Geförderte OA-Publikationen

Apache Spark jobs are often characterized by processing huge data sets and, therefore, require runtimes in the range of minutes to hours. Thus, being able to predict the runtime of such jobs would be useful not only to know when the job will finish, but also for scheduling purposes, to estimate monetary costs for cloud deployment, or to determine an appropriate cluster configuration, such as the number of nodes. However, predicting Spark job runtimes is much more challenging than for standard database queries: cluster configuration and parameters have a significant performance impact and jobs usually contain a lot of user-defined code making it difficult to estimate cardinalities and execution costs. In this paper, we present a gray-box modeling methodology for runtime prediction of Apache Spark jobs. Our approach comprises two steps: first, a white-box model for predicting the cardinalities of the input RDDs of each operator is built based on prior knowledge about the behavior and application parameters such as applied filters data, number of iterations, etc. In the second step, a black-box model for each task constructed by monitoring runtime metrics while varying allocated resources and input RDD cardinalities is used. We further show how to use this gray-box approach not only for predicting the runtime of a given job, but also as part of a decision model for reusing intermediate cached results of Spark jobs. Our methodology is validated with experimental evaluation showing a highly accurate prediction of the actual job runtime and a performance improvement if intermediate results can be reused.

Potassium-ion batteries (PIBs) configurated by organic electrodes have been identified as a promising alternative to lithium-ion batteries. Here, a porous organic PolyimideKetjenblack is demonstrated in PIBs as a cathode, which exhibits excellent performance with a large reversible capacity (143 mAh g^-1 at 100 mA g^-1), high rate capability (125 and 105 mAh g^-1 at 1000 and 5000 mA g^-1), and long cycling stability (76% capacity retention at 2000 mA g^-1 over 1000 cycles). The domination of fast capacitive-like reaction kinetics is verified, which benefits from the porous structure synthesized using in situ polymerization. Moreover, a renewable and low-cost full cell is demonstrated with superior rate behavior (106 mAh g^-1 at 3200 mA g^-1). This work proposes a strategy to design polymer electrodes for high-performance organic PIBs.

16S rRNA profiling has been applied for the investigation of bacterial communities of surface soil samples from forest-covered areas of ten prehistorical ramparts from different parts of Thuringia. Besides the majority bacterial types that are present in all samples, there could be identified bacteria that are highly abundant in some places and absent or low abundant in others. These differences are mainly related to the acidity of substrate and distinguish the communities of lime stone hills from soils of sand/quartzite and basalt hills. Minority components of bacterial communities show partially large differences that cannot be explained by the pH of the soil or incidental effects, only. They reflect certain relations between the communities of different places and could be regarded as a kind of signature-like patterns. Such relations had also been found in a comparison of the data from ramparts with formerly studied 16S rRNA profiling from an iron-age burial field. The observations are supporting the idea that a part of the components of bacterial communities from soil samples reflect their ecological history and can be understood as the "ecological memory" of a place. Probably such memory effects can date back to prehistoric times and might assist in future interpretations of archaeological findings on the prehistoric use of a place, on the one hand. On the other hand, the genetic profiling of soils of prehistoric places contributes to the evaluation of anthropogenic effects on the development of local soil bacterial diversity.