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Deployment method of dockers in cluster for dynamic workload
YIN Fei, LONG Lingli, KONG Zheng, SHAO Han, LI Xin, QIAN Zhuzhong
Journal of Computer Applications 2021, 41 (
6
): 1581-1588. DOI:
10.11772/j.issn.1001-9081.2020121913
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271
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Aiming at the problem of frequent migration of containers triggered by dynamic changes of cluster workload, a container deployment method based on resource reservation was proposed. Firstly, a dynamic change description mechanism of single-container resource demand based on Markov chain model was designed to describe the resource demand situation of single container. Secondly, the dynamic change of multi-container resource was analyzed based on the single-container Markov chain model to describe the container resource demand state. Thirdly, a container deployment and resource reservation algorithm for dynamic workload was proposed based on the multi-container Markov chain. Finally, the performance of the proposed algorithm was optimized based on the analysis of container resource demand characteristics. The simulation experimental environment was constructed based on the domestic software and hardware environment, and the simulation results show that in terms of resource conflict rate, the performance of the proposed method has the performance close to the optimal peak allocation strategy named Resource with Peak (RP), but its number of required hosts and container dynamic migration number are significantly less; in terms of resource utilization rate, the proposed method has the number of hosts used slightly more than the optimal valley allocation strategy named Resource with Valley (RV), but has less dynamic migration number and lower resource conflict rate; compared with the peak and valley allocation strategy named Resource with Valley and Peak (RVP), the proposed method has better comprehensive performance.
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Adaptive network transmission mechanism based on forward error correction
ZHU Yongjin, YIN Fei, DOU Longlong, WU Kun, ZHANG Zhiwei, QIAN Zhuzhong
Journal of Computer Applications 2021, 41 (
3
): 825-832. DOI:
10.11772/j.issn.1001-9081.2020060948
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347
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Aiming at the performance degradation of transmission performance of Transmission Control Protocol (TCP) in wireless network caused by the loss packet retransmission mechanism triggered by packet loss, an Adaptive transmission mechanism based on Forward Error Correction (AdaptiveFEC) was proposed. In the mechanism, the transmission performance of TCP was improved by the avoidance of triggering TCP loss packet retransmission mechanism, which realized by reducing data segment loss with forward error correction. Firstly, the optimal redundant segment ratio in current time was selected according to the current network status and the data transmission characteristics of the current connection. Then, the network status was estimated by analyzing the data segment sequence number in the TCP data segment, so that the redundant segment ratio was dynamically updated according to the network. Large number of experiment results show that, in the transmission environment with a round-trip delay of 20 ms and a packet loss rate of 5%, AdaptiveFEC can increase the transmission rate of TCP connection by 42% averagely compared to static forward error correction mechanism, and the download speed can be twice as much as the original speed with the proposed mechanism applied to file download applications.
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Online task scheduling algorithm for big data analytics based on cumulative running work
LI Yefei, XU Chao, XU Daoqiang, ZOU Yunfeng, ZHANG Xiaoda, QIAN Zhuzhong
Journal of Computer Applications 2019, 39 (
8
): 2431-2437. DOI:
10.11772/j.issn.1001-9081.2019010073
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A Cumulative Running Work (CRW) based task scheduler CRWScheduler was proposed to effectively process tasks without any prior knowledge for big data analytics platform like Hadoop and Spark. The running job was moved from a low-weight queue to a high-weight one based on CRW. When resources were allocated to a job, both the queue of the job and the instantaneous resource utilization of the job were considered, significantly improving the overall system performance without prior knowledge. The prototype of CRWScheduler was implemented based on Apache Hadoop YARN. Experimental results on 28-node benchmark testing cluster show that CRWScheduler reduces average Job Flow Time (JFT) by 21% and decreases JFT of 95th percentile by up to 35% compared with YARN fair scheduler. Further improvements can be obtained when CRWScheduler cooperates with task-level schedulers.
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