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The Internet gateway selection problem is becoming very important as the number of Internet-connected devices increases and stresses the limited number of Internet gateway nodes. The gateway nodes often experience frequent performance fluctuations, and the best gateway selection candidate changes frequently with growing network dynamics. We propose GateSelect, a customized selection algorithm for each client node that not only provides the best-effort selection candidate but also ensures the global, balanced distribution of the gateway nodes. We utilize over the counter, lightweight calculations to optimize the client-side selection algorithm by combining classification, short term performance prediction, and randomized selection. We compare our algorithm with several baseline algorithms, and the experiment results show that our proposal provides better performance and balanced distribution of gateway nodes.

Modern large-scale networked services, such as video streaming, are typically deployed at multiple locations in the network to provide redundancy and load balancing. Different techniques are used to provide performance monitoring information so that client nodes can select the best service instance. One of them is collaborative sensing, where clients share measurement results on the observed service performance to build a common ground of knowledge with low overhead. Clients can then use this common ground to select the most suitable service provider. However, collaborative algorithms are susceptible to false measurements sent by malfunctioning or malicious nodes, which decreases the accuracy of the performance sensing process. We propose Sense-Share, a simple light-weight and resilient collaborative sensing framework based on the similarity of the client nodes' perception of service performance. Our experimental evaluation in different topologies shows that service performance sensing using Sense-Share achieves, on average, 94% similarity to non-collaborative brute force performance sensing, tolerating faulty nodes. Furthermore, our approach effectively distributes the service monitoring requests over the service nodes and exploits direct inter-node communication to share measurements, resulting in reduced monitoring overhead.