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Distributed dataflow systems like MapReduce, Spark, and Flink help users in analyzing large datasets with a set of cluster resources. Performance modeling and runtime prediction is then used for automatically allocating resources for specific performance goals. However, the actual performance of distributed dataflow jobs can vary significantly due to factors like interference with co-located workloads,...
Resource management systems like YARN or Mesos enable users to share cluster infrastructures by running analytics jobs in temporarily reserved containers. These containers are typically not isolated to achieve high degrees of overall resource utilizations despite the often fluctuating resource usage of single analytic jobs. However, some combinations of jobs utilize the resources better and interfere...
The performance of scalable analytic frameworks supporting data-intensive parallel applications often depends significantly on the time it takes to read input data. Therefore, existing frameworks like Spark and Flink try to achieve a high degree of data locality by scheduling tasks on nodes where the input data resides. However, the set of nodes running a job and its tasks is chosen by a cluster resource...
Distributed dataflow systems like Spark or Flink enable users to analyze large datasets. Users create programs by providing sequential user-defined functions for a set of well-defined operations, select a set of resources, and the systems automatically distribute the jobs across these resources. However, selecting resources for specific performance needs is inherently difficult and users consequently...
Caused by the proliferation of the (IoT) and its related application domains such as Building Automation or E-Health, users face a continuously increasing amount of heterogeneous sensors and devices deployed to their environment. As a result, a large variety of protocols, data formats and physical sensing resources needs to be managed in order to gain benefit from the deployed devices. This raises...
Sharing cluster resources between multiple frameworks, applications and datasets is important for organizations doing large scale data analytics. It improves cluster utilization, avoids standalone clusters running only a single framework and allows data scientists to choose the best framework for each analysis task. Current systems for cluster resource management like YARN or Mesos achieve resource...
The pervasiveness of connected embedded devices and Internet of Things (IoT) related application domains like smart cities, e-Health or, transportation lead to an constantly increasing amount of data, compute- and storage resources surrounding us. However, currently there is a gap between data acquisition and processing, usually bridged by gateway based approaches that integrate the devices and forward...
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