580 research outputs found
A Lyapunov Optimization Approach to Repeated Stochastic Games
This paper considers a time-varying game with players. Every time slot,
players observe their own random events and then take a control action. The
events and control actions affect the individual utilities earned by each
player. The goal is to maximize a concave function of time average utilities
subject to equilibrium constraints. Specifically, participating players are
provided access to a common source of randomness from which they can optimally
correlate their decisions. The equilibrium constraints incentivize
participation by ensuring that players cannot earn more utility if they choose
not to participate. This form of equilibrium is similar to the notions of Nash
equilibrium and correlated equilibrium, but is simpler to attain. A Lyapunov
method is developed that solves the problem in an online \emph{max-weight}
fashion by selecting actions based on a set of time-varying weights. The
algorithm does not require knowledge of the event probabilities and has
polynomial convergence time. A similar method can be used to compute a standard
correlated equilibrium, albeit with increased complexity.Comment: 13 pages, this version fixes an incorrect statement of the previous
arxiv version (see footnote 1, page 5 in current version for the correction
Low Power Dynamic Scheduling for Computing Systems
This paper considers energy-aware control for a computing system with two
states: "active" and "idle." In the active state, the controller chooses to
perform a single task using one of multiple task processing modes. The
controller then saves energy by choosing an amount of time for the system to be
idle. These decisions affect processing time, energy expenditure, and an
abstract attribute vector that can be used to model other criteria of interest
(such as processing quality or distortion). The goal is to optimize time
average system performance. Applications of this model include a smart phone
that makes energy-efficient computation and transmission decisions, a computer
that processes tasks subject to rate, quality, and power constraints, and a
smart grid energy manager that allocates resources in reaction to a time
varying energy price. The solution methodology of this paper uses the theory of
optimization for renewal systems developed in our previous work. This paper is
written in tutorial form and develops the main concepts of the theory using
several detailed examples. It also highlights the relationship between online
dynamic optimization and linear fractional programming. Finally, it provides
exercises to help the reader learn the main concepts and apply them to their
own optimizations. This paper is an arxiv technical report, and is a
preliminary version of material that will appear as a book chapter in an
upcoming book on green communications and networking.Comment: 26 pages, 10 figures, single spac
Power Aware Wireless File Downloading: A Constrained Restless Bandit Approach
This paper treats power-aware throughput maximization in a multi-user file
downloading system. Each user can receive a new file only after its previous
file is finished. The file state processes for each user act as coupled Markov
chains that form a generalized restless bandit system. First, an optimal
algorithm is derived for the case of one user. The algorithm maximizes
throughput subject to an average power constraint. Next, the one-user algorithm
is extended to a low complexity heuristic for the multi-user problem. The
heuristic uses a simple online index policy and its effectiveness is shown via
simulation. For simple 3-user cases where the optimal solution can be computed
offline, the heuristic is shown to be near-optimal for a wide range of
parameters
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