A State-Based Proactive Approach To Network Isolation Verification In Clouds

The multi-tenancy nature of public clouds usually leads to cloud tenants' concerns over network isolation around their virtual resources. Verifying network isolation in clouds faces unique challenges. The sheer size of virtual infrastructures paired with the self-serviced nature of clouds means the verification will likely have a high complexity and yet its results may become obsolete in seconds. Moreover, the _ne-grained and distributed network access control (e.g., per-VM security group rules) typical to virtual cloud infrastructures means the verification must examine not only the events but also the current state of the infrastructures. In this thesis, we propose VMGuard, a state-based proactive approach for efficiently verifying large-scale virtual infrastructures against network isolation policies. Informally, our key idea is to proactively trigger the verification based on predicted events and their simulated impact upon the current state, such that we can have the best of both worlds, i.e., the efficiency of a proactive approach and the effectiveness of state-based verification. We implement and evaluate VMGuard based on OpenStack, and our experiments with both real and synthetic data demonstrate the performance and efficiency

DyNetKAT: An Algebra of Dynamic Networks

We introduce a formal language for specifying dynamic updates for Software Defined Networks. Our language builds upon Network Kleene Algebra with Tests (NetKAT) and adds constructs for synchronisations and multi-packet behaviour to capture the interaction between the control- and data-plane in dynamic updates. We provide a sound and ground-complete axiomatisation of our language. We exploit the equational theory to provide an efficient reasoning method about safety properties for dynamic networks. We implement our equational theory in DyNetiKAT -- a tool prototype, based on the Maude Rewriting Logic and the NetKAT tool, and apply it to a case study. We show that we can analyse the case study for networks with hundreds of switches using our initial tool prototype

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