Browsing by Author "Alayyoub, Mohammed"

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Büyük veride akışkan verileri işleyebilen sistemlerden Spark, Storm ve Flink'in karşılaştırmalı çalışması
(2016) Alayyoub, Mohammed; Yazıcı, Ali; Karakaya, Ziya; Software Engineering; 06. School Of Engineering; 01. Atılım University
Bu çalışmada, büyük veri konseptinde akışkan veri işleyebilen sistemlerden Apache Spark, Storm ve Flink karşılaştırarak incelenmektedir. Bu çalışmaya dahil edilen sistemler çeşitli durum ve şartlar altında ideal performanslarını gösterebilecek şekilde konfigüre edilmiş; ayrıca donanım kullanımları ve kullanılan donanım sayısının arttırılmasıyla oluşan ölçeklenebilirlik oranları değerlendirilmiştir. Bölüm 'Comparison of Stream Processing Frameworks' deki bulgular Flink'in eşit şartlar ve durumlar altında diğer sistemlerden daha iyi bir performans ortaya koyduğunu; bununla birlikte Spark'ın veri işleme gücü gecikmelerin göze alınabileceği şekilde konfigüre edildiğinde Flink'i geçebildiğini göstermektedir.
Citation - WoS: 27
A Comparison of Stream Processing Frameworks
(Ieee, 2017) Karakaya, Ziya; Yazici, Ali; Alayyoub, Mohammed; Computer Engineering; Software Engineering; 06. School Of Engineering; 01. Atılım University
This study compares the performance of Big Data Stream Processing frameworks including Apache Spark, Flink, and Storm. Also, it measures the resource usage and performance scalability of the frameworks against a varying number of cluster sizes. It has been observed that, Flink outperforms both Spark and Storm under equal constraints. However, Spark can be optimized to provide the higher throughput than Flink with the cost of higher latency.
Systematic Mapping for Big Data Stream Processing Frameworks
(Ieee, 2016) Alayyoub, Mohammed; Yazici, Ali; Karakaya, Ziya; Computer Engineering; Software Engineering; 06. School Of Engineering; 01. Atılım University
There has been lots of discussions about the choice of a stream processing framework (SPF) for Big Data. Each of the SPFs has different cutting edge technologies in their steps of processing the data in motion that gives them a better advantage over the others. Even though, the cutting edge technologies used in each stream processing framework might better them, it is still hard to say which framework bests the rest under different scenarios and conditions. in this study, we aim to show trends and differences about several SPFs for Big Data by using the Systematic Mapping (SM) approach. To achieve our objectives, we raise 6 research questions (RQs), in which 91 studies that conducted between 2010 and 2015 were evaluated. We present the trends by classifying the research on SPFs with respect to the proposed RQs which can help researchers to obtain an overview of the field.