340,395 research outputs found
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
MIMO throughput effectiveness for basic MIMO OTA compliance testing
During the March 2011 meeting of the CTIA MIMO OTA Subgroup (MOSG), the members agreed that the subgroup should first determine “what” aspects of a MIMO-capable device require evaluation; then the group should determine “how” to go about making these measurements. In subsequent meetings of MOSG, new yet-unnamed figures of merit were asked for in order to provide a solution to the carriers' requirements for LTE MIMO OTA evaluation. Furthermore, the December 2011 3GPP RAN4 status report on LTE MIMO OTA listed the evaluation of the use of statistical performance analysis in order to minimize test time and help ensure accurate performance assessment as an open issue. This contribution addresses these petitions by providing four new figures of merit, which could serve the purpose of evaluating the operators' top priorities for MIMO OTA compliance testing. The new figures of merit are MIMO Throughput Effectiveness (MTE), MIMO Device Throughput Effectiveness (MDTE), MIMO Throughput Gain (MTG), and MIMO Device Throughput Gain (MDTG). In this paper, MTE is evaluated using the recently available LTE MIMO OTA RR data from 3GPP
Formal Representation of the SS-DB Benchmark and Experimental Evaluation in EXTASCID
Evaluating the performance of scientific data processing systems is a
difficult task considering the plethora of application-specific solutions
available in this landscape and the lack of a generally-accepted benchmark. The
dual structure of scientific data coupled with the complex nature of processing
complicate the evaluation procedure further. SS-DB is the first attempt to
define a general benchmark for complex scientific processing over raw and
derived data. It fails to draw sufficient attention though because of the
ambiguous plain language specification and the extraordinary SciDB results. In
this paper, we remedy the shortcomings of the original SS-DB specification by
providing a formal representation in terms of ArrayQL algebra operators and
ArrayQL/SciQL constructs. These are the first formal representations of the
SS-DB benchmark. Starting from the formal representation, we give a reference
implementation and present benchmark results in EXTASCID, a novel system for
scientific data processing. EXTASCID is complete in providing native support
both for array and relational data and extensible in executing any user code
inside the system by the means of a configurable metaoperator. These features
result in an order of magnitude improvement over SciDB at data loading,
extracting derived data, and operations over derived data.Comment: 32 pages, 3 figure
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Accuracy and interpretability trade-offs in machine learning applied to safer gambling
Responsible gambling is an area of research and industry which seeks to understand the pathways to harm from gambling and implement programmes to reduce or prevent harm that gambling might cause. There is a growing body of research that has used gambling behavioural data to model and predict harmful gambling, and the industry is showing increasing interest in technologies that can help gambling operators to better predict harm and prevent it through appropriate interventions. However, industry surveys and feedback clearly indicate that in order to enable wider adoption of such data-driven methods, industry and policy makers require a greater understanding of how machine learning methods make these predictions. In this paper, we make use of the TREPAN algorithm for extracting decision trees from Neural Networks and Random Forests. We present the first comparative evaluation of predictive performance and tree properties for extracted trees, which is also the first comparative evaluation of knowledge extraction for safer gambling. Results indicate that TREPAN extracts better performing trees than direct learning of decision trees from the data. Overall, trees extracted with TREPAN from different models offer a good compromise between prediction accuracy and interpretability. TREPAN can produce decision trees with extended tests rules of different forms, so that interpretability depends on multiple factors. We present detailed results and a discussion of the trade-offs with regard to performance and interpretability and use in the gambling industry
Building Efficient Query Engines in a High-Level Language
Abstraction without regret refers to the vision of using high-level
programming languages for systems development without experiencing a negative
impact on performance. A database system designed according to this vision
offers both increased productivity and high performance, instead of sacrificing
the former for the latter as is the case with existing, monolithic
implementations that are hard to maintain and extend. In this article, we
realize this vision in the domain of analytical query processing. We present
LegoBase, a query engine written in the high-level language Scala. The key
technique to regain efficiency is to apply generative programming: LegoBase
performs source-to-source compilation and optimizes the entire query engine by
converting the high-level Scala code to specialized, low-level C code. We show
how generative programming allows to easily implement a wide spectrum of
optimizations, such as introducing data partitioning or switching from a row to
a column data layout, which are difficult to achieve with existing low-level
query compilers that handle only queries. We demonstrate that sufficiently
powerful abstractions are essential for dealing with the complexity of the
optimization effort, shielding developers from compiler internals and
decoupling individual optimizations from each other. We evaluate our approach
with the TPC-H benchmark and show that: (a) With all optimizations enabled,
LegoBase significantly outperforms a commercial database and an existing query
compiler. (b) Programmers need to provide just a few hundred lines of
high-level code for implementing the optimizations, instead of complicated
low-level code that is required by existing query compilation approaches. (c)
The compilation overhead is low compared to the overall execution time, thus
making our approach usable in practice for compiling query engines
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A GA-based technique for the scheduling of storage tanks
YesThis paper proposes the application of a
genetic algorithm based methodology for the scheduling
of storage tanks. The proposed approach is an
integration of GA and heuristic rule-based techniques,
which decomposes the complex mixed integer
optimisation problem into integer and real number subproblems.
The GA string considers the integer problem,
and the heuristic approach solves the real number
problems within the GA framework. The algorithm is
demonstrated for a test problem related to a water
treatment facility at a port, and has been found to give a
significantly better schedule than those generated using a
heuristic-based approach
Target oriented relational model finding
Lecture Notes in Computer Science 8411, 2014Model finders are becoming useful in many software engineering problems. Kodkod is one of the most popular, due to its support for relational logic (a combination of first order logic with relational algebra operators and transitive closure), allowing a simpler specification of constraints, and support for partial instances, allowing the specification of a priori (exact, but potentially partial) knowledge about a problem's solution. However, in some software engineering problems, such as model repair or bidirectional model transformation, knowledge about the solution is not exact, but instead there is a known target that the solution should approximate. In this paper we extend Kodkod's partial instances to allow the specification of such targets, and show how its model finding procedure can be adapted to support them (using both PMax-SAT solvers or SAT solvers with cardinality constraints). Two case studies are also presented, including a careful performance evaluation to assess the effectiveness of the proposed extension.(undefined
BriskStream: Scaling Data Stream Processing on Shared-Memory Multicore Architectures
We introduce BriskStream, an in-memory data stream processing system (DSPSs)
specifically designed for modern shared-memory multicore architectures.
BriskStream's key contribution is an execution plan optimization paradigm,
namely RLAS, which takes relative-location (i.e., NUMA distance) of each pair
of producer-consumer operators into consideration. We propose a branch and
bound based approach with three heuristics to resolve the resulting nontrivial
optimization problem. The experimental evaluations demonstrate that BriskStream
yields much higher throughput and better scalability than existing DSPSs on
multi-core architectures when processing different types of workloads.Comment: To appear in SIGMOD'1
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