With the demand to process ever-growing data volumes, a variety of new data stream processing frameworks have been developed. Moving an implementation from one such system to another, e.g., for performance reasons, requires adapting existing applications to new interfaces. Apache Beam addresses these high substitution costs by providing an abstraction layer that enables executing programs on any of the supported streaming frameworks. In this paper, we present a novel benchmark architecture for comparing the performance impact of using Apache Beam on three streaming frameworks: Apache Spark Streaming, Apache Flink, and Apache Apex. We find significant performance penalties when using Apache Beam for application development in the surveyed systems. Overall, usage of Apache Beam for the examined streaming applications caused a high variance of query execution times with a slowdown of up to a factor of 58 compared to queries developed without the abstraction layer. All developed benchmark artifacts are publicly available to ensure reproducible results.
With respect to benchmarking DSPSs in general, the Linear Road benchmark by @cite_42 is a very well-known work. It is an application benchmark that provides a benchmarking toolkit. This toolkit consists of a data generator, a data sender, and a result validator. The underlying idea of the benchmark is a variable tolling system for a metropolitan area. This area covers multiple expressways with moving vehicles. The amount of accumulated tolls depends on various aspects concerning the traffic situation.
@cite_42
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This paper specifies the Linear Road Benchmark for Stream Data Management Systems (SDMS). Stream Data Management Systems process streaming data by executing continuous and historical queries while producing query results in real-time. This benchmark makes it possible to compare the performance characteristics of SDMS' relative to each other and to alternative (e.g., Relational Database) systems. Linear Road has been endorsed as an SDMS benchmark by the developers of both the Aurora [1] (out of Brandeis University, Brown University and MIT) and STREAM [8] (out of Stanford University) stream systems. Linear Road simulates a toll system for the motor vehicle expressways of a large metropolitan area. The tolling system uses "variable tolling" [6, 11, 9]: an increasingly prevalent tolling technique that uses such dynamic factors as traffic congestion and accident proximity to calculate toll charges. Linear Road specifies a variable tolling system for a fictional urban area including such features as accident detection and alerts, traffic congestion measurements, toll calculations and historical queries. After specifying the benchmark, we describe experimental results involving two implementations: one using a commercially available Relational Database and the other using Aurora. Our results show that a dedicated Stream Data Management System can outperform a Relational Database by at least a factor of 5 on streaming data applications. In this paper, we combine the most complete record of daily mobility, based on large-scale mobile phone data, with detailed Geographic Information System (GIS) data, uncovering previously hidden patterns in urban road usage. We find that the major usage of each road segment can be traced to its own - surprisingly few - driver sources. Based on this finding we propose a network of road usage by defining a bipartite network framework, demonstrating that in contrast to traditional approaches, which define road importance solely by topological measures, the role of a road segment depends on both: its betweeness and its degree in the road usage network. Moreover, our ability to pinpoint the few driver sources contributing to the major traffic flow allows us to create a strategy that achieves a significant reduction of the travel time across the entire road system, compared to a benchmark approach. This paper describes a system for detecting and estimating the properties of multiple travel lanes in an urban road network from calibrated video imagery and laser range data acquired by a moving vehicle. The system operates in real-time in several stages on multiple processors, fusing detected road markings, obstacles, and curbs into a stable non-parametric estimate of nearby travel lanes. The system incorporates elements of a provided piecewise-linear road network as a weak prior. Our method is notable in several respects: it detects and estimates multiple travel lanes; it fuses asynchronous, heterogeneous sensor streams; it handles high-curvature roads; and it makes no assumption about the position or orientation of the vehicle with respect to the road. We analyze the system's performance in the context of the 2007 DARPA Urban Challenge. With five cameras and thirteen lidars, our method was incorporated into a closed-loop controller to successfully guide an autonomous vehicle through a 90 km urban course at speeds up to 40 km h amidst moving traffic. Recently, big data has been evolved into a buzzword from academia to industry all over the world. Benchmarks are important tools for evaluating an IT system. However, benchmarking big data systems is much more challenging than ever before. First, big data systems are still in their infant stage and consequently they are not well understood. Second, big data systems are more complicated compared to previous systems such as a single node computing platform. While some researchers started to design benchmarks for big data systems, they do not consider the redundancy between their benchmarks. Moreover, they use artificial input data sets rather than real world data for their benchmarks. It is therefore unclear whether these benchmarks can be used to precisely evaluate the performance of big data systems. In this paper, we first analyze the redundancy among benchmarks from ICTBench, HiBench and typical workloads from real world applications: spatio-temporal data analysis for Shenzhen transportation system. Subsequently, we present an initial idea of a big data benchmark suite for spatio-temporal data. There are three findings in this work: (1) redundancy exists in these pioneering benchmark suites and some of them can be removed safely. (2) The workload behavior of trajectory data analysis applications is dramatically affected by their input data sets. (3) The benchmarks created for academic research cannot represent the cases of real world applications.