log analyticslog compressionnear-storage FPGAtoken querying enginetree-based template search
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Abstract (AI)
This paper presents, a log analytics platform with near-storage accelerators for high-performance, cost- and power-efficient unstructured log processing. offloads log analytics queries to an efficient near-storage FPGA implementation of a token querying engine, which can take advantage of the high internal bandwidth of storage devices within the available chip resource limitations. This engine is flexible enough to handle complex queries including template search based on user-defined tree-based template libraries, as well as concurrent execution of multiple queries. also uses a log-optimized version of a simple, high-throughput compression algorithm in order to further improve the effective bandwidth of backing storage.
Key Findings
1
MithriLog employs a log-optimized high-throughput compression algorithm to improve the effective bandwidth of backing storage.
2
MithriLog is a near-storage log analytics platform targeting high-performance, cost-efficient, and power-efficient unstructured log processing.
3
The platform offloads log queries to an FPGA-based token querying engine that exploits storage-device internal bandwidth within chip resource constraints.
4
The querying engine supports complex operations, including template search using user-defined tree-based template libraries and concurrent execution of multiple queries.
Research Object
unstructured log processing in a near-storage FPGA-accelerated log analytics platform
Research Subject
high-performance, cost- and power-efficient execution of complex log analytics queries, including template search and concurrent querying, using near-storage acceleration and log-optimized compression
Publication Details
Publication Date
2021-10-17
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