| Storm集群下基于性能感知的负载均衡策略 |
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| 引用本文： | 冯馨锐,谢彬,唐鹏,秦健.Storm集群下基于性能感知的负载均衡策略[J].计算机系统应用,2018,27(12):181-186. |
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| 作者姓名： | 冯馨锐 谢彬 唐鹏 秦健 |
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| 作者单位： | 中国电子科技集团公司第三十二研究所 信息服务平台室, 上海 201808,中国电子科技集团公司第三十二研究所 信息服务平台室, 上海 201808,中国电子科技集团公司第三十二研究所 信息服务平台室, 上海 201808,中国电子科技集团公司第三十二研究所 信息服务平台室, 上海 201808 |
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| 摘 要： | Storm计算框架具有为多源异构大数据提供高效、快速、实时处理的能力.然而因Storm默认的调度策略使用了简单的轮询方法，无法根据集群动态的负载状态调整其任务的分配.针对该问题，提出了基于性能感知的负载均衡策略，根据节点的处理效率计算其性能感知值，并通过贪心调度保证节点的任务量与节点处理能力相匹配，以达到负载均衡的目的.通过与默认调度算法实验比较，结果表明该算法能够有效降低Storm处理时延，提高吞吐量和实现集群负载均衡.
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| 关 键 词： | Storm 实时计算 性能感知 负载均衡 |
| 收稿时间： | 2018/5/31 0:00:00 |
| 修稿时间： | 2018/6/19 0:00:00 |
Load Balancing Strategy Based on Performance-Aware in Storm Clusters |
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| FENG Xin-Rui,XIE Bin,TANG Peng and QIN Jian.Load Balancing Strategy Based on Performance-Aware in Storm Clusters[J].Computer Systems& Applications,2018,27(12):181-186. |
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| Authors: | FENG Xin-Rui XIE Bin TANG Peng and QIN Jian |
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| Affiliation: | Information Service Laboratory, The Thirty-second Institute of Chinese Electronic Technology Group Corporation, Shanghai 201808, China,Information Service Laboratory, The Thirty-second Institute of Chinese Electronic Technology Group Corporation, Shanghai 201808, China,Information Service Laboratory, The Thirty-second Institute of Chinese Electronic Technology Group Corporation, Shanghai 201808, China and Information Service Laboratory, The Thirty-second Institute of Chinese Electronic Technology Group Corporation, Shanghai 201808, China |
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| Abstract: | As a real-time computing framework, Storm has provided an efficient, fast, and real-time processing ability for multi-source heterogeneous data processing. However, Storm''s default scheduler uses a simple Round-robin method and unable to adjust assignments of its task according to cluster''s dynamic load status. To solve this problem, this study proposes a load-balancing strategy based on performance-aware. It could calculate Performance-Aware Value (PAV) according to node''s processing ability, then greedy scheduling to achieve load balancing, which assigns the amount of computation match with node current processing capacity to achieve load balancing. Compared with the default scheduling algorithm, the results show that this algorithm can effectively reduce the Storm processing delay and improve the throughput, finally achieve cluster''s load balance. |
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| Keywords: | Storm real-time computing performance-aware load balance |
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