As real-time and immediate feedback becomes increasingly important in tasks related to mobile information, big data stream\nprocessing systems are increasingly applied to process massive amounts of mobile data. However, when processing a drastically\nfluctuating mobile data stream, the lack of an elastic resource-scheduling strategy limits the elasticity and scalability of data stream\nprocessing systems. To address this problem, this paper builds a flow-network model, a resource allocation model, and a data\nredistribution model as the foundation for proposing Flink with an elastic resource-scheduling strategy (Flink-ER), which consists\nof a capacity detection algorithm, an elastic resource reallocation algorithm, and a data redistribution algorithm. The strategy\nimproves the performance of the platform by dynamically rescaling the cluster and increasing the parallelism of operators based\non the processing load. Theexperimental results show that the throughput of a cluster was promoted under the premise of meeting\nlatency constraints, which verifies the efficiency of the strategy.
Loading....