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Communication networks are used today everywhere and on every scale: startingfrom small Internet of Things (IoT) networks at home, via campus and enterprisenetworks, and up to tier-one networks of Internet providers. Accordingly,network devices should support a plethora of tasks with highly heterogeneouscharacteristics in terms of processing time, bandwidth energy consumption,deadlines and so on. Evaluating these characteristics and the amount ofcurrently available resources for handling them requires analyzing all thearriving inputs, gathering information from numerous remote devices, andintegrating all this information. Performing all these tasks in real time isvery challenging in today's networking environments, which are characterized bytight bounds on the latency, and always-increasing data rates. Hence, networkalgorithms should typically make decisions under uncertainty.  This work addresses optimizing performance in heterogeneous and uncertainnetworking environments. We begin by detailing the sources of heterogeneity anduncertainty and show that uncertainty appears in all layers of network design,including the time required to perform a task; the amount of availableresources; and the expected gain from successfully completing a task. Next, wesurvey current solutions and show their limitations. Based on these insights wedevelop general design concepts to tackle heterogeneity and uncertainty, andthen use these concepts to design practical algorithms. For each of ouralgorithms, we provide rigorous mathematical analysis, thus showing worst-caseperformance guarantees. Finally, we implement and run the suggested algorithmson various input traces, thus obtaining further insights as to our algorithmicdesign principles.

Advanced Algorithms in Heterogeneous and Uncertain Networking Environments