31,291 research outputs found
Partitioned Sampling of Public Opinions Based on Their Social Dynamics
Public opinion polling is usually done by random sampling from the entire
population, treating individual opinions as independent. In the real world,
individuals' opinions are often correlated, e.g., among friends in a social
network. In this paper, we explore the idea of partitioned sampling, which
partitions individuals with high opinion similarities into groups and then
samples every group separately to obtain an accurate estimate of the population
opinion. We rigorously formulate the above idea as an optimization problem. We
then show that the simple partitions which contain only one sample in each
group are always better, and reduce finding the optimal simple partition to a
well-studied Min-r-Partition problem. We adapt an approximation algorithm and a
heuristic algorithm to solve the optimization problem. Moreover, to obtain
opinion similarity efficiently, we adapt a well-known opinion evolution model
to characterize social interactions, and provide an exact computation of
opinion similarities based on the model. We use both synthetic and real-world
datasets to demonstrate that the partitioned sampling method results in
significant improvement in sampling quality and it is robust when some opinion
similarities are inaccurate or even missing
Dominant Resource Fairness in Cloud Computing Systems with Heterogeneous Servers
We study the multi-resource allocation problem in cloud computing systems
where the resource pool is constructed from a large number of heterogeneous
servers, representing different points in the configuration space of resources
such as processing, memory, and storage. We design a multi-resource allocation
mechanism, called DRFH, that generalizes the notion of Dominant Resource
Fairness (DRF) from a single server to multiple heterogeneous servers. DRFH
provides a number of highly desirable properties. With DRFH, no user prefers
the allocation of another user; no one can improve its allocation without
decreasing that of the others; and more importantly, no user has an incentive
to lie about its resource demand. As a direct application, we design a simple
heuristic that implements DRFH in real-world systems. Large-scale simulations
driven by Google cluster traces show that DRFH significantly outperforms the
traditional slot-based scheduler, leading to much higher resource utilization
with substantially shorter job completion times
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