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Thursday
Mar202014

Paper: Log-structured Memory for DRAM-based Storage - High Memory Utilization Plus High Performance

Most every programmer who gets sucked into deep performance analysis for long running processes eventually realizes memory allocation is the heart of evil at the center of many of their problems. So you replace malloc with something less worse. Or you tune your garbage collector like a fine ukulele. But there's a smarter approach brought to you from the folks at RAMCloud, a Stanford University production, which is a large scale, distributed, in-memory key-value database.

What they've found is that typical memory management approaches don't work and using a log structured approach yields massive benefits:

Performance measurements of log-structured memory in RAMCloud show that it enables high client through- put at 80-90% memory utilization, even with artificially stressful workloads. In the most stressful workload, a single RAMCloud server can support 270,000-410,000 durable 100-byte writes per second at 90% memory utilization. The two-level approach to cleaning improves performance by up to 6x over a single-level approach at high memory utilization, and reduces disk bandwidth overhead by 7-87x for medium-sized objects (1 to 10 KB). Parallel cleaning effectively hides the cost of cleaning: an active cleaner adds only about 2% to the latency of typical client write requests.

And for your edification they've written an excellent paper on their findings. Log-structured Memory for DRAM-based Storage:

Traditional memory allocation mechanisms are not suitable for new DRAM-based storage systems because they use memory inefficiently, particularly under changing access patterns. In contrast, a log-structured approach to memory management allows 80-90% memory utilization while offering high performance. The RAMCloud storage system implements a unified log-structured mechanism both for active information in memory and backup data on disk. The RAMCloud implementation of log-structured memory uses a two-level cleaning policy, which conserves disk bandwidth and improves perfor- mance up to 6x at high memory utilization. The cleaner runs concurrently with normal operations and employs multiple threads to hide most of the cost of cleaning.

Logging has been used for decades to ensure durability and consistency in storage systems. When we began designing RAMCloud, it was a natural choice to use a logging approach on disk to back up the data stored in main memory. However, it was surprising to discover that logging also makes sense as a technique for managing the data in DRAM. Log-structured memory takes advantage of the restricted use of pointers in storage systems to eliminate the global memory scans that fundamentally limit existing garbage collectors. The result is an efficient and highly incremental form of copying garbage collector that allows memory to be used efficiently even at utilizations of 80-90%. A pleasant side effect of this discovery was that we were able to use a single technique for managing both disk and main memory, with small policy differences that optimize the usage of each medium.
Although we developed log-structured memory for RAMCloud, we believe that the ideas are generally applicable and that log-structured memory is a good candidate for managing memory in DRAM-based storage systems.

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