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Background: Petroselinum crispum is a common vegetable or spice in Egypt and worldwide. It possess many pharmacological and medicinal properties. Aims: In the current research, the total phenolic and flavonoid contents as well as the antioxidant activities of P. crispum methanolic extract and its fractions were evaluated. Methodology: The total phenolic content was estimated by Folin-Ciocalteu method and total flavonoid content was tested by aluminum chloride assay. The antioxidant activity was evaluated by 1, 1-diphenyl-2-picryl hydrazyl radical (DPPH) assay, 2, 2ʹ-azino-bis (3-ethylbenzthiazoline-6sulphonic acid) assay (ABTS), and total antioxidant capacity assay. Results: The ethyl acetate fraction derived from the methanolic extract exhibited the highest total phenolic content (121.95±2.15, mg GAE/ g extract) and total flavonoids content (106.45±2.18 mg rutin equivalent / g extract). Furthermore, the ethyl acetate fraction demonstrated the more potent

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Task stragglers in MapReduce jobs dramatically impede job execution of data-intensive computing in cloud data centers. This impedance is due to the uneven distribution of input data, heterogeneous data nodes, resource contention situations, and network configurations. Data skew of intermediate data in MapReduce job causes delay failures due to the violation of job completion time. Data-intensive computing frameworks, such as MapReduce or Hadoop YARN, employ HashPartitioner. This partitioner may cause intermediate data skew, which results in straggler reducers. In this paper, we strive to make Hadoop YARN more efficient in cloud environments. We present, a new partitioning scheme, called balanced data clusters partitioner (BDCP), to handle straggler Reduce tasks based on sampling of input data and feedback information about the current processing task. Our extensive experimental results show that BDCP can outperform the default Hadoop HashPartitioner and Range partitioner. BDCP can assist in straggler mitigation during reduce phase and minimize the job completion time in MapReduce jobs within data-intensive cloud computing.

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