AIOC2: A deep Q-learning approach to autonomic I/O congestion control in Lustre

Abstract In high performance computing systems, I/O congestion is a common problem in large-scale distributed file systems. However, the current implementation mainly requires administrator to manually design low-level implementation and optimization, we proposes an adaptive I/O congestion control framework, named AIOC 2 , which can not only adaptively tune the I/O congestion control parameters, but also exploit the deep Q-learning method to start the training parameters and optimize the tuning for different types of workloads from the server and the client at the same time. AIOC 2 combines the feedback-based dynamic I/O congestion control and deep Q-learning parameter tuning technology to …

Improving LSM‐trie performance by parallel search

A configurable rule based classful token bucket filter network request scheduler for the lustre file system

HPC file systems today work in a best-effort manner where individual applications can flood the file system with requests, effectively leading to a denial of service for all other tasks. This paper presents a classful Token Bucket Filter (TBF) policy for the Lustre file system. The TBF enforces Remote Procedure Call (RPC) rate limitations based on (potentially complex) Quality of Service (QoS) rules. The QoS rules are enforced in Lustre's Object Storage Servers, where each request is assigned to an automatically created QoS class.The proposed QoS implementation for Lustre enables various features for each class including the support for high-priority and real-time requests even under heavy …

Hyperion

Indexes are essential in data management systems to increase the speed of data retrievals. Widespread data structures to provide fast and memory-efficient indexes are prefix tries. Implementations like Judy, ART, or HOT optimize their internal alignments for cache and vector unit efficiency. While these measures usually improve the performance substantially, they can have a negative impact on memory efficiency. In this paper we present Hyperion, a trie-based main-memory key-value store achieving extreme space efficiency. In contrast to other data structures, Hyperion does not depend on CPU vector units, but scans the data structure linearly. Combined with a custom memory allocator, Hyperion…

NVMM-Oriented Hierarchical Persistent Client Caching for Lustre

In high-performance computing (HPC), data and metadata are stored on special server nodes and client applications access the servers’ data and metadata through a network, which induces network latencies and resource contention. These server nodes are typically equipped with (slow) magnetic disks, while the client nodes store temporary data on fast SSDs or even on non-volatile main memory (NVMM). Therefore, the full potential of parallel file systems can only be reached if fast client side storage devices are included into the overall storage architecture. In this article, we propose an NVMM-based hierarchical persistent client cache for the Lustre file system (NVMM-LPCC for short). NVMM-LPC…

LPCC

Most high-performance computing (HPC) clusters use a global parallel file system to enable high data throughput. The parallel file system is typically centralized and its storage media are physically separated from the compute cluster. Compute nodes as clients of the parallel file system are often additionally equipped with SSDs. The node internal storage media are rarely well-integrated into the I/O and compute workflows. How to make full and flexible use of these storage media is therefore a valuable research question. In this paper, we propose a hierarchical Persistent Client Caching (LPCC) mechanism for the Lustre file system. LPCC provides two modes: RW-PCC builds a read-write cache on…