Biblio

The new generation ship integrated power system (IPS) realizes high level informatization for various physical equipments, and gradually develops to a cyber-physical system (CPS). The future trend is collecting ship big data to achieve data-driven intelligence for IPS. However, traditional relational data management framework becomes inefficient to handle the real-time data processing in ship integrated power cyber-physics system. In order to process the large-scale real-time data that collected from numerous sensors by field bus of IPS devices within acceptable latency, especially for handling the semi-structured and non-structured data. This paper proposes a novel key-value data model based real-time data management framework, which enables batch processing and distributed deployment to acquire time-efficiency as well as system scalable. We implement a real-time data management prototype system based on an open source in-memory key-value store. Finally, the evaluation results from the prototype verify the advantages of novel framework compared with traditional solution.

With the ever increasing DRAM capacity in commodity computers, applications tend to store large amount of data in main memory for fast access. Accordingly, efficient traversal of index structures to locate requested data becomes crucial to their performance. The index data structures grow so large that only a fraction of them can be cached in the CPU cache. The CPU cache can leverage access locality to keep the most frequently used part of an index in it for fast access. However, the traversal on the index to a target data during a search for a data item can result in significant false temporal and spatial localities, which make CPU cache space substantially underutilized. In this paper we show that even for highly skewed accesses the index traversal incurs excessive cache misses leading to suboptimal data access performance. To address the issue, we introduce Search Lookaside Buffer (SLB) to selectively cache only the search results, instead of the index itself. SLB can be easily integrated with any index data structure to increase utilization of the limited CPU cache resource and improve throughput of search requests on a large data set. We integrate SLB with various index data structures and applications. Experiments show that SLB can improve throughput of the index data structures by up to an order of magnitude. Experiments with real-world key-value traces also show up to 73% throughput improvement on a hash table.

Modern distributed key-value stores are offering superior performance, incremental scalability, and fine availability for data-intensive computing and cloud-based applications. Among those distributed data stores, the designs that ensure the confidentiality of sensitive data, however, have not been fully explored yet. In this paper, we focus on designing and implementing an encrypted, distributed, and searchable key-value store. It achieves strong protection on data privacy while preserving all the above prominent features of plaintext systems. We first design a secure data partition algorithm that distributes encrypted data evenly across a cluster of nodes. Based on this algorithm, we propose a secure transformation layer that supports multiple data models in a privacy-preserving way, and implement two basic APIs for the proposed encrypted key-value store. To enable secure search queries for secondary attributes of data, we leverage searchable symmetric encryption to design the encrypted secondary indexes which consider security, efficiency, and data locality simultaneously, and further enable secure query processing in parallel. For completeness, we present formal security analysis to demonstrate the strong security strength of the proposed designs. We implement the system prototype and deploy it to a cluster at Microsoft Azure. Comprehensive performance evaluation is conducted in terms of Put/Get throughput, Put/Get latency under different workloads, system scaling cost, and secure query performance. The comparison with Redis shows that our prototype can function in a practical manner.

This paper presents an efficient implementation of key-value store using Bloom filters on FPGA. Bloom filters are used to reduce the number of unnecessary accesses to the hash tables, thereby improving the performance. Additionally, for better hash table utilization, we use a modified cuckoo hashing algorithm for the implementation. They are implemented in FPGA to further improve the performance. Experimental results show significant performance improvement over existing approaches.