Distributed computing engines, such as Spark, are widely used to process the large volumes of data collected by the Internet of Things (IoT) devices. In IoT systems, computing nodes often exhibit significant heterogeneity. However, most existing task scheduling algorithms neglect the performance differences among system nodes and statically assign tasks based on data locality. When high-performance nodes complete local tasks rapidly, they are subsequently assigned massive non-local tasks, resulting in severe network congestion and performance degradation. To address these issues, we propose a dynamic resource utilization-aware task scheduling strategy (DRUTS), which can efficiently utilize idle bandwidth and high-performance computing resources while also ensuring data locality. Firstly, considering the time-varying characteristics of heterogeneous node performance under various workloads, we use a sliding window to process the task information stream and evaluate the relative performance of nodes in real-time. Then, our proposed strategy adopts weighted random methods to dynamically select taskprovidersandreceiversby combining the cluster network load. Finally, it migrates and pre-executes tasks to high-performance nodes based on data distribution. We evaluate our proposed strategy performance using six typical workloads on two types of real-world heterogeneous clusters. The experimental results clearly demonstrate that our proposed strategy not only improves CPU and network utilization by 15.7% and 13.2%, respectively, but also reduces application execution time by 37.2% compared to existing work.

In heterogeneous distributed computing platforms, task execution containers (e.g., Spark executors) often have significant performance differences. However, most task schedulers greedily utilize resources based on the first-release-first-use policy. This leads to load imbalance across heterogeneous executors and poor application performance. To solve the above problem, we first construct a heterogeneous system task execution model. Then, we formalize the load-balancing task scheduling problem in heterogeneous environments as a minimum weighted executor waiting time problem and prove its NP-hardness. Next, a remaining workload-aware task scheduling strategy is proposed to address load imbalance among heterogeneous executors. Additionally, considering the startup overhead difference in executors, we introduce an earliest available executor wait mechanism to further optimize load-balancing. We comprehensively evaluate our proposed approaches using seven typical applications in two real-world heterogeneous environments. The experimental results clearly demonstrate that our approaches not only improve cluster load-balancing but also achieve up to 35.5% performance improvement.