45,558 research outputs found
A stochastic approximation algorithm for stochastic semidefinite programming
Motivated by applications to multi-antenna wireless networks, we propose a
distributed and asynchronous algorithm for stochastic semidefinite programming.
This algorithm is a stochastic approximation of a continous- time matrix
exponential scheme regularized by the addition of an entropy-like term to the
problem's objective function. We show that the resulting algorithm converges
almost surely to an -approximation of the optimal solution
requiring only an unbiased estimate of the gradient of the problem's stochastic
objective. When applied to throughput maximization in wireless multiple-input
and multiple-output (MIMO) systems, the proposed algorithm retains its
convergence properties under a wide array of mobility impediments such as user
update asynchronicities, random delays and/or ergodically changing channels.
Our theoretical analysis is complemented by extensive numerical simulations
which illustrate the robustness and scalability of the proposed method in
realistic network conditions.Comment: 25 pages, 4 figure
Trace checking of Metric Temporal Logic with Aggregating Modalities using MapReduce
Modern complex software systems produce a large amount of execution data,
often stored in logs. These logs can be analyzed using trace checking
techniques to check whether the system complies with its requirements
specifications. Often these specifications express quantitative properties of
the system, which include timing constraints as well as higher-level
constraints on the occurrences of significant events, expressed using aggregate
operators. In this paper we present an algorithm that exploits the MapReduce
programming model to check specifications expressed in a metric temporal logic
with aggregating modalities, over large execution traces. The algorithm
exploits the structure of the formula to parallelize the evaluation, with a
significant gain in time. We report on the assessment of the implementation -
based on the Hadoop framework - of the proposed algorithm and comment on its
scalability.Comment: 16 pages, 6 figures, Extended version of the SEFM 2014 pape
Scather: programming with multi-party computation and MapReduce
We present a prototype of a distributed computational infrastructure, an associated high level programming language, and an underlying formal framework that allow multiple parties to leverage their own cloud-based computational resources (capable of supporting MapReduce [27] operations) in concert with multi-party computation (MPC) to execute statistical analysis algorithms that have privacy-preserving properties. Our architecture allows a data analyst unfamiliar with MPC to: (1) author an analysis algorithm that is agnostic with regard to data privacy policies, (2) to use an automated process to derive algorithm implementation variants that have different privacy and performance properties, and (3) to compile those implementation variants so that they can be deployed on an infrastructures that allows computations to take place locally within each participant’s MapReduce cluster as well as across all the participants’ clusters using an MPC protocol. We describe implementation details of the architecture, discuss and demonstrate how the formal framework enables the exploration of tradeoffs between the efficiency and privacy properties of an analysis algorithm, and present two example applications that illustrate how such an infrastructure can be utilized in practice.This work was supported in part by NSF Grants: #1430145, #1414119, #1347522, and #1012798
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