Key-Value-Links: A New Data Model for Developing Efficient RDMA-Based In-Memory Stores

Hai Duc Nguyen, The De Vu, Duc Hieu Nguyen, Minh Duc Le, Tien Hai Ho, Tran Vu Pham


This paper proposes a new data model, named Key-Value-Links (KVL), to help in memory store utilizes RDMA eciently. The KVL data model is essentially a key-value model with several extensions. This model organizes data as a network of items in which items are connected to each other through links. Each link is a pointer to the address of linked item and is embedded into the item establishing this link. Organizing datasets using the KVL model enables applications making use RDMA-Reads to directly fetch items at the server at very high speed. Since link chasing bypasses the CPU at the server side, this operation allows the client to read items at extremely low latency and reduces much workload at data nodes. Furthermore, our model well ts many real-life applications ranging from graph exploration and map matching to dynamic web page creation. We also developed an in-memory store utilizing the KVL model named KELI. The results of experiments on real-life workload indicate that KELI, without being applied much optimization, easily outperform Memcached, a popular in-memory key-value store, in many cases.

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