249,877 research outputs found
Benchmarking communication middleware for cloud computing virtualizers
REACTION 2013. 2nd International Workshop on Real-time and distributed computing in emerging applications. December 3rd, 2013, Vancouver, Canada.Virtualization technologies typically introduce additional
overhead that is specially challenging for specific domains
such as real-time systems. One of the sources of overhead are
the additional software layers that provide parallel execution
environments which reduce the effective performance given by
the infrastructure. This work identifies the factors to be analysed
by a benchmark for performance evaluation of a virtualized
middleware. It provides the set of benchmark tests that evaluate
empirically the overhead and stability on a trendy communication
middleware, DDS (Data Distribution System for Real-Time),
which enables message transmissions via publisher-subscriber
(P/S) interactions. Two different implementations, RTI and
OpenSplice, have been analysed over a general purpose virtual
machine monitor to evaluate their behavior on a client-server
application. Obtained results have provided initial execution clues
on the performance that a virtualized communication middleware
like DDS can exhibit
Stochastic Subgradient Algorithms for Strongly Convex Optimization over Distributed Networks
We study diffusion and consensus based optimization of a sum of unknown
convex objective functions over distributed networks. The only access to these
functions is through stochastic gradient oracles, each of which is only
available at a different node, and a limited number of gradient oracle calls is
allowed at each node. In this framework, we introduce a convex optimization
algorithm based on the stochastic gradient descent (SGD) updates. Particularly,
we use a carefully designed time-dependent weighted averaging of the SGD
iterates, which yields a convergence rate of
after gradient updates for each node on
a network of nodes. We then show that after gradient oracle calls, the
average SGD iterate achieves a mean square deviation (MSD) of
. This rate of convergence is optimal as it
matches the performance lower bound up to constant terms. Similar to the SGD
algorithm, the computational complexity of the proposed algorithm also scales
linearly with the dimensionality of the data. Furthermore, the communication
load of the proposed method is the same as the communication load of the SGD
algorithm. Thus, the proposed algorithm is highly efficient in terms of
complexity and communication load. We illustrate the merits of the algorithm
with respect to the state-of-art methods over benchmark real life data sets and
widely studied network topologies
Efficient FFT Algorithms for Mobile Devices
Increased traffic on wireless communication infrastructure has exacerbated the limited availability of radio frequency ({RF}) resources. Spectrum sharing is a possible solution to this problem that requires devices equipped with Cognitive Radio ({CR}) capabilities. A widely employed technique to enable {CR} is real-time {RF} spectrum analysis by applying the Fast Fourier Transform ({FFT}). Today’s mobile devices actually provide enough computing resources to perform not only the {FFT} but also wireless communication functions and protocols by software according to the software-defined radios paradigm. In addition to that, the pervasive availability of mobile devices make them powerful computing platform for new services. This thesis studies the feasibility of using mobile devices as a novel spectrum sensing platform with focus on {FFT}-based spectrum sensing algorithms. We benchmark several open-source {FFT} libraries on an Android smartphone. We relate the efficiency of calculating the {FFT} to both algorithmic and implementation-related aspects. The benchmark results also show the clear potential of special {FFT} algorithms that are tailored for sparse spectrum detection
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