Peer-to-peer systems can reach an incredibly large scale, up to millions of nodes, which typically join and leave continuously. These properties are very challenging to deal with. Evaluating a new protocol in a real environment, especially in its early stages of development, is not feasible. PeerSim is an open-source simulation environment for peer-to-peer systems called Peersim. Peersim has been developed with extreme scalability and support for dynamism in mind. The research group at the University of Bologna, as well as other research groups inside and outside the consortium, use it in their everyday research. It is composed of two simulation engines, a simplified (cycle-based) one and event driven one. The engines are supported by many simple, extendable, and pluggable components, with a flexible configuration mechanism. The cycle-based engine, to allow for scalability, uses some simplifying assumptions, such as ignoring the details of the transport layer in the communication protocol stack. The event-based engine is less efficient but more realistic. Among other things, it supports transport layer simulation as well. In addition, cycle-based protocols can be run by the event-based engine too. The large adoption of Peersim is witnessed by several facts; Peersim has been downloaded more than 2500 times; programmers outside the consortium are now participating in its development; and finally, an increasing number of papers written by simple users have started to appear in the scientific community.
AntPing is a prototype for ant-based management of virtual paths in the Internet. It can be used both for establishment, maintenance and monitoring the quality of the paths. The purpose of AntPing (ant-based routing and monitoring system), is to demonstrate a working implementation on a software based IP router. The implementation is based on Click, a modular software router. The prototype is running on home routers (LinkSys) with open wrt Linux distribution, and is using hping3 as an API for socket programming. The routing is used to establish, maintain, and monitor virtual paths through a replication of the Telenor core Internet backbone topology. The demonstrator visualizes the inner workings of the ant algorithm by animation of ants moving and dropping in the network, and topology changes like link and node failures and restorations. In addition, the changes in cost values of each virtual path are plotted as a function over time. The AntPing are demonstrated in R&D colloquia for researcher and network operators, and for a graduate student at the Norwegian University of Science and Technology. Further work on AntPing might include potential refinement of method and prototype for larger scale network.
Immune based search algorithm is used to develop an efficient search algorithm upon unstructured peer-to-peer networks. The efficiency arise from the two-fold strategy taken - Devising a fast propagation scheme of the query message packets, and - Arranging the peers to form clusters of peers, which host similar data so that the search operation can be consequently speeded up. To design faster propagation of message packets, we use concepts from the proliferation and mutation mechanism operative in natural immune systems. The rearrangement of peers is devised in such a way so as to build memory within the P2P system, whereby, the search operation becomes faster when it is repeated over several times. The software package implementing the above algorithms are available in BISON website with full documentation. Besides the algorithms, rigorous theoretical results explaining the reason behind the efficiency of the algorithms has also been done and is available in the deliverables published.
We have developed a theory of epidemic spreading, which is based on a network picture, and on a novel structural analysis of the network. The theory makes a number of predictions, which have been tested extensively in simulations, and strongly confirmed. Our ideas also naturally suggest a number of practical strategies for influencing epidemic spreading. For example, if it is desired to enhance spreading (of innovation or product adoption), our picture has concrete suggestions for how to do so; and similarly, if the spreading is to be minimized (computer viruses, or biological epidemics), we offer strategies here also. Application of our theory is dependent on measuring or estimating the network over which the spreading takes place - including, if possible, the relative probabilities (per unit time) for spreading over each link.
We implemented AntHocNet, a novel algorithm for routing in mobile ad hoc networks (MANETs). AntHocNet is designed to show adaptive and robust behaviour with respect to network changes. This is achieved by means of a hybrid design that combines reactive and proactive behaviours allowing to both anticipate and respond in timely fashion to sudden disruptive events. At the very core of the algorithm there are two adaptive learning mechanisms: the Monte Carlo sampling and learning typical of ant-based approaches, and an information bootstrapping process typical of many reinforcement learning schemes. Operating at different timescales the two mechanisms allow to continuously adapt nodes' routing tables and efficiently set-up multiple routing paths optimised with respect to a number of metrics of interest, such as delay, throughput, signal-to-noise ratio, etc. Using QualNet, a popular commercial network simulator, AntHocNet performance has been assessed through extensive simulation studies considering a number of open space and urban/structured MANET scenarios with different traffic and mobility dynamics. Compared to other state-of-the-art algorithms such as AODV and OLSR, AntHocNet always shows superior performance and robustness of response over the different scenarios, at the expenses of a comparable or even lower routing overhead. AntHocNet also shows excellent superior performance in wireless mesh networks and in wired scenarios (outperforming OSPF). All these results support the view that AntHocNet has an enormous potential for use in real-world MANETs, and, more in particular, in heterogeneous mesh networks composed of mobile, fixed, and wired nodes. In fact, the hybrid and composite nature of its design makes it an algorithm able to cope equally well with a number of different networks and network dynamics, as well as with a number of different modes/characteristics coexisting in the same, possibly heterogeneous, network. We are proceeding to an implementation of the algorithm on Linux-based wireless/wired devices. In a few months we should have the first data from real-world experiments in a mesh network. If real-world results will confirm the simulation ones we will proceed considering the deployment of mesh networks based on the use of AntHocNet.