Cache Management Algorithm Research Articles

DAC: A Device-Aware Cache Management Algorithm for Heterogeneous Mobile Storage Systems

In recent years, heterogeneous devices have been employed frequently in mobile storage systems because a combination of such devices can supply a synergistically useful storage solution by taking advantage of each device. One important design constraint in heterogeneous storage systems is to mitigate I/O performance degradation stemming from the difference between access times of different devices. To this end, there has not been much work to devise proper buffer cache management algorithms. This paper presents a novel buffer cache management algorithm which considers both I/O cost per device and workload patterns in mobile computing systems with a heterogeneous storage pair of a hard disk and a NAND flash memory. In order to minimize the total I/O cost under varying workload patterns, the proposed algorithm employs a dynamic cache partitioning technique over different devices and manages each partition according to request patterns and I/O types along with the temporal locality. Trace-based simulations show that the proposed algorithm reduces the total I/O cost and flash write count significantly over the existing buffer cache algorithms on typical mobile traces.

An intermediate language level optimization framework for dynamic binary translation

This paper presents an intermediate language level optimization framework for dynamic binary translation. Performance is important to a dynamic binary translation system, so there has been a growing interest in exploring new optimization algorithms. The framework proposed in this paper includes efficient profiling, hot code recognition and smart code cache management policies. Profiling is responsible for collecting runtime information, which will be used by hot code recognition and code cache management algorithms. We only focus on recognizing the hottest code, and assign priorities to basic blocks according to their hotness to facilitate code cache management.

A unified multiple-level cache for high performance storage systems

Multi-level cache hierarchies are widely used in high-performance storage systems to improve I/O performance. However, traditional cache management algorithms are not suited well for such cache organisations. Recently proposed multi-level cache replacement algorithms using aggressive exclusive caching work well with single or multiple-client, low-correlated workloads, but suffer serious performance degradation with multiple-client, high-correlated workloads. In this paper, we propose a new cache management algorithm that handles multi-level buffer caches by forming a unified cache (uCache), which uses both exclusive caching in L2 storage caches and cooperative client caching. We also propose a new local replacement algorithm, Frequency Based Eviction-Reference (FBER), based on our study of access patterns in exclusive caches. Our simulation results show that uCache increases the cumulative cache hit ratio dramatically. Compared to other popular cache algorithms, such as LRU, the I/O response time is improved by up to 46% for low-correlated workloads and 53% for high-correlated workloads.

Power-Aware Storage Cache Management

Reducing energy consumption is an important issue for data centers. Among the various components of a data center, storage is one of the biggest energy consumers. Previous studies have shown that the average idle period for a server disk in a data center is very small compared to the time taken to spin down and spin up. This significantly limits the effectiveness of disk power management schemes. This article proposes several power-aware storage cache management algorithms that provide more opportunities for the underlying disk power management schemes to save energy. More specifically, we present an offline energy-optimal cache replacement algorithm using dynamic programming, which minimizes the disk energy consumption. We also present an offline power-aware greedy algorithm that is more energy-efficient than Belady's offline algorithm (which minimizes cache misses only). We also propose two online power-aware algorithms, PA-LRU and PB-LRU. Simulation results with both a real system and synthetic workloads show that, compared to LRU, our online algorithms can save up to 22 percent more disk energy and provide up to 64 percent better average response time. We have also investigated the effects of four storage cache write policies on disk energy consumption.

Quantitative Evaluation of State-Preserving Leakage Reduction Algorithm for L1 Data Caches

As the transistor feature sizes and threshold voltages reduce, leakage energy consumption has become an inevitable issue for highperformance microprocessor designs. Since on-chip caches are major contributors of the leakage, a number of researchers have proposed efficient leakage reduction techniques. However, it is still not clear that 1) what kind of algorithm can be considered and 2) how much they have impact on energy and performance. To answer these questions, we explore runtime cache management algorithm, and evaluate the energy-performance efficiency for several alternatives.

A New Semantic Cache Management Method in Mobile Databases

In mobile database systems, caching is a crucial way to improve the performance because a new query can be partially answered locally when the client is continuously moving around. Semantic caching has been paid much attention in traditional database systems. However, the methods of semantic cache management proposed in those systems are not suitable for mobile computing environment. Moreover, although many improved semantic caching strategies for mobile computing application have been proposed in recent years, few papers discussed semantic cache coherence control, which is very important in mobile computing environment. In this paper, we propose some new semantic cache model and management algorithms, which can significantly reduce the unnecessary uplink requests and downlink broadcasts compared to previous schemes. Simulation experiments are carried out to evaluate the proposed strategy.

Second-level buffer cache management

Buffer caches are commonly used in servers to reduce the number of slow disk accesses or network messages. These buffer caches form a multilevel buffer cache hierarchy. In such a hierarchy, second-level buffer caches have different access patterns from first-level buffer caches because accesses to a second-level are actually misses from a first-level. Therefore, commonly used cache management algorithms such as the least recently used (LRU) replacement algorithm that work well for single-level buffer caches may not work well for second-level. We investigate multiple approaches to effectively manage second-level buffer caches. In particular, we report our research results in 1) second-level buffer cache access pattern characterization, 2) a new local algorithm called multi-queue (MQ) that performs better than nine tested alternative algorithms for second-level buffer caches, 3) a set of global algorithms that manage a multilevel buffer cache hierarchy globally and significantly improve second-level buffer cache hit ratios over corresponding local algorithms, and 4) implementation and evaluation of these algorithms in a real storage system connected with commercial database servers (Microsoft SQL server and Oracle) running industrial-strength online transaction processing benchmarks.

Network-aware partial caching for Internet streaming media

The delivery of multimedia over the Internet is affected by adverse network conditions such as high packet loss rate and long delay. This paper aims at mitigating such effects by leveraging client-side caching proxies. We present a novel cache architecture and associated cache management algorithms that turn edge caches into accelerators of streaming media delivery. This architecture allows partial caching of media objects and joint delivery from caches and origin servers. Most importantly, the caching algorithms are both network-aware and stream-aware; they take into account the popularity of streaming media objects, their bit rate requirements, and the available bandwidth between clients and servers. Using Internet bandwidth models derived from proxy cache logs and measured over real Internet paths, we have conducted extensive simulations to evaluate the performance of various cache management algorithms. Our experiments demonstrate that network-aware caching algorithms can significantly reduce startup delay and improve stream quality. Our experiments also show that partial caching is particularly effective when bandwidth variability is not very high.

High performance data broadcasting systems

Data broadcasting as a means of efficient data dissemination is a key technology facilitating ubiquitous computing. For this reason, broadcast scheduling algorithms have received a lot of attention. However, all existing algorithms make the core assumption that the data items to be broadcast are immediately available in the transmitter's queue, ignoring the key role that the disk subsystem and the cache management play in the overall broadcast system performance. With this paper we contribute a comprehensive system's perspective towards the development of high performance broadcast systems, taking into account how broadcast scheduling, disk scheduling, and cache management algorithms affect the overall performance. We contribute novel techniques that ensure an efficient interplay between broadcast scheduling, cache management, and disk scheduling. We study comprehensively the performance of the broadcast server, as it consists of the broadcast scheduling, the disk scheduling, the cache management algorithms, and the transmitter. Our results show that the contributed algorithms yield considerably higher performance. Furthermore, one of our algorithms is shown to enjoy considerably higher performance, under all values of the problem and system parameters. A key contribution is the result that broadcast scheduling algorithms have only a small effect on the overall system performance, which necessitates the definition of different focal points for efforts towards high performance data broadcasting.

Radiance interpolants for accelerated bounded-error ray tracing

Ray tracers, which sample radiance, are usually regarded as offline rendering algorithms that are too slow for interactive use. In this article we present a system that exploits object-space, ray-space, image-space, and temporal coherence to accelerate ray tracing. Our system uses per-surface interpolants to approximate radiance both interactive and batch ray tracers. Our approach explicity decouples the two primary operations of a ray tracer—shading and visibility determination—and accelerates each of them independently. Shading is accelerated by quadrilinearily interpolating lazily acquired radiance samples. Interpolation error does not exceed a user-specified bound, allowing the user to control performance/quality tradeoffs. Error is bounded by adaptive sampling at discontinuities and radiance nonlinearities. Visibility determination at pixels is accelerated by reprojecting interpolants as the user's viewpoint changes. A fast scan-line alogoithm then achieves high performance without sacrificing image quality. For a smoothly varying viewpoint, the combination of lazy interpolants and projection substantially accelerates the ray tracer. Additionally, an efficient cache management algorithm keeps the memory footprint of the system small with negilgible overhead.

Cache management algorithms for flexible filesystems

Cache management in flexible filesystems deals with the problem of determining a cached file to be replaced when the local cachespace is exhausted. In analogy to virtual memory management, several different algorithms exist for managing cached files. In this paper we simulate the behavior of First-In-First-Out (FIFO), Least Recently Used (LRU), Least Frequently Used (LFU) and a variation of LFU we call the File Length Algorithm (LEN) from the viewpoint of file access times, cache hit ratios and availability. The results of several simulation runs are presented and interpreted.

Cache management for shared sequential data access

This paper presents a new set of cache management algorithms for shared data objects that are accessed sequentially. I/O delays on sequentially accessed data is a dominant performance factor in many application domains, in particular for batch processing. Our algorithms fall into three classes: replacement, prefetching and scheduling strategies. Our replacement algorithms empirically estimate the rate at which the jobs are proceeding through the data. These velocity estimates are used to project the next reference times for cached data objects and our algorithms replace data with the longest time to re-use. The second type of algorithm performs asynchronous prefetching. This algorithm uses the velocity estimations to predict future cache misses and attempts to preload data to avoid these misses. Finally, we present a simple job scheduling strategy that increases locality of reference between jobs. Our new algorithms are evaluated through a detailed simulation study. Our experiments show that the algorithms substantially improve performance compared to traditional algorithms for cache management.