Abstract
Emerging scale-out I/O intensive applications are broadly used now, which process a large amount of data in buffer/cache for reorganization or analysis and their performances are greatly affected by the speed of the I/O system. Efficient management scheme of the limited kernel buffer plays a key role in improving I/O system performance, such as caching hinted data for reuse in future, prefetching hinted data, and expelling data not to be accessed again from a buffer, which are called proactive mechanisms in buffer management. However, most of the existing buffer management schemes cannot identify data reference regularities (i.e., sequential or looping patterns) that can benefit proactive mechanisms, and they also cannot perform in the application level for managing specified applications. In this paper, we present an A pplication Oriented I/O Optimization (AOIO) technique automatically benefiting the kernel buffer/cache by exploring the I/O regularities of applications based on program counter technique. In our design, the input/output data and the looping pattern are in strict symmetry. According to AOIO, each application can provide more appropriate predictions to operating system which achieve significantly better accuracy than other buffer management schemes. The trace-driven simulation experiment results show that the hit ratios are improved by an average of 25.9% and the execution times are reduced by as much as 20.2% compared to other schemes for the workloads we used.
Highlights
The scale of I/O intensive workloads generated and shared by enterprises, scientific research, and databases has increased immeasurably
We explore the feasibility of PC-based technique by making statistics analysis between logical block numbers and files originated from different applications, and propose the I/O
For the basic Least Recently Used (LRU) policy, it has no prediction ability, and it performs the worst among remaining four schemes listed in this article
Summary
The scale of I/O intensive workloads generated and shared by enterprises, scientific research, and databases has increased immeasurably. GraphChi is an application for large-scale graph computation on a computer. The popular big data training applications analyze data in kernel buffer as large as they can narrow the speed gap between CPU and storage devices. The management for size-limited buffer plays a critical role in systems, which provides a way to store data in storage devices (e.g., HDD, SSD) by bringing them into kernel buffer when they are needed. Frequency or recency-based buffer management schemes, called LRU-based schemes. User-level buffer management schemes based on application hints. LRU-based schemes with data frequency or recency factors are still widely used in kernel buffer management due to their simplicity. The LRFU scheme [21] makes use of both the frequency and recency factors
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
