1,346 research outputs found
Asymptotically optimal load balancing in large-scale heterogeneous systems with multiple dispatchers
We consider the load balancing problem in large-scale heterogeneous systems with multiple dispatchers. We introduce a general framework called Local-Estimation-Driven (LED). Under this framework, each dispatcher keeps local (possibly outdated) estimates of the queue lengths for all the servers, and the dispatching decision is made purely based on these local estimates. The local estimates are updated via infrequent communications between dispatchers and servers. We derive sufficient conditions for LED policies to achieve throughput optimality and delay optimality in heavy-traffic, respectively. These conditions directly imply delay optimality for many previous local-memory based policies in heavy traffic. Moreover, the results enable us to design new delay optimal policies for heterogeneous systems with multiple dispatchers. Finally, the heavy-traffic delay optimality of the LED framework also sheds light on a recent open question on how to design optimal load balancing schemes using delayed information
The Empirical Implications of Privacy-Aware Choice
This paper initiates the study of the testable implications of choice data in
settings where agents have privacy preferences. We adapt the standard
conceptualization of consumer choice theory to a situation where the consumer
is aware of, and has preferences over, the information revealed by her choices.
The main message of the paper is that little can be inferred about consumers'
preferences once we introduce the possibility that the consumer has concerns
about privacy. This holds even when consumers' privacy preferences are assumed
to be monotonic and separable. This motivates the consideration of stronger
assumptions and, to that end, we introduce an additive model for privacy
preferences that does have testable implications
Generalizing Kronecker graphs in order to model searchable networks
This paper describes an extension to stochastic
Kronecker graphs that provides the special structure required
for searchability, by defining a “distance”-dependent Kronecker
operator. We show how this extension of Kronecker graphs
can generate several existing social network models, such as
the Watts-Strogatz small-world model and Kleinberg’s latticebased
model. We focus on a specific example of an expanding
hypercube, reminiscent of recently proposed social network
models based on a hidden hyperbolic metric space, and prove
that a greedy forwarding algorithm can find very short paths
of length O((log log n)^2) for graphs with n nodes
Distance-Dependent Kronecker Graphs for Modeling Social Networks
This paper focuses on a generalization of stochastic
Kronecker graphs, introducing a Kronecker-like operator and
defining a family of generator matrices H dependent on distances
between nodes in a specified graph embedding. We prove
that any lattice-based network model with sufficiently small
distance-dependent connection probability will have a Poisson
degree distribution and provide a general framework to prove
searchability for such a network. Using this framework, we focus
on a specific example of an expanding hypercube and discuss
the similarities and differences of such a model with recently
proposed network models based on a hidden metric space. We
also prove that a greedy forwarding algorithm can find very short
paths of length O((log log n)^2) on the hypercube with n nodes,
demonstrating that distance-dependent Kronecker graphs can
generate searchable network models
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