MediateSpace: applying contextual mediation to the tuple space paradigm

I designed, implemented and evaluated a decentralised context-aware content distribution middleware. It can support a variety of applications, with all network communication handled transparently behind a tuple space based interface. Content is inserted into the network with an associated condition stipulating the context that must be matched to receive it. Conditions can be expressed using conjunctions, disjunctions, a form of universal and existential quantification and nested block scopes. Conditions are mapped onto a set of spatial indexes to enable lookup; and these are inserted into a distributed multi-dimensional spatial data structure (e.g. an R-Tree). They are also translated into an OWL representation to enable evaluation. Nodes bind to their most geographically proximate neighbours which allows distance-sensitive context sharing. The middleware is capability-aware, pushing computationally expensive tasks onto more capable nodes. I evaluated my system through benchmarks and simulation, defining condition classes which collectively represent a large portion of the condition space. Random conditions were generated from these classes. Node mobility was controlled through a number of probability distributions. Benchmark evaluation times were reasonable, evaluating 500 typical messages in 1.4 seconds each. When the number of stored contexts were reduced, this improved dramatically, evaluating 500 much more complicated conditions in one-tenth of a second each. The number and complexity of context parameters has a major impact on efficiency. The number of spatial indexes generated was reasonable for most conditions, with a 95th percentile of 6. However, existential quantification was a challenge for both condition evaluation and index generation due to the potentially large number of possible combinations of conditions. As expected, simulations found that the distribution of workload was very uneven because nodes tend to cluster in large cities; meaning that most communication is localised within these areas. Also, node density had a dramatic impact on the number of received messages as nodes within sparse areas were unable to obtain context information which precluded condition evaluation. I achieved my research goals of developing a distributed context-aware content distribution framework

Enhancing Fault Tolerance of Distributed R-Tree

International audienceDistributed R-trees (DR-trees) are appealing infrastructures for implementing range queries, content based filtering or k-NN structures since they inherit the features of R-trees such as logarithmic height, bounded number of neighbors and balanced shape. However, they are crash-sensitivite since each single crash can potentially break the tree structure connectivity. In this article, we present a fault tolerant approach which exploits replication of non leaf nodes ensuring the tree connectivity in presence of crashes. Our contribution is twofold. First, we enhance the connectivity without modifying the R-tree structure. Second, via extensive simulations we prove that our approach drastically reduces the cost of both message traffic and stabilization time when compared to the original approach proposed in where all nodes of a disconnected subtree are reinserted. Finally, our approach can be easily extended to other crash-sensitive structures