In this study, we investigated the problem of scheduling streaming applications on a heterogeneous cluster environment and, based on our previous work, developed the maximum throughput scheduler algorithm (MT-Scheduler) for streaming applications. The proposed algorithm uses a dynamic programming technique to efficiently map the application topology onto the heterogeneous distributed system based on computing and data transfer requirements, while also taking into account the capacity of the underlying cluster resources. The proposed approach maximizes the system throughput by identifying and minimizing the time incurred at the computing/transfer bottleneck. The MT-Scheduler supports scheduling applications structured as a directed acyclic graph. We conducted experiments using three Storm microbenchmark topologies in both simulation and real Apache Storm environments. In terms of the performance evaluation , we compared the proposed MT-Scheduler with the simulated round robin and the default Storm scheduler algorithms. The results indicated that the MT-Scheduler outperforms the default round robin approach in terms of both the average system latency and throughput.

中文翻译：

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异构分布式处理系统中流式应用程序的作业调度程序

在这项研究中，我们研究了在异构集群环境中调度流应用程序的问题，并基于我们之前的工作，开发了流应用程序的最大吞吐量调度器算法（MT-Scheduler）。所提出的算法使用动态编程技术，根据计算和数据传输需求，将应用拓扑有效地映射到异构分布式系统上，同时还考虑了底层集群资源的容量。所提出的方法通过识别和最小化在计算/传输瓶颈处发生的时间来最大化系统吞吐量。MT-Scheduler 支持结构化为有向无环图的调度应用程序。我们在模拟和真实 Apache Storm 环境中使用三个 Storm 微基准拓扑进行了实验。在性能评估方面，我们将所提出的 MT-Scheduler 与模拟循环和默认 Storm 调度器算法进行了比较。结果表明，MT-Scheduler 在平均系统延迟和吞吐量方面都优于默认的循环方法。