29,984 research outputs found
BDGS: A Scalable Big Data Generator Suite in Big Data Benchmarking
Data generation is a key issue in big data benchmarking that aims to generate
application-specific data sets to meet the 4V requirements of big data.
Specifically, big data generators need to generate scalable data (Volume) of
different types (Variety) under controllable generation rates (Velocity) while
keeping the important characteristics of raw data (Veracity). This gives rise
to various new challenges about how we design generators efficiently and
successfully. To date, most existing techniques can only generate limited types
of data and support specific big data systems such as Hadoop. Hence we develop
a tool, called Big Data Generator Suite (BDGS), to efficiently generate
scalable big data while employing data models derived from real data to
preserve data veracity. The effectiveness of BDGS is demonstrated by developing
six data generators covering three representative data types (structured,
semi-structured and unstructured) and three data sources (text, graph, and
table data)
DeepMasterPrints: Generating MasterPrints for Dictionary Attacks via Latent Variable Evolution
Recent research has demonstrated the vulnerability of fingerprint recognition
systems to dictionary attacks based on MasterPrints. MasterPrints are real or
synthetic fingerprints that can fortuitously match with a large number of
fingerprints thereby undermining the security afforded by fingerprint systems.
Previous work by Roy et al. generated synthetic MasterPrints at the
feature-level. In this work we generate complete image-level MasterPrints known
as DeepMasterPrints, whose attack accuracy is found to be much superior than
that of previous methods. The proposed method, referred to as Latent Variable
Evolution, is based on training a Generative Adversarial Network on a set of
real fingerprint images. Stochastic search in the form of the Covariance Matrix
Adaptation Evolution Strategy is then used to search for latent input variables
to the generator network that can maximize the number of impostor matches as
assessed by a fingerprint recognizer. Experiments convey the efficacy of the
proposed method in generating DeepMasterPrints. The underlying method is likely
to have broad applications in fingerprint security as well as fingerprint
synthesis.Comment: 8 pages; added new verification systems and diagrams. Accepted to
conference Biometrics: Theory, Applications, and Systems 201
Performance Testing of Distributed Component Architectures
Performance characteristics, such as response time, throughput andscalability, are key quality attributes of distributed applications. Current practice,however, rarely applies systematic techniques to evaluate performance characteristics.We argue that evaluation of performance is particularly crucial in early developmentstages, when important architectural choices are made. At first glance, thiscontradicts the use of testing techniques, which are usually applied towards the endof a project. In this chapter, we assume that many distributed systems are builtwith middleware technologies, such as the Java 2 Enterprise Edition (J2EE) or theCommon Object Request Broker Architecture (CORBA). These provide servicesand facilities whose implementations are available when architectures are defined.We also note that it is the middleware functionality, such as transaction and persistenceservices, remote communication primitives and threading policy primitives,that dominates distributed system performance. Drawing on these observations, thischapter presents a novel approach to performance testing of distributed applications.We propose to derive application-specific test cases from architecture designs so thatthe performance of a distributed application can be tested based on the middlewaresoftware at early stages of a development process. We report empirical results thatsupport the viability of the approach
Benchmarking Distributed Stream Data Processing Systems
The need for scalable and efficient stream analysis has led to the
development of many open-source streaming data processing systems (SDPSs) with
highly diverging capabilities and performance characteristics. While first
initiatives try to compare the systems for simple workloads, there is a clear
gap of detailed analyses of the systems' performance characteristics. In this
paper, we propose a framework for benchmarking distributed stream processing
engines. We use our suite to evaluate the performance of three widely used
SDPSs in detail, namely Apache Storm, Apache Spark, and Apache Flink. Our
evaluation focuses in particular on measuring the throughput and latency of
windowed operations, which are the basic type of operations in stream
analytics. For this benchmark, we design workloads based on real-life,
industrial use-cases inspired by the online gaming industry. The contribution
of our work is threefold. First, we give a definition of latency and throughput
for stateful operators. Second, we carefully separate the system under test and
driver, in order to correctly represent the open world model of typical stream
processing deployments and can, therefore, measure system performance under
realistic conditions. Third, we build the first benchmarking framework to
define and test the sustainable performance of streaming systems.
Our detailed evaluation highlights the individual characteristics and
use-cases of each system.Comment: Published at ICDE 201
- ā¦