Comparative Evaluation for the Performance of Big Stream Processing Systems

Andmete hulk kasvab tänapäeval meeletu kiirusega ning seda andmete hulka tuleb korrektselt töödelda, et saavutada kontroll andmete üle. Antud olukord sunnib meid mõtlema andmevoo töötlemise peale. Enamasti nõuavad andmemahuline pettuse tuvastus-, kaubandus-, tootmis-, sõjanduse ja luure süsteemid pidevat andmete analüüsi (reaalajas). Sellist tüüpi süsteemid nõuavad kõrgetasemel ist mustrite sobitamist ja korrelatsioone. Aja jooksul on ilmnenud erinevaid andmevoo töötlemise võimalusi. Antud lõputöös tehakse jõudlustest Apache Flink, Apache Storm, Heron, Kafka ja Apache Spark andmevoo töötlemismootoritega ning tulemusi võrreldakse ja vastandatakse omavahel. Nendes rakendustes ja domeenides on väga oluline nõue koguda, menetleda ning analüüsida olulisi andmevooge, et eraldada sealt väärtusliku informatsiooni. Antud magistritöö eesmärk on läbi viia empiiriline hindamine ning võrdlemine kõrgtasemel andmevoo töötlemissüsteemide vahel.Nowadays data is growing with tremendous acceleration, and this growing data must be processed properly if we want to have control over it. It pushes us to think about data stream processing. Most of the time, a data-intensive fraud detecting, trading, manufacturing, military and intelligence systems require processing data immediately (real-time). These kinds of systems need considerably ssophisticated pattern matching and correlations. However, other uses of stream processing have also emerged over time. In this thesis, we will benchmark to compare and contrast Apache Flink, Apache Storm, Heron, Kafka an Apache Spark stream processing engines. In these applications and domains, there is a crucial requirement to collect, process, and analyze significant streams of data to extract valuable information. This thesis aims to conduct an empirical evaluation and benchmarking of the state-of-the-art of big stream processing systems

MLPerf Inference Benchmark

Machine-learning (ML) hardware and software system demand is burgeoning. Driven by ML applications, the number of different ML inference systems has exploded. Over 100 organizations are building ML inference chips, and the systems that incorporate existing models span at least three orders of magnitude in power consumption and five orders of magnitude in performance; they range from embedded devices to data-center solutions. Fueling the hardware are a dozen or more software frameworks and libraries. The myriad combinations of ML hardware and ML software make assessing ML-system performance in an architecture-neutral, representative, and reproducible manner challenging. There is a clear need for industry-wide standard ML benchmarking and evaluation criteria. MLPerf Inference answers that call. In this paper, we present our benchmarking method for evaluating ML inference systems. Driven by more than 30 organizations as well as more than 200 ML engineers and practitioners, MLPerf prescribes a set of rules and best practices to ensure comparability across systems with wildly differing architectures. The first call for submissions garnered more than 600 reproducible inference-performance measurements from 14 organizations, representing over 30 systems that showcase a wide range of capabilities. The submissions attest to the benchmark's flexibility and adaptability.Comment: ISCA 202

BigDataBench: a Big Data Benchmark Suite from Internet Services

As architecture, systems, and data management communities pay greater attention to innovative big data systems and architectures, the pressure of benchmarking and evaluating these systems rises. Considering the broad use of big data systems, big data benchmarks must include diversity of data and workloads. Most of the state-of-the-art big data benchmarking efforts target evaluating specific types of applications or system software stacks, and hence they are not qualified for serving the purposes mentioned above. This paper presents our joint research efforts on this issue with several industrial partners. Our big data benchmark suite BigDataBench not only covers broad application scenarios, but also includes diverse and representative data sets. BigDataBench is publicly available from http://prof.ict.ac.cn/BigDataBench . Also, we comprehensively characterize 19 big data workloads included in BigDataBench with varying data inputs. On a typical state-of-practice processor, Intel Xeon E5645, we have the following observations: First, in comparison with the traditional benchmarks: including PARSEC, HPCC, and SPECCPU, big data applications have very low operation intensity; Second, the volume of data input has non-negligible impact on micro-architecture characteristics, which may impose challenges for simulation-based big data architecture research; Last but not least, corroborating the observations in CloudSuite and DCBench (which use smaller data inputs), we find that the numbers of L1 instruction cache misses per 1000 instructions of the big data applications are higher than in the traditional benchmarks; also, we find that L3 caches are effective for the big data applications, corroborating the observation in DCBench.Comment: 12 pages, 6 figures, The 20th IEEE International Symposium On High Performance Computer Architecture (HPCA-2014), February 15-19, 2014, Orlando, Florida, US

CHORUS Deliverable 2.2: Second report - identification of multi-disciplinary key issues for gap analysis toward EU multimedia search engines roadmap

After addressing the state-of-the-art during the first year of Chorus and establishing the existing landscape in multimedia search engines, we have identified and analyzed gaps within European research effort during our second year. In this period we focused on three directions, notably technological issues, user-centred issues and use-cases and socio- economic and legal aspects. These were assessed by two central studies: firstly, a concerted vision of functional breakdown of generic multimedia search engine, and secondly, a representative use-cases descriptions with the related discussion on requirement for technological challenges. Both studies have been carried out in cooperation and consultation with the community at large through EC concertation meetings (multimedia search engines cluster), several meetings with our Think-Tank, presentations in international conferences, and surveys addressed to EU projects coordinators as well as National initiatives coordinators. Based on the obtained feedback we identified two types of gaps, namely core technological gaps that involve research challenges, and “enablers”, which are not necessarily technical research challenges, but have impact on innovation progress. New socio-economic trends are presented as well as emerging legal challenges

Scalability Benchmarking of Cloud-Native Applications Applied to Event-Driven Microservices

Cloud-native applications constitute a recent trend for designing large-scale software systems. This thesis introduces the Theodolite benchmarking method, allowing researchers and practitioners to conduct empirical scalability evaluations of cloud-native applications, their frameworks, configurations, and deployments. The benchmarking method is applied to event-driven microservices, a specific type of cloud-native applications that employ distributed stream processing frameworks to scale with massive data volumes. Extensive experimental evaluations benchmark and compare the scalability of various stream processing frameworks under different configurations and deployments, including different public and private cloud environments. These experiments show that the presented benchmarking method provides statistically sound results in an adequate amount of time. In addition, three case studies demonstrate that the Theodolite benchmarking method can be applied to a wide range of applications beyond stream processing

BDEv 3.0: energy efficiency and microarchitectural characterization of Big Data processing frameworks

This is a post-peer-review, pre-copyedit version of an article published in Future Generation Computer Systems. The final authenticated version is available online at: https://doi.org/10.1016/j.future.2018.04.030[Abstract] As the size of Big Data workloads keeps increasing, the evaluation of distributed frameworks becomes a crucial task in order to identify potential performance bottlenecks that may delay the processing of large datasets. While most of the existing works generally focus only on execution time and resource utilization, analyzing other important metrics is key to fully understanding the behavior of these frameworks. For example, microarchitecture-level events can bring meaningful insights to characterize the interaction between frameworks and hardware. Moreover, energy consumption is also gaining increasing attention as systems scale to thousands of cores. This work discusses the current state of the art in evaluating distributed processing frameworks, while extending our Big Data Evaluator tool (BDEv) to extract energy efficiency and microarchitecture-level metrics from the execution of representative Big Data workloads. An experimental evaluation using BDEv demonstrates its usefulness to bring meaningful information from popular frameworks such as Hadoop, Spark and Flink.Ministerio de Economía, Industria y Competitividad; TIN2016-75845-PMinisterio de Educación; FPU14/02805Ministerio de Educación; FPU15/0338