Implementation of informed prefetching and caching in Linux

Abstract-In this paper, we present an informed prefetching technique called IPODS that makes use of application-disclosed access patterns to prefetch hinted blocks in distributed multilevel storage systems. We develop a prefetching pipeline in IPODS, where an informed prefetching process is divided into a set of independent prefetching steps among multiple storage levels in a distributed system. In the IPODS system, while data blocks are prefetched from hard disks to memory buffers in remote storage servers, data blocks buffered in the servers are prefetched through networks to clients' local cache. We show that these two prefetching steps can be handled in a pipelining manner to improve I/O performance of distributed storage systems. Our IPODS technique differs itself from existing prefetching schemes in two ways. First, IPODS reduces applications' I/O stalls by keeping hinted data in clients' local caches and storage servers' fast buffers (e.g., solid state disks). Second, in a prefetching pipeline, multiple informed prefetching mechanisms semi-dependently coordinate to fetch blocks (1) from low-level (slow) to high-level (fast) storage devices in servers and (2) from high-level devices in servers to clients' local cache. The prefetching pipeline in IPODS judiciously hides network latencies in distributed storage systems, thereby reducing the overall I/O access time in distributed systems. Using a wide range of real-world I/O traces, our experiments show that IPODS can improve noticeably I/O performance of distributed storage systems.

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“…[5]. Performance benefits of informed prefetching were confirmed by other studies undertaken by Huizinga et al [3] and Chen et al [11].…”

Section: A Multi-level Storage Systemssupporting

confidence: 65%

A Pipelining Approach to Informed Prefetching in Distributed Multi-level Storage Systems

Assaf

Alghamdi

Jiang

et al. 2012

2012 IEEE 11th International Symposium on Network Computing and Applications

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“…Informed prefetching performs aggressive prefetching to helps in wrapping the application's I/O stalls [3,8,16]. The performance benefits of informed prefetching were also presented by other researchers such as Huizinga et al [6] and Chen et al [17].…”

Section: Informed Prefetchingmentioning

confidence: 89%

Eco-Storage: A Hybrid Storage System with Energy-Efficient Informed Prefetching

Assaf

Jiang

Abid

et al. 2013

J Sign Process Syst

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In this paper, we present an energy-aware informed prefetching technique called Eco-Storage that makes use of the application-disclosed access patterns to group the informed prefetching process in a hybrid storage system (e.g., hard disk drive and solid state disks). Since the SSDs are more energy efficient than HDDs, aggressive prefetching for the data in the HDD level enables it to have as much standby time as possible in order to save power. In the Eco-Storage system, the application can still read its on-demand I/O reading requests from the hybrid storage system while the data blocks are prefetched in groups from HDD to SSD. We show that these two steps can be handled in parallel to decreases the system's power consumption. Our Eco-Storage technique differs from existing energy-aware prefetching schemes in two ways. First, EcoStorage is implemented in a hybrid storage system where the SDD level is more energy efficient. Second, it can group the informed prefetching process and quickly prefetch the data from the HDD to the SSD to increase the frequent HDD standby times. This will makes the application finds most of its on-demand I/O reading requests in the SSD level. Finally, we develop a simulator to evaluate our Eco-Storage system performance. Our results show that our Eco-Storage reduces the power consumption by at least 75 % when compared with the worst case of non-Eco-Storage case using a real-world I/O trace.

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“…More details about predictive prefetching approach will be discussed in latter sections. Other studies undertaken by Huizinga et al [31] and Chen et al [32].…”

Section: Predictive Prefetchingmentioning

confidence: 94%

Predictive Prefetching for Parallel Hybrid Storage Systems

Assaf¹

2015

IJCNS

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In this paper, we present a predictive prefetching mechanism that is based on probability graph approach to perform prefetching between different levels in a parallel hybrid storage system. The fundamental concept of our approach is to invoke parallel hybrid storage system's parallelism and prefetch data among multiple storage levels (e.g. solid state disks, and hard disk drives) in parallel with the application's on-demand I/O reading requests. In this study, we show that a predictive prefetching across multiple storage levels is an efficient technique for placing near future needed data blocks in the uppermost levels near the application. Our PPHSS approach extends previous ideas of predictive prefetching in two ways: (1) our approach reduces applications' execution elapsed time by keeping data blocks that are predicted to be accessed in the near future cached in the uppermost level; (2) we propose a parallel data fetching scheme in which multiple fetching mechanisms (i.e. predictive prefetching and application's on-demand data requests) can work in parallel; where the first one fetches data blocks among the different levels of the hybrid storage systems (i.e. low-level (slow) to high-level (fast) storage devices) and the other one fetches the data from the storage system to the application. Our PPHSS strategy integrated with the predictive prefetching mechanism significantly reduces overall I/O access time in a hybrid storage system. Finally, we developed a simulator to evaluate the performance of the proposed predictive prefetching scheme in the context of hybrid storage systems. Our results show that our PPHSS can improve system performance by 4% across real-world I/O traces without the need of using large size caches.

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Implementation of informed prefetching and caching in Linux

Cited by 7 publications

References 9 publications

A Pipelining Approach to Informed Prefetching in Distributed Multi-level Storage Systems

A Pipelining Approach to Informed Prefetching in Distributed Multi-level Storage Systems

Eco-Storage: A Hybrid Storage System with Energy-Efficient Informed Prefetching

Predictive Prefetching for Parallel Hybrid Storage Systems

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