Biblio

When encountering a packet flow for which it has no covering rule, a software-defined networking (SDN) switch requests an appropriate rule from its controller; this request delays the routing of the flow until the controller responds. We show that this delay gives rise to a timing side channel in which an attacker can test for the recent occurrence of a target flow by judiciously probing the switch with forged flows and using the delays they suffer to discern whether covering rules were previously installed in the switch. We develop a Markov model of an SDN switch to permit the attacker to select the best probe (or probes) to infer whether a target flow has recently occurred. Our model captures complexities related to rule evictions to make room for other rules; rule timeouts due to inactivity; the presence of multiple rules that apply to overlapping sets of flows; and rule priorities. We show that our model permits detection of target flows with considerable accuracy in many cases.

When encountering a packet for which it has no matching forwarding rule, a software-defined networking (SDN) switch requests an appropriate rule from its controller; this request delays the routing of the flow until the controller responds.  We show that this delay gives rise to a timing side channel in which an attacker can test for the recent occurrence of a target flow by judiciously probing the switch with forged flows and using the delays they encounter to discern whether covering rules were previously installed in the switch.  We develop a Markov model of an SDN switch to permit the attacker to select the best probe (or probes) to infer whether a target flow has recently occurred.  Our model captures practical challenges related to rule evictions to make room for other rules; rule timeouts due to inactivity; the presence of multiple rules that apply to overlapping sets of flows; and rule priorities.  We show that our model enables detection of target flows with considerable accuracy in many cases.

Modern Internet applications are being disaggregated into a microservice-based architecture, with services being updated and deployed hundreds of times a day. The accelerated software life cycle and heterogeneity of language runtimes in a single application necessitates a new approach for testing the resiliency of these applications in production infrastructures. We present Gremlin, a framework for systematically testing the failure-handling capabilities of microservices.  Gremlin is based on the observation that microservices are loosely coupled and thus rely on standard message-exchange patterns over the network. Gremlin allows the operator to easily design tests and executes them by manipulating inter-service messages at the network layer. We show how to use Gremlin to express common failure scenarios and how developers of an enterprise application were able to discover previously unknown bugs in their failure-handling code without modifying the application.

Software-defined networking (SDN) can enable diverse network management applications such as traffic engineering, service chaining, network function outsourcing, and topology reconfiguration. Realizing the benefits of SDN for these applications, however, entails addressing complex network optimizations that are central to these problems. Unfortunately, such optimization problems require significant manual effort and expertise to express and non-trivial computation and/or carefully crafted heuristics to solve. Our vision is to simplify the deployment of SDN applications using general high-level abstractions for capturing optimization requirements from which we can efficiently generate optimal solutions. To this end, we present SOL, a framework that demonstrates that it is indeed possible to simultaneously achieve generality and efficiency. The insight underlying SOL is that SDN applications can be recast within a unifying path-based optimization abstraction, from which it efficiently generates near-optimal solutions, and device configurations to implement those solutions. We illustrate the generality of SOL by prototyping diverse and new applications. We show that SOL simplifies the development of SDN-based network optimization applications and provides comparable or better scalability than custom optimization solutions.