Section: Scientific Foundations

Efficient Queries and Compact Data Structures

The optimization schemes for content distribution processes or for handling standard queries require a good knowledge of the physical topology or performance (latencies, throughput, ...) of the network. Assuming that some rough estimate of the physical topology is given, former theoretical results described in Section  6.2 show how to pre-process the network so that local computations are performed efficiently. Due to the dynamism of large distributed platforms, some requirements on the coding of local data structures and the updating mechanism are needed. This last process is done using the maintenance of light virtual networks, so-called overlay networks (see Section  6.2 ). In our approach, we focus on:

• Compact Routing tables.

  Routing queries and broadcasting information on large scale platforms are tasks involving many basic message communications. The maximum performance objective imposes that basic messages are routed along paths of cost as low as possible. On the other hand, local routing decisions must be fast and the algorithms and data structures involved must support a certain amount of dynamism in the platform. Since the size of the data-structure impacts negatively the query and the update time, the space of the data-structure must be of limited size.

• Local computations.

  Although the size of the data structures is less constrained in comparison with P2P systems (due to security reasons), however, even in our collaborative framework, it is unrealistic that each node manages a complete view of the platform with the full resource characteristic. Thus, a node has to manage data structures concerning only a fraction of the whole system. In fact, a partial view of the network will be sufficient for many tasks: for instance, in order to compute the distance between two nodes a local and limited information available at the two nodes may suffice (distance labeling).

• Overlay and small world networks.

  The processes we consider can be highly dynamic. The preprocessing usually assumed takes polynomial time. Hence, when a new process arrives, it must be dealt with in an on-line fashion, i.e., we do not want to totally re-compute, and the (partial) re-computation has to be simple.

  In order to meet these requirements, overlay networks are normally implemented. These are light virtual networks, i.e., they are sparse and a local change of the physical network will only lead to a small change of the corresponding virtual network. As a result, small address books are sufficient at each node.

  A specific class of overlay networks are small-world networks. These are efficient overlay networks for (greedy) routing tasks assuming that distance requests can be performed easily.

• Mobile Agent Computing.

  Mobile Agent Computing has been proposed as a powerful paradigm to study distributed systems. Our purpose is to study the computational power of the mobile agent systems under various assumptions. Indeed, many models exist but little is known about their computational power. One major parameter describing a mobile agent model is the ability of the agents to interact.

  The most natural mobile agent computing problem is the exploration or mapping problem in which one or several mobile agents have to explore or map their environment. The rendezvous problem consists for two agents to meet at some unspecified node of the network. Two other fundamental problems deal with security, which is often the main concern of actual mobile agent systems. The first one consists in exploring the network in spite of harmful hosts that destroy incoming agents. An additional goal in this context is to locate the harmful host(s) to prevent further agent losses. We already mentioned the second problem related to security, which consists for the agents in capturing an intruder.

  The goal is to enlarge the knowledge on the foundations of mobile agent computing. This will be done by developing new efficient algorithms for mobile agent systems and by proving impossibility results. This will also allow to compare the different models.

Of course, the main difficulty is to adapt the maintenance of local data structures to the dynamism of the network.