28,840 research outputs found
Towards Data-driven Simulation of End-to-end Network Performance Indicators
Novel vehicular communication methods are mostly analyzed simulatively or
analytically as real world performance tests are highly time-consuming and
cost-intense. Moreover, the high number of uncontrollable effects makes it
practically impossible to reevaluate different approaches under the exact same
conditions. However, as these methods massively simplify the effects of the
radio environment and various cross-layer interdependencies, the results of
end-to-end indicators (e.g., the resulting data rate) often differ
significantly from real world measurements. In this paper, we present a
data-driven approach that exploits a combination of multiple machine learning
methods for modeling the end-to-end behavior of network performance indicators
within vehicular networks. The proposed approach can be exploited for fast and
close to reality evaluation and optimization of new methods in a controllable
environment as it implicitly considers cross-layer dependencies between
measurable features. Within an example case study for opportunistic vehicular
data transfer, the proposed approach is validated against real world
measurements and a classical system-level network simulation setup. Although
the proposed method does only require a fraction of the computation time of the
latter, it achieves a significantly better match with the real world
evaluations
Transferable knowledge for Low-cost Decision Making in Cloud Environments
Users of Infrastructure as a Service (IaaS) are increasingly overwhelmed with the wide range of providers and services offered by each
provider. As such, many users select services based on description alone. An emerging alternative is to use a decision support system (DSS), which
typically relies on gaining insights from observational data in order to assist a customer in making decisions regarding optimal deployment of cloud
applications. The primary activity of such systems is the generation of a prediction model (e.g. using machine learning), which requires a significantly
large amount of training data. However, considering the varying architectures of applications, cloud providers, and cloud offerings, this activity is
not sustainable as it incurs additional time and cost to collect data to train the models. We overcome this through developing a Transfer Learning (TL)
approach where knowledge (in the form of a prediction model and associated data set) gained from running an application on a particular IaaS is
transferred in order to substantially reduce the overhead of building new models for the performance of new applications and/or cloud infrastructures.
In this paper, we present our approach and evaluate it through extensive experimentation involving three real world applications over two major public
cloud providers, namely Amazon and Google. Our evaluation shows that our novel two-mode TL scheme increases overall efficiency with a factor of
60% reduction in the time and cost of generating a new prediction model. We test this under a number of cross-application and cross-cloud scenario
Report from GI-Dagstuhl Seminar 16394: Software Performance Engineering in the DevOps World
This report documents the program and the outcomes of GI-Dagstuhl Seminar
16394 "Software Performance Engineering in the DevOps World".
The seminar addressed the problem of performance-aware DevOps. Both, DevOps
and performance engineering have been growing trends over the past one to two
years, in no small part due to the rise in importance of identifying
performance anomalies in the operations (Ops) of cloud and big data systems and
feeding these back to the development (Dev). However, so far, the research
community has treated software engineering, performance engineering, and cloud
computing mostly as individual research areas. We aimed to identify
cross-community collaboration, and to set the path for long-lasting
collaborations towards performance-aware DevOps.
The main goal of the seminar was to bring together young researchers (PhD
students in a later stage of their PhD, as well as PostDocs or Junior
Professors) in the areas of (i) software engineering, (ii) performance
engineering, and (iii) cloud computing and big data to present their current
research projects, to exchange experience and expertise, to discuss research
challenges, and to develop ideas for future collaborations
Using Machine Learning for Handover Optimization in Vehicular Fog Computing
Smart mobility management would be an important prerequisite for future fog
computing systems. In this research, we propose a learning-based handover
optimization for the Internet of Vehicles that would assist the smooth
transition of device connections and offloaded tasks between fog nodes. To
accomplish this, we make use of machine learning algorithms to learn from
vehicle interactions with fog nodes. Our approach uses a three-layer
feed-forward neural network to predict the correct fog node at a given location
and time with 99.2 % accuracy on a test set. We also implement a dual stacked
recurrent neural network (RNN) with long short-term memory (LSTM) cells capable
of learning the latency, or cost, associated with these service requests. We
create a simulation in JAMScript using a dataset of real-world vehicle
movements to create a dataset to train these networks. We further propose the
use of this predictive system in a smarter request routing mechanism to
minimize the service interruption during handovers between fog nodes and to
anticipate areas of low coverage through a series of experiments and test the
models' performance on a test set
A Survey of Prediction and Classification Techniques in Multicore Processor Systems
In multicore processor systems, being able to accurately predict the future provides new optimization opportunities, which otherwise could not be exploited. For example, an oracle able to predict a certain application\u27s behavior running on a smart phone could direct the power manager to switch to appropriate dynamic voltage and frequency scaling modes that would guarantee minimum levels of desired performance while saving energy consumption and thereby prolonging battery life. Using predictions enables systems to become proactive rather than continue to operate in a reactive manner. This prediction-based proactive approach has become increasingly popular in the design and optimization of integrated circuits and of multicore processor systems. Prediction transforms from simple forecasting to sophisticated machine learning based prediction and classification that learns from existing data, employs data mining, and predicts future behavior. This can be exploited by novel optimization techniques that can span across all layers of the computing stack. In this survey paper, we present a discussion of the most popular techniques on prediction and classification in the general context of computing systems with emphasis on multicore processors. The paper is far from comprehensive, but, it will help the reader interested in employing prediction in optimization of multicore processor systems
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