摘要：
针对分布式哈希表(DHT)云存储系统中的存储负载不均问题，提出一种基于节点动态前移(NDF)的负载均衡算法。通过过载节点动态前移，缩小节点存储分区，进而降低节点的存储负载。同时，过载节点向第3个后继节点复制相关数据，保证系统中数据的副本数量稳定。在NDF算法执行过程中，只需过载节点与第3个后继节点进行简单协调，多个过载节点可以并发地进行负载转移，适合在大规模集群中部署。在10个节点的小规模集群中进行功能测试，验证NDF算法具有良好的负载均衡能力。在 5 000个节点的大规模集群中进行性能测试，结果证明在系统总负载不超过60%时，与虚拟节点算法相比，NDF算法负载转移代价可以提升30%以上。
关键词:
分布式哈希表,
云存储,
负载均衡,
节点动态前移算法,
虚拟节点,
负载迁移代价
Abstract:
In order to solve the problem of load imbalance of Distributed Hash Table(DHT) cloud storage system, this paper proposes a load balancing algorithm based on Node Dynamic Forward(NDF). Through overload nodes forward, this algorithm can reduce the node’s partition, and thereby reduce the storage node load. At the same time, copy the corresponding data to the third successor node, to ensure the stable number of copies the data in the system. In the NDF implementation process, it only needs a simple coordination between overload node and the third successor node. Therefore, multiple overloaded nodes can concurrently do load transfer, which is suitable for deployment in a large cluster. The functional test in a minor cluster of 10 nodes verifies that the NDF has good ability of load balancing. The performance test in a massive cluster of 5 000 nodes demonstrates the total system load does not exceed 60%, and compared with the virtual node algorithm, NDF algorithm can improve the load transfer cost by more than 30%.
Key words:
Distributed Hash Table(DHT),
cloud storage,
load balancing,
Node Dynamic Forward(NDF) algorithm,
virtual node,
load transfer cost
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