172,991 research outputs found
Large-scale Spatial Distribution Identification of Base Stations in Cellular Networks
The performance of cellular system significantly depends on its network
topology, where the spatial deployment of base stations (BSs) plays a key role
in the downlink scenario. Moreover, cellular networks are undergoing a
heterogeneous evolution, which introduces unplanned deployment of smaller BSs,
thus complicating the performance evaluation even further. In this paper, based
on large amount of real BS locations data, we present a comprehensive analysis
on the spatial modeling of cellular network structure. Unlike the related
works, we divide the BSs into different subsets according to geographical
factor (e.g. urban or rural) and functional type (e.g. macrocells or
microcells), and perform detailed spatial analysis to each subset. After
examining the accuracy of Poisson point process (PPP) in BS locations modeling,
we take into account the Gibbs point processes as well as Neyman-Scott point
processes and compare their accuracy in view of large-scale modeling test.
Finally, we declare the inaccuracy of the PPP model, and reveal the general
clustering nature of BSs deployment, which distinctly violates the traditional
assumption. This paper carries out a first large-scale identification regarding
available literatures, and provides more realistic and more general results to
contribute to the performance analysis for the forthcoming heterogeneous
cellular networks
Exploring emerging technologies for extreme scale HPC architectures
While architectures and programming models have remained relatively stable for almost two decades, new architectural features, such as heterogeneous processing, nonvolatile memory, and optical interconnection networks, will demand that software systems and applications be redesigned so that they expose massive amounts of hierarchical parallelism, carefully orchestrate data movement, and balance concerns over performance, power, and resiliency. This instability has led to two inevitable problems: decreased programmer productivity, and difficult performance prediction. In this talk, I will describe two solutions to these problems, respectively: our OpenARC compiler and runtime system, and our Aspen performance modeling language. First, OpenARC is a research compiler that supports OpenACC and OpenMP4, and can generate code in CUDA, OpenCL, and LLVM IR. OpenARC has enabled us to explore how to enable performance portability of applications across diverse architectures. Second, Aspen is a domain specific language for structured analytical modeling of applications and architectures. It is designed to enable rapid exploration of new algorithm and architectures. Once created, Aspen models can then be used for a variety of purposes including predicting performance of future applications, evaluating system architectures, informing runtime scheduling decisions, and identifying system anomalies
Statistical Analysis of Video Frame Size Distribution Originating from Scalable Video Codec (SVC)
Designing an effective and high performance network requires an accurate characterization and modeling of network traffic.The
modeling of video frame sizes is normally applied in simulation studies and mathematical analysis and generating streams for
testing and compliance purposes. Besides, video traffic assumed as a major source of multimedia traffic in future heterogeneous
network. Therefore, the statistical distribution of video data can be used as the inputs for performance modeling of networks. The
finding of this paper comprises the theoretical definition of distribution which seems to be relevant to the video trace in terms of
its statistical properties and finds the best distribution using both the graphical method and the hypothesis test.The data set used
in this article consists of layered video traces generating from Scalable Video Codec (SVC) video compression technique of three
different movies
Influence of various application types on the performance of LTE mobile networks
Modern mobile internet networks are becoming heavier and denser. Also it is not regularly planned, and becoming more heterogeneous. The explosive growth in the usage of smartphones poses numerous challenges for LTE cellular networks design and implementation. The performance of LTE networks with bursty and self-similar traffic has become a major challenge. Accurate modeling of the data generated by each connected wireless device is important for properly investigating the performance of LTE networks. This paper presents a mathematical model for LTE networks using queuing theory considering the influence of various application types. Using sporadic source traffic feeding to the queue of the evolved node B and with the exponential service time assumption, we construct a queuing model to estimate the performance of LTE networks. We use the performance model presented in this paper to study the influence of various application categories on the performance of LTE cellular networks. Also we validate our model with simulation using NS3 simulator with different scenarios
On Modeling Heterogeneous Wireless Networks Using Non-Poisson Point Processes
Future wireless networks are required to support 1000 times higher data rate,
than the current LTE standard. In order to meet the ever increasing demand, it
is inevitable that, future wireless networks will have to develop seamless
interconnection between multiple technologies. A manifestation of this idea is
the collaboration among different types of network tiers such as macro and
small cells, leading to the so-called heterogeneous networks (HetNets).
Researchers have used stochastic geometry to analyze such networks and
understand their real potential. Unsurprisingly, it has been revealed that
interference has a detrimental effect on performance, especially if not modeled
properly. Interference can be correlated in space and/or time, which has been
overlooked in the past. For instance, it is normally assumed that the nodes are
located completely independent of each other and follow a homogeneous Poisson
point process (PPP), which is not necessarily true in real networks since the
node locations are spatially dependent. In addition, the interference
correlation created by correlated stochastic processes has mostly been ignored.
To this end, we take a different approach in modeling the interference where we
use non-PPP, as well as we study the impact of spatial and temporal correlation
on the performance of HetNets. To illustrate the impact of correlation on
performance, we consider three case studies from real-life scenarios.
Specifically, we use massive multiple-input multiple-output (MIMO) to
understand the impact of spatial correlation; we use the random medium access
protocol to examine the temporal correlation; and we use cooperative relay
networks to illustrate the spatial-temporal correlation. We present several
numerical examples through which we demonstrate the impact of various
correlation types on the performance of HetNets.Comment: Submitted to IEEE Communications Magazin
Exploring Student Check-In Behavior for Improved Point-of-Interest Prediction
With the availability of vast amounts of user visitation history on
location-based social networks (LBSN), the problem of Point-of-Interest (POI)
prediction has been extensively studied. However, much of the research has been
conducted solely on voluntary checkin datasets collected from social apps such
as Foursquare or Yelp. While these data contain rich information about
recreational activities (e.g., restaurants, nightlife, and entertainment),
information about more prosaic aspects of people's lives is sparse. This not
only limits our understanding of users' daily routines, but more importantly
the modeling assumptions developed based on characteristics of recreation-based
data may not be suitable for richer check-in data. In this work, we present an
analysis of education "check-in" data using WiFi access logs collected at
Purdue University. We propose a heterogeneous graph-based method to encode the
correlations between users, POIs, and activities, and then jointly learn
embeddings for the vertices. We evaluate our method compared to previous
state-of-the-art POI prediction methods, and show that the assumptions made by
previous methods significantly degrade performance on our data with dense(r)
activity signals. We also show how our learned embeddings could be used to
identify similar students (e.g., for friend suggestions).Comment: published in KDD'1
BrainFrame: A node-level heterogeneous accelerator platform for neuron simulations
Objective: The advent of High-Performance Computing (HPC) in recent years has
led to its increasing use in brain study through computational models. The
scale and complexity of such models are constantly increasing, leading to
challenging computational requirements. Even though modern HPC platforms can
often deal with such challenges, the vast diversity of the modeling field does
not permit for a single acceleration (or homogeneous) platform to effectively
address the complete array of modeling requirements. Approach: In this paper we
propose and build BrainFrame, a heterogeneous acceleration platform,
incorporating three distinct acceleration technologies, a Dataflow Engine, a
Xeon Phi and a GP-GPU. The PyNN framework is also integrated into the platform.
As a challenging proof of concept, we analyze the performance of BrainFrame on
different instances of a state-of-the-art neuron model, modeling the Inferior-
Olivary Nucleus using a biophysically-meaningful, extended Hodgkin-Huxley
representation. The model instances take into account not only the neuronal-
network dimensions but also different network-connectivity circumstances that
can drastically change application workload characteristics. Main results: The
synthetic approach of three HPC technologies demonstrated that BrainFrame is
better able to cope with the modeling diversity encountered. Our performance
analysis shows clearly that the model directly affect performance and all three
technologies are required to cope with all the model use cases.Comment: 16 pages, 18 figures, 5 table
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