2 research outputs found
Telescope: Telemetry at Terabyte Scale
Data-hungry applications that require terabytes of memory have become
widespread in recent years. To meet the memory needs of these applications,
data centers are embracing tiered memory architectures with near and far memory
tiers. Precise, efficient, and timely identification of hot and cold data and
their placement in appropriate tiers is critical for performance in such
systems. Unfortunately, the existing state-of-the-art telemetry techniques for
hot and cold data detection are ineffective at the terabyte scale.
We propose Telescope, a novel technique that profiles different levels of the
application's page table tree for fast and efficient identification of hot and
cold data. Telescope is based on the observation that, for a memory- and
TLB-intensive workload, higher levels of a page table tree are also frequently
accessed during a hardware page table walk. Hence, the hotness of the higher
levels of the page table tree essentially captures the hotness of its subtrees
or address space sub-regions at a coarser granularity. We exploit this insight
to quickly converge on even a few megabytes of hot data and efficiently
identify several gigabytes of cold data in terabyte-scale applications.
Importantly, such a technique can seamlessly scale to petabyte-scale
applications.
Telescope's telemetry achieves 90%+ precision and recall at just 0.009%
single CPU utilization for microbenchmarks with a 5 TB memory footprint. Memory
tiering based on Telescope results in 5.6% to 34% throughput improvement for
real-world benchmarks with a 1-2 TB memory footprint compared to other
state-of-the-art telemetry techniques
Recommended from our members
Hardening the NOVA File System
Emerging fast, persistent memories will enable systems that combine conventional DRAM with large amounts of non-volatile main memory (NVMM) and provide huge increases in storage performance. Fully realizing this potential requires fundamental changes in how system software manages, protects, and provides access to data that resides in NVMM. We address these needs by describing a NVMM-optimized file system called NOVA that is both fast and resilient in the face of corruption due to media errors and software bugs. We identify and propose solutions for the unique challenges in hardening an NVMM file system, adapt state-of-the-art reliability techniques to an NVMM file system, and quantify the performance and storage overheads of these techniques. We find that NOVA's reliability features increase file system size system size by 14.9% and reduce application-level performance by between 2% and 38%.Pre-2018 CSE ID: CS2017-101