Maximum Sustainable Throughput Evaluation Using an Adaptive Method for Stream Processing Platforms

作者：Zheng Chu, Jiong Yu, Askar Hamdull · 发表于：IEEE Access · 年份：2020 · DOI：10.1109/access.2020.2976738 · 被引用次数：10 · 研究领域：Data Stream Mining Techniques、Cloud Computing and Resource Management、Mobile Crowdsensing and Crowdsourcing

The volume and type of streaming data is increasing rapidly, thus, real-time processing scenarios for streaming data have continued to increase. The inherent volatility of streaming data causes large changes in the throughput volatility of stream processing platforms, making evaluations of the maximum sustainable throughput (MST) for such platforms a challenge. To address these problems and improve the low efficiency of manual evaluations, a naïve method is typically used to evaluate the MST of platforms by periodically measuring throughput. The throughput volatility is detected using a skip sliding window that is defined as a bounded sequence in which a limited number of throughput values stored to measure volatility in a skipping fashion in each data growth cycle, which is a fixed time interval with respect to the continuous data rate increases. A skipping sliding window that is used to describe the procedure with which the MST is calculated in an approximate way is based on a sliding window, which is used usually in the context of stateful streaming operators. Then, according to the throughput volatility, the method judges whether the MST has been reached using a latency guarantee. However, because this method has low efficiency and a large error rate, we propose an adaptive MST evaluation method that adds a data-growth factor function to the naïve method cycle that dynamically and adaptively tunes the data rate for each data growth cycle. The experimental results from fou...