Large scale platform : Instantiable models and algorithmic design of communication schemes. (Modélisation des communications sur plates-formes à grande echelles)

The increasing popularity of Internet bandwidth-intensive applications prompts us to consider followingproblem: How to compute efficient collective communication schemes on large-scale platform? The issue of designing a collective communication in the context of a large scale distributed networkis a difficult and a multi-level problem. A lot of solutions have been extensively studied andproposed. But a new, comprehensive and systematic approach is required, that combines networkmodels and algorithmic design of solutions.In this work we advocate the use of models that are able to capture real-life network behavior,but also are simple enough that a mathematical analysis of their properties and the design of optimalalgorithms is achievable.First, we consider the problem of the measuring available bandwidth for a given point-topointconnection. We discuss how to obtain reliable datasets of bandwidth measurements usingPlanetLab platform, and we provide our own datasets together with the distributed software usedto obtain it. While those datasets are not a part of our model per se, they are necessary whenevaluating the performance of various network algorithms. Such datasets are common for latencyrelatedproblems, but very rare when dealing with bandwidth-related ones.Then, we advocate for a model that tries to accurately capture the capabilities of a network,named LastMile model. This model assumes that essentially the congestion happens at the edgesconnecting machines to the wide Internet. It has a natural consequence in a bandwidth predictionalgorithm based on this model. Using datasets described earlier, we prove that this algorithm is ableto predict with an accuracy comparable to best known network prediction algorithm (DistributedMatrix Factorization) available bandwidth between two given nodes. While we were unable toimprove upon DMF algorithm in the field of point-to-point prediction, we show that our algorithmhas a clear advantage coming from its simplicity, i.e. it naturally extends to the network predictionsunder congestion scenario (multiple connections sharing a bandwidth over a single link). We areactually able to show, using PlanetLab datasets, that LastMile prediction is better in such scenarios.In the third chapter, we propose new algorithms for solving the large scale broadcast problem.We assume that the network is modeled by the LastMile model. We show that under thisassumption, we are able to provide algorithms with provable, strong approximation ratios. Takingadvantage of the simplicity and elasticity of the model, we can even extend it, so that it captures theidea of connectivity artifacts, in our case firewalls preventing some nodes to communicate directlybetween each other. In the extended case we are also able to provide approximation algorithmswith provable performance.The chapters 1 to 3 form three successful steps of our program to develop from scratch amathematical network communication model, prove it experimentally, and show that it can beapplied to develop algorithms solving hard problems related to design of communication schemesin networks.In the chapter 4 we show how under different network cost models, using some simplifyingassumptions on the structure of network and queries, one can design very efficient communicationschemes using simple combinatorial techniques. This work is complementary to the previous chapter in the sense that previously when designing communication schemes, we assumed atomicityof connections, i.e. that we have no control over routing of simple connections. In chapter 4 weshow how to solve the problem of an efficient routing of network request, given that we know thetopology of the network. It shows the importance of instantiating the parameters and the structureof the network in the context of designing efficient communication schemes.