Storage systems using Peer-to-Peer (P2P) architecture are an alternative to the traditional client-server systems. They offer better scalability and fault tolerance while at the same time eliminate the single point of failure. The nature of P2P storage systems (which consist of heterogeneous nodes) introduce however data placement challenges that create implementation trade-offs (e.g., between performance and scalability). Existing Kademlia-based DHT data placement method stores data at closest node, where the distance is measured by bit-wise XOR operation between data and a given node. This approach is highly scalable because it does not require global knowledge for placing data nor for the data retrieval. It does not however consider the heterogeneous performance of the nodes, which can result in imbalanced resource usage affecting the overall latency of the system. Other works implement criteria-based selection that addresses heterogeneity of nodes, however often cause subsequent data retrieval to require global knowledge of where the data stored. This paper introduces Residual Performance-based Data Placement (RPDP), a novel data placement method based on dynamic temporal residual performance of data nodes. RPDP places data to most appropriate selected nodes based on their throughput and latency with the aim to achieve lower overall latency by balancing data distribution with respect to the individual performance of nodes. RPDP relies on Kademlia-based DHT with modified data structure to allow data subsequently retrieved without the need of global knowledge. The experimental results indicate that RPDP reduces the overall latency of the baseline Kademlia-based P2P storage system (by 4.87%) and it also reduces the variance of latency among the nodes, with minimal impact to the data retrieval complexity.

翻译：采用对等（P2P）架构的存储系统是传统客户端-服务器系统的一种替代方案。它们在提供更优可扩展性和容错性的同时，消除了单点故障。然而，P2P存储系统由异构节点组成的特性引入了数据放置的挑战，由此产生了实现上的权衡（例如性能与可扩展性之间的平衡）。现有基于Kademlia的分布式哈希表（DHT）数据放置方法将数据存储在最近节点，距离通过数据与给定节点间的按位异或运算测量。该方法因无需全局知识即可完成数据放置与检索而具有高度可扩展性，但未考虑节点的异构性能，可能导致资源使用不平衡，进而影响系统的整体延迟。其他研究虽实现了基于准则的选择机制以解决节点异构性问题，但常导致后续数据检索需依赖存储位置的全局知识。本文提出基于残余性能的数据放置方法（RPDP），一种基于数据节点动态时间残余性能的新型数据放置策略。RPDP根据节点的吞吐量与延迟将数据放置到最合适的选定节点，旨在通过依据节点个体性能均衡数据分布来降低整体延迟。该方法依托于采用改进数据结构的Kademlia式DHT，使后续数据检索无需全局知识。实验结果表明，RPDP将基于Kademlia的P2P存储系统基线方案的整体延迟降低了4.87%，并减小了节点间延迟方差，同时对数据检索复杂度的影响极小。