As more applications rely on distributed systems (peer-to-peer services, content distribution networks, cloud services), it becomes necessary to identify hosts that return content to the user with minimal delay. A large scale map of delays would aid in solving this problem. Existing methods, which deploy devices to every region of the Internet or use of a single vantage point have yet to create such a map. While services such as PlanetLab offer a distributed network for measurements, they only cover 0.3% of the Internet. The focus of our research is to increase the speed of the single vantage point approach so that it becomes a feasible solution. We evaluate the feasibility of performing large scale measurements by performing an experiment using more hosts than any previous study. First, an efficient scanning algorithm is developed to perform the measurement scan. We then find that a custom Windows network driver is required to overcome bottlenecks in the operating system. After developing a custom driver, we perform a measurement scan larger than any previous study. Analysis of the results reveals previously unidentified drawbacks to the existing architectures and measurement methodologies. We propose novel meth- ods for increasing the speed of experiments, improving the accuracy of measurement results, and reducing the amount of traffic generated by the scan. Finally, we present architectures for performing an Internet scale measurement scan. We found that with custom drivers, the Windows operating system is a capable platform for performing large scale measurements. Scan results showed that in the eleven years since the original measurement technique was developed, the response patterns it relied upon had changed from what was expected. With our suggested improvements to the measurement algorithm and proposed scanning architectures, it may be possible to perform Internet scale measurement studies in the future.

Computer network complexity has increased in the last decades due to the introduction of various concepts, leaving network maintainers in hardship to manage such huge and tangled networks. In this study, we aim to aid service providers to optimize and automate their networks. Currently, network maintainers perform a vast number of explicit measurements, which has a negative effect on the network’s health and stability. Depending on the service’s nature, measurements are either made at service initiation as in the case of server-client selection or continuously done to monitor the quality of service as in the case of quality assurance applications. We intend to apply artificial intelligence to minimize the dependency on such explicit measurements and hence, optimize the network with minimal cost. From the two types of applications, we focus on distributed delay measurements for Esports server-client selection problem as well as network automation and failure mitigation task done by Internet service providers. In large-scale networks, it is impractical to measure the delay between every node explicitly. As a result, we propose an AI-based delay measurement estimator system. The system’s inputs are just the source and destination nodes’ IP-addresses. Network maintainers continuously monitor their network status to detect any sudden change in the network and take suitable action(s) to keep the network in the best conditions. We propose an ML-based action recommender engine that is able to identify the current network status and suggest a set of actions that restore the network to its optimum state.