Entry and exit probabilities based cache replacement policy for location dependent data in mobile environments

Caching frequently accessed items on the mobile client is an important technique to enhance data availability and to improve data access time. Cache replacement policies are used to find a suitable subset of items for eviction from the cache due to limited cache size. The existing policies rely on Euclidean space and consider Euclidean distance as an important parameter for eviction. However, in practice the position and movement of objects are constrained to spatial networks where the important distance measure is the network distance. In this paper we propose a cache replacement policy, which considers the network density, network distance and probability of access as important factors for eviction. We make use of an already proven technique called Progressive incremental network expansion to compute the network distance more efficiently. A series of simulation experiments have been conducted to evaluate the performance of the policy. Results indicate that the proposed cache replacement scheme performs significantly better than the existing policies FAR and PAID and WPRRP.

This article describes how the named data networking (NDN) has recently received a lot of attention as a potential information-centric networking (ICN) architecture for the future Internet. The NDN paradigm has a great potential to efficiently address and solve the current seminal IP-based IoT architecture issues and requirements. NDN can be used with different sets of caching algorithms and caching replacement policies. The authors investigate the most suitable combination of these two features to be implemented in an IoT environment. For this purpose, the authors first reviewed the current research and development progress in ICN, then they conduct a qualitative comparative study of the relevant ICN proposals and discuss the suitability of the NDN as a promising architecture for IoT. Finally, they evaluate the performance of NDN in an IoT environment with different caching algorithms and replacement policies. The obtained results show that the consumer-cache caching algorithm used with the Random Replacement (RR) policy significantly improve NDN content validity in an IoT environment.

SummaryHybrid storage systems that consist of flash‐based solid state drives (SSDs) and traditional disks are now widely used. In hybrid storage systems, there exists a two‐level cache hierarchy that regard dynamic random access memory (DRAM) as the first level cache and SSD as the second level cache for disk storage. However, this two‐level cache hierarchy typically uses independent cache replacement policies for each level, which makes cache resource management inefficient and reduces system performance. In this paper, we propose a novel adaptive multi‐level cache (AMC) replacement algorithm in hybrid storage systems. The AMC algorithm adaptively adjusts cache blocks between DRAM and SSD cache levels using an integrated solution. AMC uses combined selective promote and demote operations to dynamically determine the level in which the blocks are to be cached. In this manner, the AMC algorithm achieves multi‐level cache exclusiveness and makes cache resource management more efficient. By using real‐life storage traces, our evaluation shows the proposed algorithm improves hybrid multi‐level cache performance and also increases the SSD lifetime compared with traditional multi‐level cache replacement algorithms. Copyright © 2015 John Wiley & Sons, Ltd.

We consider the problem of on-chip L2 cache management and replacement policies. We propose a new adaptive cache replacement policy, called Dueling CLOCK (DC), that has several advantages over the Least Recently Used (LRU) cache replacement policy.

In previous research on cache replacement policies, have seen that a block usually becomes dead after some uses. By classifying the dead block and discarding them early, there are various policies have been used but still there are number of problems such as cache pollution, high cache miss rate and cache overhead existed which degrades the system performance. In this paper, a new approach for cache replacement policy has introduced which reduces the cache miss rate and cache overhead by using shared L2 level cache. In terms of less miss rate and good hit rate there is need to deploy effective replacement policies. Cache replacement techniques is one of the crucial design parameter that affects the overall processor performance and also become more important with current growing technological moves in the direction of high associative cache . Keywords: Cache pollution; Cache overhead; Cache miss rate; Cache replacement policy (LRU and LFU); Memory overhead;

We describe the data management principles on which data processing is based in an embedded navigation system. We study this management in the framework of a Web-based navigation and spatial data visualization. We use an Internet-based connection to download the data located on a distant data server and a Web browser interface to process and to visualize them locally. All data are transferred as the result of queries defined on the client side and executed on the data server. The data management on the client side uses a data cache and takes into account the spatial aspect of data. It performs a test for semantic inclusion of results of queries and reuses already available data. We also introduce spatial query restrictions in order to reduce the selected data only to the data set relevant to the actual location of the mobile user. Restriction and semantic caching of queries allows us to reduce the amount of data transferred between a mobile client and a data server.

The cache replacement policy is a crucial phase in caching-based systems that deal with the process of selecting applicable cache contents. In this paper, we propose two novel cache replacement algorithms based on the dataset obtained from a typical wireless femto network. In the first algorithm, called Weighted Least Frequently used with an Adaptive Time Window (WLF-ATW), we aim to make a balance between the network's traffic and the recognition of popular contents. The WLF-ATW algorithm takes the frequency and the recency information of files into account to ascertain the popularity of contents. We suggest another new cache replacement policy namely Fairness Scheduling-based with an Adaptive Time Window (FS-ATW) that is based on fairness scheduling in order to minimize the user's access delay. The novelty of our proposed FS-ATW lies in ranking clients according to their last situations that lead to a further user's experience. The effectiveness of these new algorithms is evaluated from the cache hit ratio, transferred byte volume, user's access delay, user's experience, and load balance. A comprehensive numerical evaluation shows that the performance of the proposed WLF-ATW and FS-ATW algorithms is significantly better than some existing cache replacement strategies.

The technology advance in network has accelerated the development of multimedia applications over the wired and wireless communication. To alleviate network congestion and to reduce latency and workload on multimedia servers, the concept of multimedia proxy has been proposed to cache popular contents. Caching the data objects can relieve the bandwidth demand on the external network, and reduce the average time to load a remote data object to local side. Since the effectiveness of a proxy server depends largely on cache replacement policy, various approaches are proposed in recent years. In this paper, we discuss the cache replacement policy in a multimedia transcoding proxy. Unlike the cache replacement for conventional web objects, to replace some elements with others in the cache of a transcoding proxy, we should further consider the transcoding relationship among the cached items. To maintain the transcoding relationship and to perform cache replacement, we propose in this paper the RESP framework (standing for REplacement with Shortest Path). The RESP framework contains two primary components, i.e., procedure MASP (standing for Minimum Aggregate Cost with Shortest Path) and algorithm EBR (standing for Exchange-Based Replacement). Procedure MASP maintains the transcoding relationship using a shortest path table, whereas algorithm EBR performs cache replacement according to an exchanging strategy. The experimental results show that the RESP framework can approximate the optimal cache replacement with much lower execution time for processing user queries.

Relatively little research has been conducted into designing interfaces that allow GIS users to interact effectively with geospatial data in mobile environments. Users on the move are faced with limited interaction modalities. The standard mode of input on mobile devices is the pen or stylus, which some users may find difficult or too time-consuming to use. Voice commands, combined with pen input, can provide an attractive alternative for interacting with mobile GIS, as speech is a natural form of interaction. However, the idea of combining speech and pen input in mobile GIS is relatively unexplored. To this effect, we have developed a multimodal interface to a mobile GIS, providing users with the ability to freely switch between modalities to suit current tasks or environments.

Communication between mobile clients and database servers in a mobile computing environment is via wireless channels with low bandwidth and low reliability. A mobile client could cache its frequently accessed database items into its local storage in order to improve performance of database queries and availability of database items for query processing during disconnection. We describe a mobile caching mechanism for a mobile environment utilizing point to point communication paradigm. In particular, we investigate issues on caching granularity, coherence strategy, and replacement policy of mobile caching. Via a detailed simulation model, we compare our proposed caching mechanism with conventional ones and discover that our mobile caching mechanism outperforms conventional ones in most situations.

A bandwidth and effective hit optimal cache scheme for wireless data access networks with client injected updates

As technology scales down at an exponential rate, leakage power is fast becoming the dominant component of the total power budget. A large share of the total leakage power is dissipated in the cache hierarchy. To reduce cache leakage, individual cache lines can be kept in drowsy mode, a low voltage, low leakage state. Every cache access may then result in dynamic power consumption and performance penalties. A trade-off between the amount of leakage power saved on one hand, and the impact on dynamic power and performance on the other hand must be reachedTo affect this trade-off, we introduce caches in which the power level of cache lines is controlled with the cache replacement policy. In a slumberous cache, cache lines are maintained at different power save modes which we call levels, which depend on their order of replacement priorities.We evaluate the trade-offs in the context of PLRU, a common cache replacement algorithm. We explore various assignments of the tranquility levels to lines and compare overall power and performance impacts. As technology scales down, the dynamic power required to energize slumberous cache lines drops drastically while the leakage power savings remain roughly steady. The performance penalty--in cycles-- remains constant with technology scaling for each scheme we evaluate.

Reliability improvement in private non-uniform cache architecture using two enhanced structures for coherence protocols and replacement policies

Due to the increasing gap between CPU and memory speed, cache performance plays an increasingly critical role in determining the overall performance of microprocessor systems. One of the important factors that a affect cache performance is the cache replacement policy. Despite the importance, current analytical cache performance models ignore the impact of cache replacement policies on cache performance. To the best of our knowledge, this paper is the first to propose an analytical model which predicts the performance of cache replacement policies. The input to our model is a simple circular sequence profiling of each application, which requires very little storage overhead. The output of the model is the predicted miss rates of an application under different replacement policies. The model is based on probability theory and utilizes Markov processes to compute each cache access' miss probability. The model realistic assumptions and relies solely on the statistical properties of the application, without relying on heuristics or rules of thumbs. The model's run time is less than 0.1 seconds, much lower than that of trace simulations. We validate the model by comparing the predicted miss rates of seventeen Spec2000 and NAS benchmark applications against miss rates obtained by detailed execution-driven simulations, across a range of different cache sizes, associativities, and four replacement policies, and show that the model is very accurate. The model's average prediction error is 1.41%,and there are only 14 out of 952 validation points in which the prediction errors are larger than 10%.

By rapid growth of the Internet users and devices, thenumber of servers also increase simultaneously which causes the exponentialincrement of the internet traffic and static data. Handling these huge amountsof user requests and efficiently responding to them require high bandwidthlinks, powerful servers and robust equipment, which despite the availability ofthese requirements getting the full user satisfaction is extremely difficultand a tough challenge. In order to overcome the mentioned problem, the cacheservers are being used as a suitable solution. The performance of web cacheserver directly depends on its replacement policies. Several cache replacementpolicies have been proposed in literature each having varied hit rate (HR) andbyte hit rate (BHR) performances on different networks. The replacement policyproposed in this paper is a dynamic cache replacement policy which trainsitself utilizing previous network logs and by exploiting the data miningclustering algorithm. Once the training step is completed, the proposed policyutilizes the normalization formulas to score each metric of the enquiriesincluding recency, frequency, size and delay. Simulation results showed thatthe proposed policy has the optimum performance on different networks and itnot only improved the performance of web cache server in term of HR and BHR,but also decreases the data retrieval time (Delay Ratio (DR)) of the cacheservers.