GPU-Based Heuristic Solver for Linear Sum Assignment Problems under Real-time Constraints

In this thesis, we explore the use of a centrally-coordinated peer-to-peer overlay as a possible solution to the live streaming problem. Our contribution lies in showing that such approach is indeed feasible given that a number of key challenges are met. The motivation behind exploring an alternative design is that, although a number of approaches have been investigated in the past, e.g. mesh-pull and tree-push, hybrids and best-of-both-worlds mesh-push, no consensus has been reached on the best solution for the problem of peer-to-peer live streaming, despite current deployments and reported successes. In the proposed system, we model sender/receiver peer assignments as an optimization problem. Optimized peer selection based on multiple utility factors, such as bandwidth availability, delays and connectivity compatibility, make it possible to achieve large source bandwidth savings and provide high quality of user experience. Clear benefits of our approach are observed when Network Address Translation constraints are present on the network. We have addressed key scalability issues of our platform by parallelizing the heuristic which is the core of our optimization engine and by implementing the resulting algorithm on commodity Graphic Processing Units (GPUs). The outcome is a Linear Sum Assignment Problem (LSAP) solver for time-constrained systems which produces near-optimal results and can be used for any instance of LSAP, i.e. not only in our system. As part of this work, we also present our experience in working with Network Address Translators (NATs) traversal in peer-to-peer systems. Our contribution in this context is threefold. First, we provide a semi-formal model of state of the art NAT behaviors. Second, we use our model to show which NAT combinations can be theoretically traversed and which not. Last, for each of the combinations, we state which traversal technique should be used. Our findings are confirmed by experimental results on a real network. Finally, we address the problem of reproducibility in testing, debugging and evaluation of our peer-to-peer application. We achieve this by providing a software framework which can be transparently integrated with any already-existing software and which is able to handle concurrency, system time and network events in a reproducible manner.