Following [4] we extend and generalize the game-theoretic model of distributed computing, identifying different utility functions that encompass different potential preferences of players in a distributed system. A good distributed algorithm in the game-theoretic context is one that prohibits the agents (processors with interests) from deviating from the protocol; any deviation would result in the agent losing, i.e., reducing its utility at the end of the algorithm. We distinguish between different utility functions in the context of distributed algorithms, e.g., utilities based on communication preference, solution preference, and output preference. Given these preferences we construct two basic building blocks for game theoretic distributed algorithms, a wake-up building block resilient to any preference and in particular to the communication preference (to which previous wake-up solutions were not resilient), and a knowledge sharing building block that is resilient to any and in particular to solution and output preferences. Using the building blocks we present several new algorithms for consensus, and renaming as well as a modular presentation of the leader election algorithm of [4].

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Efficient algorithms and techniques to detect and identify large flows in a high throughput traffic stream in the SDN matchand-action model are presented. This is in contrast to previous work that either deviated from the match and action model by requiring additional switch level capabilities or did not exploit the SDN data plane. Our construction has two parts; (a) how to sample in an SDN match and action model, (b) how to detect large flows efficiently and in a scalable way, in the SDN model.Our large flow detection methods provide high accuracy and present a good and practical tradeoff between switch -controller traffic, and the number of entries required in the switch flow table. Based on different parameters, we differentiate between heavy flows, elephant flows and bulky flows and present efficient algorithms to detect flows of the different types.Additionally, as part of our heavy flow detection scheme, we present sampling methods to sample packets with arbitrary probability p per packet or per byte that traverses an SDN switch.Finally, we show how our algorithms can be adapted to a distributed monitoring SDN setting with multiple switches, and easily scale with the number of monitoring switches.

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