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Utility functions provide a natural and advantageous framework for achieving self-optimization in distributed autonomic computing systems. We present a distributed architecture, implemented in a realistic prototype data center, that demonstrates how utility functions can enable a collection of autonomic elements to continually optimize the use of(More)
Server farms today consume more than 1.5% of the total electricity in the U.S. at a cost of nearly $4.5 billion. Given the rising cost of energy, many industries are now seeking solutions for how to best make use of their available power. An important question which arises in this context is how to distribute available power among servers in a server farm(More)
The Continuous Double Auction (CDA) is the dominant market institution for real-world trading of equities, commodities, derivatives, etc. We describe a series of laboratory experiments that, for the first time, allow human subjects to interact with software bidding agents in a CDA. Our bidding agents use strategies based on extensions of the(More)
We develop a model for analyzing complex games with repeated interactions, for which a full game-theoretic analysis is intractable. Our approach treats exogenously specified, heuristic strategies, rather than the atomic actions, as primitive, and computes a heuristic-payoff table specifying the expected payoffs of the joint heuristic strategy space. We(More)
Self-management in accordance with high-level objectives that users can specify is a hallmark of autonomic computing systems. The authors advocate utility functions as a principled, practical, and general way of representing such objectives. In an effort to bring the promise of utility-based frameworks to the marketplace, they describe how they've(More)
We review recent work done by our group on applying genetic algorithms (GAs) to the design of cellular automata (CAs) that can perform computations requiring global coordination. A GA was used to evolve CAs for two computational tasks: density classi cation and synchronization. In both cases, the GA discovered rules that gave rise to sophisticated emergent(More)
Continuous space word embeddings learned from large, unstructured corpora have been shown to be effective at capturing semantic regularities in language. In this paper we replace LDA’s parameterization of “topics” as categorical distributions over opaque word types with multivariate Gaussian distributions on the embedding space. This encourages the model to(More)
Reinforcement Learning (RL) provides a promising new approach to systems performance management that differs radically from standard queuing-theoretic approaches making use of explicit system performance models. In principle, RL can automatically learn high-quality management policies without an explicit performance model or traffic model and with little or(More)