PushBased Networkefficient Hadoop YARN Scheduling Mechanism for InMemory Computing In the big data era, dataintensive cluster computing systems like Hadoop, have gained much popularity, and YARN, the second generation of Hadoop becomes the general resource manager in the Hadoop ecosystem. In the distributed computing scenarios, data locality (scheduling tasks on where the data resides) is essential to the performance since higher data locality brings lower network transmission cost and higher throughput. However, we find that the native YARN scheduling mechanism has little data locality and the delay scheduling strategy leads to the longtail effect while achieving data locality for inmemory computing scenarios. Therefore, in this paper we propose the pushbased YARN scheduling mechanism for the inmemory computing environment. First, we classify the ResourceRequests into various categories. Then, we prune the nonlocal ResourceRequests to achieve fast datalocality inmemory computation. Finally, we push the left longtail ResourceRequests to the datalocality nodes to avoid the longtail effect. The experimental results demonstrate that the proposed scheduling mechanism achieves nearly 100% datalocality percentage comparing to the native YARN scheduling mechanism that only achieves 10%xcbx9c20% datalocality percentage. Under the identical datalocality percentage, the proposed pushbased scheduling mechanism promotes nearly 20% throughput and reduces nearly 10% application running time comparing to the existing delay scheduling mechanism used in YARN.

Benchmarking and Performance Modelling of MapReduce Communication Pattern Understanding and predicting the performance of big data applications running in the cloud or onpremises could help minimise the overall cost of operations and provide opportunities in efforts to identify performance bottlenecks. The complexity of the lowlevel internals of big data frameworks and the ubiquity of application and workload configuration parameters makes it challenging and expensive to come up with comprehensive performance modelling solutions. In this paper, instead of focusing on a wide range of configurable parameters, we studied the lowlevel internals of the MapReduce communication pattern and used a minimal set of performance drivers to develop a set of phase level parametric models for approximating the execution time of a given application on a given cluster. Models can be used to infer the performance of unseen applications and approximate their performance when an arbitrary dataset is used as input. Our approach is validated by running empirical experiments in two setups. On average, the error rate in both setups is xc2xb110% from the measured values.