Efficient techniques for end-to-end bandwidth estimation: performance evaluations and scalable deployment

Several applications, services, and protocols are conjectured to benefit from the knowledge of the end-to-end available bandwidth on a given Internet path. Unfortunately, despite the availability of several bandwidth estimation techniques, there has been only a limited adoption of these in contemporary applications. We identify two issues that contribute to this state of affairs. First, there is a lack of comprehensive evaluations that can help application developers in calibrating the relative performance of these tools--this is especially limiting since the performance of these tools depends on algorithmic, implementation, as well as temporal aspects of probing for available bandwidth. Second, most existing bandwidth estimation tools impose a large probing overhead on the paths over which bandwidth is measured. This can be a significant deterrent for deploying these tools in distributed infrastructures that need to measure bandwidth on several paths periodically. In this dissertation, we address the two issues raised above by making the following contributions: We conduct the first comprehensive black-box evaluation of a large suite of prominent available bandwidth estimation tools on a high-speed network. In this evaluation,we also illustrate the impact that technological and implementation limitations can have on the performance of bandwidth-estimation tools. We conduct the first comprehensive evaluation of available bandwidth estimation algorithms, independent of systemic and implementation biases. In this evaluation, we also illustrate the impact temporal factor such as measurement timescales have on the observed relative performance of bandwidth-estimation tools. We demonstrate that temporal properties can significantly impact the AB estimation process. We redesign the interfaces of existing bandwidth-estimation tools to allow temporal parameters to be explicitly specified and controlled. We design AB inference schemes which can be used to scalably and collaboratively infer the available bandwidth for a large set of end-to-end paths. These schemes allow an operator to select the desired operating point in the trade-off between accuracy and overhead of AB estimation. We further demonstrate that in order to monitor the bandwidth on all paths of a network we do not need access to per-hop bandwidth estimates and can simply rely on end-to-end bandwidth estimates

Achieving network resiliency using sound theoretical and practical methods

Computer networks have revolutionized the life of every citizen in our modern intercon- nected society. The impact of networked systems spans every aspect of our lives, from financial transactions to healthcare and critical services, making these systems an attractive target for malicious entities that aim to make financial or political profit. Specifically, the past decade has witnessed an astounding increase in the number and complexity of sophisti- cated and targeted attacks, known as advanced persistent threats (APT). Those attacks led to a paradigm shift in the security and reliability communities’ perspective on system design; researchers and government agencies accepted the inevitability of incidents and malicious attacks, and marshaled their efforts into the design of resilient systems. Rather than focusing solely on preventing failures and attacks, resilient systems are able to maintain an acceptable level of operation in the presence of such incidents, and then recover gracefully into normal operation. Alongside prevention, resilient system design focuses on incident detection as well as timely response. Unfortunately, the resiliency efforts of research and industry experts have been hindered by an apparent schism between theory and practice, which allows attackers to maintain the upper hand advantage. This lack of compatibility between the theory and practice of system design is attributed to the following challenges. First, theoreticians often make impractical and unjustifiable assumptions that allow for mathematical tractability while sacrificing accuracy. Second, the security and reliability communities often lack clear definitions of success criteria when comparing different system models and designs. Third, system designers often make implicit or unstated assumptions to favor practicality and ease of design. Finally, resilient systems are tested in private and isolated environments where validation and reproducibility of the results are not publicly accessible. In this thesis, we set about showing that the proper synergy between theoretical anal- ysis and practical design can enhance the resiliency of networked systems. We illustrate the benefits of this synergy by presenting resiliency approaches that target the inter- and intra-networking levels. At the inter-networking level, we present CPuzzle as a means to protect the transport control protocol (TCP) connection establishment channel from state- exhaustion distributed denial of service attacks (DDoS). CPuzzle leverages client puzzles to limit the rate at which misbehaving users can establish TCP connections. We modeled the problem of determining the puzzle difficulty as a Stackleberg game and solve for the equilibrium strategy that balances the users’ utilizes against CPuzzle’s resilience capabilities. Furthermore, to handle volumetric DDoS attacks, we extend CPuzzle and implement Midgard, a cooperative approach that involves end-users in the process of tolerating and neutralizing DDoS attacks. Midgard is a middlebox that resides at the edge of an Internet service provider’s network and uses client puzzles at the IP level to allocate bandwidth to its users. At the intra-networking level, we present sShield, a game-theoretic network response engine that manipulates a network’s connectivity in response to an attacker who is moving laterally to compromise a high-value asset. To implement such decision making algorithms, we leverage the recent advances in software-defined networking (SDN) to collect logs and security alerts about the network and implement response actions. However, the programma- bility offered by SDN comes with an increased chance for design-time bugs that can have drastic consequences on the reliability and security of a networked system. We therefore introduce BiFrost, an open-source tool that aims to verify safety and security proper- ties about data-plane programs. BiFrost translates data-plane programs into functionally equivalent sequential circuits, and then uses well-established hardware reduction, abstrac- tion, and verification techniques to establish correctness proofs about data-plane programs. By focusing on those four key efforts, CPuzzle, Midgard, sShield, and BiFrost, we believe that this work illustrates the benefits that the synergy between theory and practice can bring into the world of resilient system design. This thesis is an attempt to pave the way for further cooperation and coordination between theoreticians and practitioners, in the hope of designing resilient networked systems

Testbeds in Computer Science

International audienceComputer scientists working on the design of hardware and software architectures and in particular on the design of distributed architectures (network, high performance computing, cloud, sensor networks, IoT, etc.) need to evaluate the relevance of their proposal at scale on a regular basis. Hence, their practice relies on frequent experimental evaluations, which leads to specific needs in term of experimental control. In this context, reproducing the work of other colleagues happens to be very difficult as it requires not only to have precise information about experimental conditions (software stack, external load, hardware type and configuration, etc.) but also to have a testbed allowing to recreate similar experimental conditions. A few experimental testbeds allowing for fine-grain experimental control have been built in the last decade (Grid5000, R2Lab, …). Such testbeds are mutualized and generally open-access, which makes it possible to compare in a fair and truthful way alternative approaches at large scale. In this webinar, Lucas Nussbaum will provide an overview of these projects and of their internals

Minimally Invasive Solutions to Challenges Posed by Mobility Changes

Today, things have changed radically. As network technologies have proliferated and evolved, the components of, and participants in, computerized systems have become increasingly decoupled. Users travel and commute while connecting to their office computer or home media server. Hardware devices may be carried by users, move on their own, or reside in data centers, never to be seen or touched by end-users. Even operating systems (OSes) and applications may now migrate across the network while executing, thanks to advances in virtualization that are only just beginning to remake the computing landscape. The decoupling of users, devices, and software has invalidated properties that enabled desired functionality: resulting in compromised function. Power interfaces utilize physi- cal user interactions to determine when transitions between high and lower power states should occur; what happens when users are no longer physically present? Operating system execution often relies on components such as CPU and local disk responding with tightly bounded delays; what should be done when the OS itself is in the process of migrating between two separate physical machines? The fundamental question explored by this dissertation is: Can we find highly adoptable solutions to restore desired functionality that has been lost because of changed mobility characteristics? Our emphasis on adoptability stems from pragmatic concerns: if a solution is difficult to adopt, it is highly unlikely to be used. Consequently, while many potential approaches may involve changes to the network itself, our work focuses on modifying end-point behavior. We show that practical solutions implemented solely in software and deployed only on network endpoints can be developed for a wide problem range. We consider concrete challenges arising from user, device, and software mobility changes, affecting sub-disciplines spanning cloud computing, green computing, and wireless networks. Cloud Computing: Users increasingly utilize virtual machine (VM) technology to migrate and replicate OS and software amongst networked hosts. Traditional execution required one VM image copy on each host's local storage. By transitioning to networked execution, dozens, if not hundreds, of VM replicas may now be distributed from a single networked storage location to a commensurately large set of physical machines. As these systems expand, they have come to be plagued by boot storms (and similar problems) caused when networked access to storage becomes a major bottleneck, drastically delaying VM distribution and execution. Can we develop techniques that resolve this network bottleneck without the need for expensive hardware over-provisioning? Green Computing: Remote access technologies have enabled users to travel while still interacting with computational machinery left in the office or home. Yet, energy savings mechanisms have traditionally relied on the activity of attached peripherals to determine power usage. The shift to remote interaction, which bypasses physically attached peripherals, has effectively broken these energy savings mechanisms. Can we build an economic and practical system that accommodates energy efficiency without compromising the fluid remote interactions users have now come to expect? Wireless Computing: Increasingly advanced mobile devices have provoked a shift towards heavy usage of 3G and 4G bandwidth use. Accordingly, the capacity of infrastructure wireless networks becomes increasingly strained. Can we find a way of supplementing this relatively low-latency infrastructure with high-latency, high-bandwidth opportunistic content exchange? In each scenario, we design a solution that aims to strike the proper balance between adoptability and technical efficiency - producing what we believe are rigorous, practical and adoptable solutions

Scaling your experiments

National audienceThere is a wide range of options to experiment on distributed systems and networking. Simulators running on a laptop or self-made testbeds are sometimes enough, but our field usually targets large to very large systems with potentially millions or billions of elements. In such a case, relying on a laptop or a self-made testbed is impossible. To scale up our experimental research, we can rely on larger-scale infrastructures and testbeds.In a first part, this talk will provide an overview of the landscape of infrastructures and testbeds supporting experimental research in distributed systems and networking.In a second part, we will focus on SDN/NFV experimentation, and will provide some feedback about the current state of available experimentation tools targeting large scale systems

Traffic Re-engineering: Extending Resource Pooling Through the Application of Re-feedback

Parallelism pervades the Internet, yet efficiently pooling this increasing path diversity has remained elusive. With no holistic solution for resource pooling, each layer of the Internet architecture attempts to balance traffic according to its own needs, potentially at the expense of others. From the edges, traffic is implicitly pooled over multiple paths by retrieving content from different sources. Within the network, traffic is explicitly balanced across multiple links through the use of traffic engineering. This work explores how the current architecture can be realigned to facilitate resource pooling at both network and transport layers, where tension between stakeholders is strongest. The central theme of this thesis is that traffic engineering can be performed more efficiently, flexibly and robustly through the use of re-feedback. A cross-layer architecture is proposed for sharing the responsibility for resource pooling across both hosts and network. Building on this framework, two novel forms of traffic management are evaluated. Efficient pooling of traffic across paths is achieved through the development of an in-network congestion balancer, which can function in the absence of multipath transport. Network and transport mechanisms are then designed and implemented to facilitate path fail-over, greatly improving resilience without requiring receiver side cooperation. These contributions are framed by a longitudinal measurement study which provides evidence for many of the design choices taken. A methodology for scalably recovering flow metrics from passive traces is developed which in turn is systematically applied to over five years of interdomain traffic data. The resulting findings challenge traditional assumptions on the preponderance of congestion control on resource sharing, with over half of all traffic being constrained by limits other than network capacity. All of the above represent concerted attempts to rethink and reassert traffic engineering in an Internet where competing solutions for resource pooling proliferate. By delegating responsibilities currently overloading the routing architecture towards hosts and re-engineering traffic management around the core strengths of the network, the proposed architectural changes allow the tussle surrounding resource pooling to be drawn out without compromising the scalability and evolvability of the Internet

Trustworthy Knowledge Planes For Federated Distributed Systems

In federated distributed systems, such as the Internet and the public cloud, the constituent systems can differ in their configuration and provisioning, resulting in significant impacts on the performance, robustness, and security of applications. Yet these systems lack support for distinguishing such characteristics, resulting in uninformed service selection and poor inter-operator coordination. This thesis presents the design and implementation of a trustworthy knowledge plane that can determine such characteristics about autonomous networks on the Internet. A knowledge plane collects the state of network devices and participants. Using this state, applications infer whether a network possesses some characteristic of interest. The knowledge plane uses attestation to attribute state descriptions to the principals that generated them, thereby making the results of inference more trustworthy. Trustworthy knowledge planes enable applications to establish stronger assumptions about their network operating environment, resulting in improved robustness and reduced deployment barriers. We have prototyped the knowledge plane and associated devices. Experience with deploying analyses over production networks demonstrate that knowledge planes impose low cost and can scale to support Internet-scale networks

Efficient Support for Application-Specific Video Adaptation

As video applications become more diverse, video must be adapted in different ways to meet the requirements of different applications when there are insufficient resources. In this dissertation, we address two sorts of requirements that cannot be addressed by existing video adaptation technologies: (i) accommodating large variations in resolution and (ii) collecting video effectively in a multi-hop sensor network. In addition, we also address requirements for implementing video adaptation in a sensor network. Accommodating large variation in resolution is required by the existence of display devices with widely disparate screen sizes. Existing resolution adaptation technologies usually aim at adapting video between two resolutions. We examine the limitations of these technologies that prevent them from supporting a large number of resolutions efficiently. We propose several hybrid schemes and study their performance. Among these hybrid schemes, Bonneville, a framework that combines multiple encodings with limited scalability, can make good trade-offs when organizing compressed video to support a wide range of resolutions. Video collection in a sensor network requires adapting video in a multi-hop storeand- forward network and with multiple video sources. This task cannot be supported effectively by existing adaptation technologies, which are designed for real-time streaming applications from a single source over IP-style end-to-end connections. We propose to adapt video in the network instead of at the network edge. We also propose a framework, Steens, to compose adaptation mechanisms on multiple nodes. We design two signaling protocols in Steens to coordinate multiple nodes. Our simulations show that in-network adaptation can use buffer space on intermediate nodes for adaptation and achieve better video quality than conventional network-edge adaptation. Our simulations also show that explicit collaboration among multiple nodes through signaling can improve video quality, waste less bandwidth, and maintain bandwidth-sharing fairness. The implementation of video adaptation in a sensor network requires system support for programmability, retaskability, and high performance. We propose Cascades, a component-based framework, to provide the required support. A prototype implementation of Steens in this framework shows that the performance overhead is less than 5% compared to a hard-coded C implementation