Distributed Storage Management of Evolving Files in Delay Tolerant Ad Hoc Networks

This work focuses on a class of distributed storage systems whose content may evolve over time. Each component or node of the storage system is mobile and the set of all nodes forms a delay tolerant (ad hoe) network (DTN). The goal of the paper is to study efficient ways for distributing evolving files within DTNs and for managing dynamically their content. We specify to dynamic files where not only the latest version is useful but also previous ones; we restrict however to files where a file has no use if another more recent version is available. The DTN is composed of fixed number of nodes including a single source. At some points in time the source makes available a new version of a single file F. We consider both the cases when (a) nodes do not cooperate and (b) nodes cooperate. In case (a) only the source may transmit a copy of F to a node that it meets, while in case (b) any node may transmit a copy of F to a node that it meets. Scenario (a) is studied under the assumption that the source updates F at discrete times t = 0, 1,..... Within each slot [t, t + 1) there is a fixed probability that a node meets the source. A file management policy is a set of rules specifying when the source transmits a copy of F to a node (say node i) that it meets; this decision only depends on the age of the version of F (if any) that node i is carrying, where the age is k if this version was created k - 1 slots ago. We find the optimal static (resp. dynamic) policy which maximizes a general utility function under a constraint on the number of transmissions within a slot. In particular, we show the existence of a threshold dynamic policy. In scenario (b) F is updated at random points in time. Similar to scenerio (a) we assume that each node knows the age of the file it carries (the case where nodes only know the date of creation of a file is studied in [1]). Under Markovian assumptions regarding nodes mobility and update frequency of F, we study the stability of the system (aging of the nodes) and derive an (approximate) optimal static policy. We then revisit scenario (a) when the source does not know the number of nodes and the probability that the source meets a node in a slot, and we derive a stochastic approximation algorithm which we show to converge to the optimal static policy found in the complete information setting. Numerical results illustrate the respective performance of optimal static and dynamic policies as well as the benefit of node cooperation.