundamental concern of such design, we apply a number of techniques tailored to SDN for optimizing the data store performance. To evaluate the impact of these techniques we analyze the workloads generated by three real SDN applications as they interact with the data store. Our results show a two-to four-fold improvement in latency and throughput, respectively, when compared with a non-optimized design.

Recent SDN-based solutions give cloud providers the opportunity to extend their "as-a-service" model with the offer of complete network virtualization. They provide tenants with the freedom to specify the network topologies and addressing schemes of their choosing, while guaranteeing the required level of isolation among them. These platforms, however, have been targeting the datacenter of a single cloud provider with full control over the infrastructure. This paper extends this concept further by supporting the creation of virtual networks that span across several datacenters, which may belong to distinct cloud providers, while including private facilities owned by the tenant. In order to achieve this, we introduce a new network layer above the existing cloud hypervisors, affording the necessary level of control over the communications while hiding the heterogeneity of the clouds. The benefits of this approach are various, such as enabling finer decisions on where to place the virtual machines (e.g., to fulfill legal requirements), avoiding single points of failure, and potentially decreasing costs. Although our focus in the paper is on architecture design, we also present experimental results of a first prototype of the proposed solution.

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.