DISCO: Distributed Multi-domain SDN Controllers

Modern multi-domain networks now span over datacenter networks, enterprise networks, customer sites and mobile entities. Such networks are critical and, thus, must be resilient, scalable and easily extensible. The emergence of Software-Defined Networking (SDN) protocols, which enables to decouple the data plane from the control plane and dynamically program the network, opens up new ways to architect such networks. In this paper, we propose DISCO, an open and extensible DIstributed SDN COntrol plane able to cope with the distributed and heterogeneous nature of modern overlay networks and wide area networks. DISCO controllers manage their own network domain and communicate with each others to provide end-to-end network services. This communication is based on a unique lightweight and highly manageable control channel used by agents to self-adaptively share aggregated network-wide information. We implemented DISCO on top of the Floodlight OpenFlow controller and the AMQP protocol. We demonstrated how DISCO's control plane dynamically adapts to heterogeneous network topologies while being resilient enough to survive to disruptions and attacks and providing classic functionalities such as end-point migration and network-wide traffic engineering. The experimentation results we present are organized around three use cases: inter-domain topology disruption, end-to-end priority service request and virtual machine migration

A framework for community detection in heterogeneous multi-relational networks

There has been a surge of interest in community detection in homogeneous single-relational networks which contain only one type of nodes and edges. However, many real-world systems are naturally described as heterogeneous multi-relational networks which contain multiple types of nodes and edges. In this paper, we propose a new method for detecting communities in such networks. Our method is based on optimizing the composite modularity, which is a new modularity proposed for evaluating partitions of a heterogeneous multi-relational network into communities. Our method is parameter-free, scalable, and suitable for various networks with general structure. We demonstrate that it outperforms the state-of-the-art techniques in detecting pre-planted communities in synthetic networks. Applied to a real-world Digg network, it successfully detects meaningful communities.Comment: 27 pages, 10 figure

Virus Propagation in Multiple Profile Networks

Suppose we have a virus or one competing idea/product that propagates over a multiple profile (e.g., social) network. Can we predict what proportion of the network will actually get "infected" (e.g., spread the idea or buy the competing product), when the nodes of the network appear to have different sensitivity based on their profile? For example, if there are two profiles $\mathcal{A}$ and $\mathcal{B}$ in a network and the nodes of profile $\mathcal{A}$ and profile $\mathcal{B}$ are susceptible to a highly spreading virus with probabilities $\beta_{\mathcal{A}}$ and $\beta_{\mathcal{B}}$ respectively, what percentage of both profiles will actually get infected from the virus at the end? To reverse the question, what are the necessary conditions so that a predefined percentage of the network is infected? We assume that nodes of different profiles can infect one another and we prove that under realistic conditions, apart from the weak profile (great sensitivity), the stronger profile (low sensitivity) will get infected as well. First, we focus on cliques with the goal to provide exact theoretical results as well as to get some intuition as to how a virus affects such a multiple profile network. Then, we move to the theoretical analysis of arbitrary networks. We provide bounds on certain properties of the network based on the probabilities of infection of each node in it when it reaches the steady state. Finally, we provide extensive experimental results that verify our theoretical results and at the same time provide more insight on the problem