Cache Management Algorithm Research Articles

Hybrid Cache Management in Ad Hoc Networks

With the consistent growing maneuver and applications of mobile technologies, dynamic handling of queries related to information on electronic media (Broadcast Queries) plays a vital role. Broadcast Queries (BQs) consists of some authentic characteristics that are not addressed when compared to conventional spatial query processing. It is possible to achieve a considerable reduction in latency while maintaining the highest accuracy and scalability related to queries related to peer-to-peer allocation and thereby allows finishing queries with no interruption at a mobile congregation. In this paper, we propose a hybrid cache management algorithm in order to proficiently encourage accessing of data in ad hoc networks. The verification algorithm gathers the neighboring peer data items to provide the spatial query solutions. The network utilization in the network is related with the available bandwidth and the free space through efficient cache management. The hybrid cache management is combined with $$Cach{e}_{Data}$$ and $$Cach{e}_{Path}$$ techniques utilizing the concept of cache replacement policies with the motivation to further improve the performance of BQs. The experimental results indicate that the proposed scheme works well in reduction of query delay and also message complexity than the related techniques.

An efficient cache management algorithm for streaming workloads

In this paper, we present a novel cache management algorithm for real-world streaming workloads. Streaming workloads are believed to exhibit very large and sequential access patterns, which has been the main consideration in designing media caching algorithms. However, legacy caching algorithms do not fully utilise fine-grained access patterns of streaming workloads and also tend to ignore human interactivity. In this paper, we present the least expectation first (LEF) algorithm, which manages a large number of block caches as two-level grouping. Specifically, we select caching and eviction targets based on the expected gain of the cached data blocks, thereby improving the cache hit ratio significantly. Experimental results show that the proposed algorithm performs better than well-known interval caching and LRU algorithms with respect to the hit ratio and the I/O bandwidth.

An efficient cache management algorithm for streaming workloads

In this paper, we present a novel cache management algorithm for real-world streaming workloads. Streaming workloads are believed to exhibit very large and sequential access patterns, which has been the main consideration in designing media caching algorithms. However, legacy caching algorithms do not fully utilise fine-grained access patterns of streaming workloads and also tend to ignore human interactivity. In this paper, we present the least expectation first (LEF) algorithm, which manages a large number of block caches as two-level grouping. Specifically, we select caching and eviction targets based on the expected gain of the cached data blocks, thereby improving the cache hit ratio significantly. Experimental results show that the proposed algorithm performs better than well-known interval caching and LRU algorithms with respect to the hit ratio and the I/O bandwidth.

A Network-Coded Caching-Based Multicasting Scheme for Information-Centric Networking (ICN)

Information-centric networking (ICN) is gradually becoming an alternative approach to the conventional Internet architecture through the distribution of enlightening information (named as content) on the Internet. Given the growing Internet traffic, the adapted information-centric architecture is expected to more favourably serve the user’s need of accessing data. In this paper, we proposed a novel framework for cache management in ICN that jointly considers the caching strategy and network coding. For the proposed cache management framework, we first evaluated a new Centrality-measures to assess the variation in the behaviour of each node in the network and to identify such behaviour. Subsequently, we developed an efficient random network coding-based cache management algorithm to obtain a support for caching and multicasting in the ICN paradigm. Extensive simulations were made, and related results showed that our proposed scheme has better performance in terms of cache hit ratio, average download time, server hit reduction ratio, and traffic overhead compared with other schemes in our simulated scenarios.

SECM: status estimation and cache management algorithm in opportunistic networks

In online social network, many data are created by human activities. Data communication in social networks can be recorded and analyzed by evaluation. The identification of nodes with the same types of attribute among numerous users has become a research problem in the field of social opportunistic network. Many mobile devices by users have to save and deliver information moment by moment. However, cache space with devices in social network is limited; a large amount of data must be saved and transmitted on time in online social networks. Delay and low delivery ratio may affect communication in opportunistic network. Thus, we propose an algorithm based on user nodes and neighbors in opportunistic networks to improve the transmission environment. Such networks can identify surrounding neighbors to evaluate nodes with probability, which will evaluate neighbors, ensure the high probability of node preferential access to information, and achieve the objectives of cache adjustment. Cache by node can be distributed reasonably. Experiments and a comparison of social opportunistic network algorithms show that the proposed method improves the cache utilization rate of nodes, reduces data transmission delay, and improves the overall network efficiency.

Information cache management and data transmission algorithm in opportunistic social networks

In social networks, nodes should analyze communication area during data transmission and find suitable neighbors to perform effective data classification transmission. This is similar to finding certain transmission destinations during data transmission with mobile devices. However, cache space with node in social opportunistic networks is limited, and waiting for destination node could also cause end-to-end delay. To improve the transmission environment, this study established a node recognition method for assessment probability, to satisfy the priority adjustment for the high probability nodes of cache, and then cache space should be reconstructed. To avoid accidentally deleting cached data, the cache task of the node is shared through the neighbor node cooperation, and the effective data transmission is performed. Through experiments and the comparison of social network algorithms, the proposed scheme improves delivery ratio by 82% and reduces delay by 74% with the traditional algorithms on average.

A Cache Management Algorithm for the Heterogeneous Storage Systems

A Cache Management Algorithm for the Heterogeneous Storage Systems

A minimum cost cache management framework for information-centric networks with network coding

In recent years, the increasing demand for media-rich content has driven many efforts to redesign the Internet architecture. As one of the main candidates, information-centric network(ICN) has attracted significant attention, where in-network cache is a key component in different ICN architectures. In this paper, we propose a novel framework for optimal cache management in ICNs that jointly considers caching strategy and content routing. Specifically, our framework is based on software-defined networking (SDN) where a controller is responsible for determining the optimal caching strategy and content routing via linear network coding (LNC). For the proposed cache management framework, we first formulate an optimization problem to minimize the network bandwidth cost and cache cost by jointly considering caching strategy and content routing with LNC. We then develop an efficient network coding based cache management (NCCM) algorithm to obtain a near-optimal caching and routing solution for ICNs. We further derive the upper and lower bounds of the problem and conduct extensive experiments to compare the performance of the NCCM algorithm with these bounds. Simulation results validate the effectiveness of the NCCM algorithm and the framework.

A pattern-based evaluation of download and cache management algorithms for annotated interactive non-linear videos

With modern technologies, it is possible to create annotated interactive non-linear videos (a form of hypervideo) for the Web. These videos have a non-linear structure of linked scenes to which additional information (other media like images, text, audio, or additional videos) can be added. A variety of user interactions—like in- and between-scene navigation or zooming into additional information—are possible in players for this type of video. Like linear video, quality of experience (QoE) with annotated hypervideo experiences is tied to the temporal consistency of the video stream at the client end—its flow. Despite its interactive complexity, users expect this type of video experience to flow as seamlessly as simple linear video. However, the added hypermedia elements bog playback engines down. Download and cache management systems address the flow issue, but their effectiveness is tied to numerous questions respecting user requirements, computational strategy, and evaluative metrics. In this work, we (a) define QoE metrics, (b) examine structural and behavioral patterns of interactive annotated non-linear video, (c) propose download and cache management algorithms and strategies, (d) describe the implementation of an evaluative simulation framework, and (e) present the algorithm test results.

Improving Flash-Based Disk Cache with Lazy Adaptive Replacement

For years, the increasing popularity of flash memory has been changing storage systems. Flash-based solid-state drives (SSDs) are widely used as a new cache tier on top of hard disk drives (HDDs) to speed up data-intensive applications. However, the endurance problem of flash memory remains a concern and is getting worse with the adoption of MLC and TLC flash. In this article, we propose a novel cache management algorithm for flash-based disk cache named Lazy Adaptive Replacement Cache (LARC). LARC adopts the idea of selective caching to filter out seldom accessed blocks and prevent them from entering cache. This avoids cache pollution and preserves popular blocks in cache for a longer period of time, leading to a higher hit rate. Meanwhile, by avoiding unnecessary cache replacements, LARC reduces the volume of data written to the SSD and yields an SSD-friendly access pattern. In this way, LARC improves the performance and endurance of the SSD at the same time. LARC is self-tuning and incurs little overhead. It has been extensively evaluated by both trace-driven simulations and synthetic benchmarks on a prototype implementation. Our experiments show that LARC outperforms state-of-art algorithms for different kinds of workloads and extends SSD lifetime by up to 15.7 times.

Toward Cost-Effective Mobile Video Streaming via Smart Cache With Adaptive Thresholding

Mobile video streaming has become the leading contributor to the explosive growth of mobile Internet traffic. As a key system parameter of mobile video apps, cache threshold plays an important role in regulating the downloading behavior of a mobile device, and directly affects the efficacy of mobile video streaming. In this paper, we first conduct a series of well-designed experiments to understand the mechanism of cache management in the Android OS and investigate the impact of cache threshold on the performance of mobile video streaming. The experiments show that the current static configuration of cache thresholds in the Android OS cannot well balance the tradeoff between cost incurred by unconsumed content and user quality of experience. To achieve cost-effective mobile video streaming, this paper further proposes a control-theoretic cache management algorithm called smart cache with adaptive thresholding (SCAT), which can intelligently tune cache thresholds to satisfy user preferences. The complexity of cache management and optimization can be decreased extensively by the SCAT strategy, facilitating its easy integration with the OS kernel codes. Finally, we implement and evaluate our proposed scheme on the real Android platform and the experimental results verify that our proposed scheme achieves significant gain over other alternative approaches. Compared to the Android’s default scheme, SCAT achieves over 40% reduction of unconsumed content cost and nearly 30% reduction of freezing duration in the low-bandwidth network environment.

RFFE: A Buffer Cache Management Algorithm for Flash-Memory-Based SSD to Improve Write Performance

Flash-memory-based solid-state drives use multiple NAND flash memory chips as storage media and deploy a large-sized random access memory (RAM) inside it. This RAM buffer absorbs the read and write requests by file systems, and thus the resulting write requests to NAND flash memory are determined by the buffer replacement scheme. Many of the previously proposed algorithms concentrate on improving the random write performance by reordering the writes, addressing the temporal locality, or evicting the clean pages beforehand. However, the sequential write patterns in the incoming write stream are not completely utilized by the flash translation layer; this increases garbage collection overhead. To overcome this limitation, we propose a novel algorithm, called random first flash enlargement (RFFE), to improve the performance of the write operation. The algorithm identifies the interleaved sequential writes and builds various policy decisions, and the write sequence is constructed by contemplating the flash memory characteristics. In particular, the write stream is written into an appropriate log block area. Trace driven simulation is compared with the previously proposed least recently used fully-associative sector translation (FAST), block padding least recently used (BPLRU), and recently-evicted-first (REF) buffer management schemes. The result shows that the RFFE outperforms the previously proposed schemes with respect to merge, erase, and write count.

Heterogeneous-aware cache partitioning: Improving the fairness of shared storage cache

We explore the un-fairness problem when heterogeneous applications share the cache.A dynamic partitioning caching algorithm is proposed for fairness and performance.We use both theory analysis and experiments to support our algorithm.The experiments show that our algorithm gets significant improvement in fairness.The experiments show that our algorithm get slightly improvement in performance. In this paper, we investigate the problem of fair storage cache allocation among multiple competing applications with diversified access rates. Commonly used cache replacement policies like LRU and most LRU variants are inherently unfair in cache allocation for heterogeneous applications. They implicitly give more cache to the applications that has high access rate and less cache to the applications of slow access rate. However, applications of fast access rate do not always gain higher performance from the additional cache blocks. In contrast, the slow application suffer poor performance with a reduced cache size. It is beneficial in terms of both performance and fairness to allocate cache blocks by their utility.In this paper, we propose a partition-based cache management algorithm for a shared cache. The goal of our algorithm is to find an allocation such that all heterogeneous applications can achieve a specified fairness degree as least performance degradation as possible. To achieve this goal, we present an adaptive partition framework, which partitions the shared cache among competing applications and dynamically adjusts the partition size based on predicted utility on both fairness and performance. We implement our algorithm in a storage simulator and evaluate the fairness and performance with various workloads. Experimental results show that, compared with LRU, our algorithm achieves large improvement in fairness and slightly in performance.

Exploiting application-level similarity to improve SSD cache performance in Hadoop

To boost the performance of massive data processing, solid-state drives (SSDs) have been used as a kind of cache in the Hadoop system. However, most of existing SSD cache management algorithms are ...

Distributed Cache Management in Information-Centric Networks

The main promise of current research efforts in the area of Information-Centric Networking (ICN) architectures is to optimize the dissemination of information within transient communication relationships of endpoints. Efficient caching of information is key to delivering on this promise. In this paper, we look into achieving this promise from the angle of managed replication of information. Management decisions are made in order to efficiently place replicas of information in dedicated storage devices attached to nodes of the network. In contrast to traditional off-line external management systems we adopt a distributed autonomic management architecture where management intelligence is placed inside the network. Particularly, we present an autonomic cache management approach for ICNs, where distributed managers residing in cache-enabled nodes decide on which information items to cache. We propose four on-line intra-domain cache management algorithms with different level of autonomicity and compare them with respect to performance, complexity, execution time and message exchange overhead. Additionally, we derive a lower bound of the overall network traffic cost for a certain category of network topologies. Our extensive simulations, using realistic network topologies and synthetic workload generators, signify the importance of network wide knowledge and cooperation.

Self-Adaptive Replacement and Pre-dispatch Algorithm Considering Spatial Relationship Constraint for 3D Spatial Data

Since processing of large scale 3D spatial data is far exceeding the current calculation capacity of computer hardware, it is impossible that the whole 3D data could be loaded into memory at one time during 3D scene display and analysis visualization. Dynamic data dispatch is needed according to the scope of real-time 3D scene. In this case, traditional cache processing methods are inefficient. Aiming at the feature of spatial data, a new cache management algorithm is proposed based on spatial relationship constraint. Its kernel idea is to build spatial index for objects in the cache considering their spatial position factors, and thus improve the traditional cache replacement algorithm which only based on time and hit rate. On the other hand, 3D spatial data pre-dispatch method considering influence factor is put forward by extending the cache spatial index with R-tree to spatial object sample tree. Integrated module with this cache replacement and pre-dispatch approach become a part of 3D spatial data engine as a database management tool. The practice in 3D urban planning information system of Wuhan city, central China, has proved the validity and efficiency of this method on dynamic dispatch and analysis processing with large scale spatial data with size of more than Tb.

An Identifier-to-Locator Mapping Buffer Management Algorithm Based on Aimed Pushing and Pre-Fetching Method

This paper analyzed the features of a new network architecture whose locator and identifier are separated, in order to improve the query and replacement efficiency of the identifier mapping, the backup of the locator/identifier mapping information needs storing in the most necessary place, which is a problem of cache management in fact .In this paper the typical cache management algorithms are analyzed firstly, and then a new aimed pushing and pre-fetching strategy is proposed according to the bidirectional interactive character of the communication activity in querying the mapping relationship, thus divided the Access Switch Router(ASR) cachespace into three section: Waiting_First_Access_Section (WFA), Frequently_Used_se- ction(FU) and Session_Duration_Section (SDU). And based on the identity mapping information reuse probability predicted by the Markov model, an information lifetime-based adaptive cache management algorithm is proposed. The simulation result shows that the performance of the buffer management algorithm proposed by this paper is better than those of the existing cache management algorithms such as LFU, LRU and LFU-LRU

Performance Analysis and Improvement of Storage Virtualization in an OS-Online System

An OS-online system called TransCom is based on a virtual storage system that supports heterogeneous services of the operating system and applications online. In TransCom, OS and software which run in the client are stored on the centralized servers, while computing tasks are carried out by the clients, so the server is the bottleneck of the system performance. This paper firstly analyzes the characteristics of its real usage workload and builds a queuing model to locate the system bottlenecks. According to the study, the disk is the primary bottleneck, so an optimal two-level cache arrangement policy is developed on both the server and the client, which aims to avoid most of the server disk accesses. LRU algorithm is used in the client-side cache. A cache management algorithm called Frequency-based Multi-Priority Queues (FMPQ) proposed in this paper is used in the server-side cache. Experiment results show that the appropriate cache arrangement can significantly improve the capability of the TransCom server.

CaPaS: an optimal security-aware cache replacement algorithm for cluster storage systems

In this paper, we introduce a novel dynamic cache management approach, i.e., a cache partitioning system (CaPaS), to optimise security levels in contemporary cluster storage systems for data-intensive applications with acceptable disk response times. CaPaS utilises an adaptive security control mechanism to adjust for varying workload conditions initiated by client-issued disk requests as well as security requirements and contains a cache partitioning scheme, a response-time estimator, and an adaptive security quality controller. The CaPaS algorithm solves a non-linear optimisation problem to increase the quality of security of disk requests in cluster storage systems while ensuring the disk requests to be completed within their desired response times. The efficiency of the proposed algorithm has been tested with the newly defined performance indicators on a cluster storage system including CaPaS, eight cryptographic and seven integrity services. Numerical simulation results demonstrate that the CaPaS algorithm improves the overall system performance enormously over two traditional baseline cache management algorithms.

An information-theoretic framework for field monitoring using autonomously mobile sensors

We consider a mobile sensor network monitoring a spatio-temporal field. Given limited caches at the sensor nodes, the goal is to develop a distributed cache management algorithm to efficiently answer queries with a known probability distribution over the spatial dimension. First, we propose a novel distributed information theoretic approach assuming knowledge of the distribution of the monitored phenomenon. Under this scheme, nodes minimize an entropic utility function that captures the average amount of uncertainty in queries given the probability distribution of query locations. Second, we propose a correlation-based technique, which only requires knowledge of the second-order statistics, relaxing the stringent constraint of a priori knowledge of the query distribution, while significantly reducing the computational overhead. We show that the proposed approaches considerably improve the average field estimation error. Further, we show that the correlation-based technique is robust to model mismatch in case of imperfect knowledge of the underlying generative correlation structure.