Adaptive Schedulers for Deadline-Constrained Content Upload from Mobile Multihomed Vehicles

We consider the practical problem of video surveillance in public transport systems, where security videos are stored onboard, and a central operator occasionally needs to access portions of the recordings. When this happens, the video portion must be uploaded within a deadline, possibly using multiple parallel wireless interfaces. Interfaces have different associated costs, related to tariffs charged by Mobile Network Operators (MNOs), energy consumption, data quotas, system load. Our goal is to choose which interfaces to use, and when, so as to minimize the cost of the upload while meeting the deadline, despite the unknown short-term variations in throughput. To achieve this goal, we first collected real traces of mobile uploads for different MNOs from vehicles. Examination of these traces confirms the unpredictability of the short-term throughput of wireless connections, and motivates the adoption of adaptive schedulers with very limited a-priori knowledge of the system status. To effectively solve our problem, we devised a family of adaptive algorithms, that we thoroughly evaluated using a trace-driven approach. Results show that our adaptive approach can effectively leverage the fundamental tradeoff between the total cost and the delivery time of content upload, despite unknown short-term variations in throughput.