Cache Management Research Articles

Location-assistant content distribution scheme for emergency rescue

In order to optimize the timeliness and pertinence of message delivery in emergency rescue scenarios and improve the service performance of emergency communications network, we propose a location-assistant content distribution scheme based on delay tolerant network. First of all, considering that the movement patterns of rescue teams tend to follow a predetermined course of action, we design a location-based group mobility model. Due to the intermittent network connectivity and variety of emergency service, a content-classification-based publish/subscribe architecture and a GenericSpray routing algorithm based on the prediction of overlap opportunity in spatio-temporal positions are proposed. Furthermore, we also give a cache management strategy based on the content significance. Since location-assistant content distribution scheme can predict the overlap of activity between rescue teams through the course of action, not only can the number of copy forwarding and message delivery delays be significantly reduced but also the priority delivery of important messages can be ensured by message classification. Simulation experiments show that compared with the traditional delay tolerant network routing algorithm and the classic first-in-first-out caching strategy, location-assistant content distribution scheme improves the performance of message delivery rate, transmission delay, and control overhead significantly.

PARC: A novel OS cache manager

SummaryTo boost input‐output performance, operating systems employ a kernel‐managed caching space called the buffer cache or page cache. Given the limited size of a buffer cache, an effective cache manager is required to decide which blocks should be evicted from the cache. Previous cache managers use historical information to make replacement decisions. However, existing approaches are unable to maximize performance since they rely on limited historical information. Motivated by the limitations of existing solutions, this paper proposes a novel manager called the Pattern‐assisted Adaptive Recency Caching (PARC) manager. PARC simultaneously uses the historical information of recency, frequency, and access patterns to estimate the locality strengths of blocks and, upon a cache miss, evicts the block with the least strength. Specifically, PARC exploits the reference regularities exhibited in past input‐output behaviors to actively and rapidly adapt the recency and frequency information of blocks so as to precisely distinguish blocks with long‐ and short‐term utility. Through comprehensive simulations on a variety of traces of different access patterns, we show that PARC is robust since, except for random workloads where the performance of each cache manager is similar, PARC always outperforms existing solutions.

DIDACache

Key-value caching is crucial to today’s low-latency Internet services. Conventional key-value cache systems, such as Memcached, heavily rely on expensive DRAM memory. To lower Total Cost of Ownership, the industry recently is moving toward more cost-efficient flash-based solutions, such as Facebook’s McDipper [14] and Twitter’s Fatcache [56]. These cache systems typically take commercial SSDs and adopt a Memcached-like scheme to store and manage key-value cache data in flash. Such a practice, though simple, is inefficient due to the huge semantic gap between the key-value cache manager and the underlying flash devices. In this article, we advocate to reconsider the cache system design and directly open device-level details of the underlying flash storage for key-value caching. We propose an enhanced flash-aware key-value cache manager, which consists of a novel unified address mapping module, an integrated garbage collection policy, a dynamic over-provisioning space management, and a customized wear-leveling policy, to directly drive the flash management. A thin intermediate library layer provides a slab-based abstraction of low-level flash memory space and an API interface for directly and easily operating flash devices. A special flash memory SSD hardware that exposes flash physical details is adopted to store key-value items. This co-design approach bridges the semantic gap and well connects the two layers together, which allows us to leverage both the domain knowledge of key-value caches and the unique device properties. In this way, we can maximize the efficiency of key-value caching on flash devices while minimizing its weakness. We implemented a prototype, called DIDACache, based on the Open-Channel SSD platform. Our experiments on real hardware show that we can significantly increase the throughput by 35.5%, reduce the latency by 23.6%, and remove unnecessary erase operations by 28%.

A Smart Congestion Control Mechanism for the Green IoT Sensor-Enabled Information-Centric Networking.

Information-Centric Networking (ICN) is a new Internet architecture design, which is considered as the global-scale Future Internet (FI) paradigm. Though ICN offers considerable benefits over the existing IP-based Internet architecture, its practical deployment in real life still has many challenges, especially in the case of high congestion and limited power in a sensor enabled-network for the Internet of Things (IoT) era. In this paper, we propose a smart congestion control mechanism to diminish the network congestion rate, reduce sensor power consumptions, and enhance the network performance of ICN at the same time to realize a complete green and efficient ICN-based sensor networking model. The proposed network system uses the chunk-by-chunk aggregated packets according to the content popularity to diminish the number of exchanged packets needed for data transmission. We also design the sensor power-based cache management strategy, and an adaptive Markov-based sensor scheduling policy with selective sensing algorithm to further maximize power savings for the sensors. The evaluation results using ndnSIM (a widely-used ICN simulator) show that the proposed model can provide higher network performance efficiency with lower energy consumption for the future Internet by achieving higher throughput with higher cache hit rate and lower Interest packet drop rate as we increase the number of IoT sensors in ICN.

FluteDB: An efficient and scalable in-memory time series database for sensor-cloud

Recently, with the widespread use of large-scale sensor network, time series data is vastly generated and requires to be processed. However, those traditional databases show their limitations on storage when handling such a large stream data in cloud, and even their actual reliability and availability are also difficult to be guaranteed. To deal with the problem, this paper proposes FluteDB, an efficient and scalable in-memory time series database for sensor-cloud. We adequately analyze the unique characteristics of time series data and its relevant operations to strike the right balance among efficiency, scalability, resources consumption, reliability and availability. Specifically, on basis of the aggregate analysis of root cause for ongoing time series problems, FluteDB targeted optimizes the strategies for key operations in memory and physical storage, at the expense of partial acceptable data precision and consistency. FluteDB’s enhanced strategies are primarily comprised of Triggered Time Series Merge Tree (TTSM Tree), time series enhanced cache management and corresponding compression algorithms for different data types. The validations of all sub-modules have demonstrated that our improved strategies outperform existing methods in real time series environment significantly. Global experimental results also show that the integrated FluteDB reduces query latency by 17x, improves write rate by 98x and saves about 47% storage resources. The average available service time and recovery rate and degree of FluteDB are competitive with the state-of-the-art reliability and availability strategy in real and simulated faults, which demonstrates FluteDB can provide highly stable large-scale data cloud services.

Boosting NVDIMM Performance With a Lightweight Caching Algorithm

In the big data era, data-intensive applications have growing demand for the capacity of DRAM main memory, but the frequent DRAM refresh, high leakage power, and high unit cost bring serious design issues on scaling up DRAM capacity. To address this issue, a nonvolatile dual inline memory module (NVDIMM), which is a hybrid memory module, becomes a possible alternative to replace the DRAM as main memory in some data-intensive applications. The NVDIMM that consists of a small-sized high-speed DRAM and a large-sized low-cost nonvolatile memory (i.e., flash memory) has the serious performance issue on accessing data stored in the flash memory because of the huge performance gap between the DRAM and the flash memory. However, there is limited room to adopt a complex caching algorithm for using the DRAM as the cache of flash memory in the NVDIMM main memory, because a complex caching algorithm itself would already cause too much performance degradation on handling each request to access the NVDIMM main memory. In this paper, we present a lightweight caching algorithm to boost NVDIMM performance by minimizing the cache management overhead and reducing the frequencies to access flash memory. A series of experiments was conducted based on popular benchmarks, and the results demonstrate that the proposed algorithm can effectively improve the performance of the NVDIMM main memory.

Shared Last-Level Cache Management and Memory Scheduling for GPGPUs with Hybrid Main Memory

Memory intensive workloads become increasingly popular on general purpose graphics processing units (GPGPUs), and impose great challenges on the GPGPU memory subsystem design. On the other hand, with the recent development of non-volatile memory (NVM) technologies, hybrid memory combining both DRAM and NVM achieves high performance, low power, and high density simultaneously, which provides a promising main memory design for GPGPUs. In this article, we explore the shared last-level cache management for GPGPUs with consideration of the underlying hybrid main memory. To improve the overall memory subsystem performance, we exploit the characteristics of both the asymmetric read/write latency of the hybrid main memory architecture, as well as the memory coalescing feature of GPGPUs. In particular, to reduce the average cost of L2 cache misses, we prioritize cache blocks from DRAM or NVM based on observations that operations to NVM part of main memory have a large impact on the system performance. Furthermore, the cache management scheme also integrates the GPU memory coalescing and cache bypassing techniques to improve the overall system performance. To minimize the impact of memory divergence behaviors among simultaneously executed groups of threads, we propose a hybrid main memory and warp aware memory scheduling mechanism for GPGPUs. Experimental results show that in the context of a hybrid main memory system, our proposed L2 cache management policy and memory scheduling mechanism improve performance by 15.69% on average for memory intensive benchmarks, whereas the maximum gain can be up to 29% and achieve an average memory subsystem energy reduction of 21.27%.

RT-CaCC: A Reliable Transport With Cache-Aware Congestion Control Protocol in Wireless Sensor Networks

Reliability of data transport in wireless sensor networks is critical in the presence of high packet loss level either due to link contentions or buffer congestions. Mechanisms such as intermediate caching and congestion control can enhance the reliability performance of transport protocols in the event of packet loss. However, these two mechanisms are designed independently for most cache-based transport protocols. In this paper, we developed a new reliable transport protocol with a cache-aware congestion control mechanism called RT-CaCC. RT-CaCC utilizes cache management policies such as cache insertion, cache elimination, and cache size allocation to mitigate packet losses in the network while maximizing cache utilization and bandwidth allocation. The protocol was evaluated in network topologies with different packet loss scenarios. Results showed that RT-CaCC obtained an average of 15%–38% improvement gain compared with baseline protocols. In addition, RT-CaCC consistently outperformed other cache-based transport protocols like distributed TCP caching and end-to-end reliable and congestion aware transport layer protocol in terms of cache utilization, end-to-end delay, and fairness metrics. Finally, the comparison between simulation and analytical results showed that there is an almost perfect match with the difference between curve points never exceeding 14% of the analytical cost.

Prefetch-aware fingerprint cache management for data deduplication systems

Data deduplication has been widely utilized in large-scale storage systems, particularly backup systems. Data deduplication systems typically divide data streams into chunks and identify redundant chunks by comparing chunk fingerprints. Maintaining all fingerprints in memory is not cost-effective because fingerprint indexes are typically very large. Many data deduplication systems maintain a fingerprint cache in memory and exploit fingerprint prefetching to accelerate the deduplication process. Although fingerprint prefetching can improve the performance of data deduplication systems by leveraging the locality of workloads, inaccurately prefetched fingerprints may pollute the cache by evicting useful fingerprints. We observed that most of the prefetched fingerprints in a wide variety of applications are never used or used only once, which severely limits the performance of data deduplication systems. We introduce a prefetch-aware fingerprint cache management scheme for data deduplication systems (PreCache) to alleviate prefetch-related cache pollution. We propose three prefetch-aware fingerprint cache replacement policies (PreCache-UNU, PreCache-UOO, and PreCache-MIX) to handle different types of cache pollution. Additionally, we propose an adaptive policy selector to select suitable policies for prefetch requests. We implement PreCache on two representative data deduplication systems (Block Locality Caching and SiLo) and evaluate its performance utilizing three real-world workloads (Kernel, MacOS, and Homes). The experimental results reveal that PreCache improves deduplication throughput by up to 32.22% based on a reduction of on-disk fingerprint index lookups and improvement of the deduplication ratio by mitigating prefetch-related fingerprint cache pollution.

A Cooperative Cache Management Scheme for IEEE802.15.4 based Wireless Sensor Networks

Wireless Sensor Networks (WSNs) based on the IEEE 802.15.4 MAC and PHY layer standards is a recent trend in the market. It has gained tremendous attention due to its low energy consumption characteristics and low data rates. However, for larger networks minimizing energy consumption is still an issue because of the dissemination of large overheads throughout the network. This consumption of energy can be reduced by incorporating a novel cooperative caching scheme to minimize overheads and to serve data with minimal latency and thereby reduce the energy consumption. This paper explores the possibilities to enhance the energy efficiency by incorporating a cooperative caching strategy.

Enhanced Service Discovery Protocol for MANET by Effective Cache Management

With the increasing pervasiveness of the mobile nodes, service discovery has become inevitable. In the resource constrained mobile nodes, caching is being used to reduce the access cost. The enhanced service discovery protocol presented in this paper is a combination of cache placement, cache discovery, cache consistency and replacement algorithms. The overheard service information is cached by the coordinator nodes of each geographical area to reduce the discovery delay. A light weight cache discovery algorithm that uses a minimum spanning tree is proposed. The cache consistency is maintained in a soft state. Cache replacement is done based on a weight value that is calculated using the recency, frequency and cost factors. An M/G/1/∞ non-preemptive queuing model is proposed to analyse the cache replacement performance. The extensive simulation results performed to demonstrate the effectiveness of the proposed enhanced service discovery protocol in terms of reduction in the service discovery delay and number of cache replacements. The replacement scheme proposed has increased the cache hit ratio.

A Differentiated Caching Mechanism to Enable Primary Storage Deduplication in Clouds

Existing primary deduplication techniques either use inline caching to exploit locality in primary workloads or use post-processing deduplication to avoid the negative impact on I/O performance. However, neither of them works well in the cloud servers running multiple services for the following two reasons: First, the temporal locality of duplicate data writes varies among primary storage workloads, which makes it challenging to efficiently allocate the inline cache space and achieve a good deduplication ratio. Second, the post-processing deduplication does not eliminate duplicate I/O operations that write to the same logical block address as it is performed after duplicate blocks have been written. A hybrid deduplication mechanism is promising to deal with these problems. Inline fingerprint caching is essential to achieving efficient hybrid deduplication. In this paper, we present a detailed analysis of the limitations of using existing caching algorithms in primary deduplication in the cloud. We reveal that existing caching algorithms either perform poorly or incur significant memory overhead in fingerprint cache management. To address this, we propose a novel fingerprint caching mechanism that estimates the temporal locality of duplicates in different data streams and prioritizes the cache allocation based on the estimation. We integrate the caching mechanism and build a hybrid deduplication system. Our experimental results show that the proposed mechanism provides significant improvement for both deduplication ratio and overhead reduction.

Collaborative Caching and Matching for D2D Content Sharing

D2D content sharing is recognized as an effective response to the prevalence of multimedia and local services, especially for 5G edge networks. We construct a content sharing framework based on cache-assisted D2D communications, which consists of the content placement and delivery phases. When considering the random content location and the limited storage and transmission capability of devices, which content should be cached and how to optimally match potential providers to requesters of contents are of importance. As such, we extend the concept of cacheability, and accordingly propose a distributed collaborative cache management scheme. It involves a caching decision and update model based on the submodularity over matroid constraints, and a best effort distributed algorithm framework. Moreover, we extend the sharing mode category, and propose the sharing mode selection scheme by constructing a general social welfare maximization with efficient incentive mechanisms. By leveraging the total unimodularity, it can be simplified to its linear relaxation without loss of optimization.

Combining Software Cache Partitioning and Loop Tiling for Effective Shared Cache Management

One of the biggest challenges in multicore platforms is shared cache management, especially for data-dominant applications. Two commonly used approaches for increasing shared cache utilization are cache partitioning and loop tiling. However, state-of-the-art compilers lack efficient cache partitioning and loop tiling methods for two reasons. First, cache partitioning and loop tiling are strongly coupled together, and thus addressing them separately is simply not effective. Second, cache partitioning and loop tiling must be tailored to the target shared cache architecture details and the memory characteristics of the corunning workloads. To the best of our knowledge, this is the first time that a methodology provides (1) a theoretical foundation in the above-mentioned cache management mechanisms and (2) a unified framework to orchestrate these two mechanisms in tandem (not separately). Our approach manages to lower the number of main memory accesses by an order of magnitude keeping at the same time the number of arithmetic/addressing instructions to a minimal level. We motivate this work by showcasing that cache partitioning, loop tiling, data array layouts, shared cache architecture details (i.e., cache size and associativity), and the memory reuse patterns of the executing tasks must be addressed together as one problem, when a (near)-optimal solution is requested. To this end, we present a search space exploration analysis where our proposal is able to offer a vast deduction in the required search space.

Efficient flash-aware page-mapping cache management for on-board remote sensing image processing

Abstract The use of flash-based storage devices has become popular in both research and practice due to their intrinsic advantages over traditional magnetic counterparts. Many advanced flash-aware data management techniques have been developed to exploit the full potential of flash storage devices. It is then reasonable to adopt flash storage devices to deliver high I/O performance for on-board remote sensing image processing tasks, which pose particular challenges in data storage and I/O management. In this work, we focus on caching issues and propose a page-mapping cache management approach based on a page-level flash translation layer (FTL) to accommodate various types of workloads presented by different remote sensing image processing operations. The adaptability is achieved by three major techniques: 1) separating mapping cache into hot and cold regions according to access frequency; 2) a probability-based locality-aware cache replacement priority model to balance high hit ratio and low write/erase costs; and 3) adaptive mapping cache replacement algorithms that are seamlessly integrated with 1) and 2) to handle complicated I/O patterns efficiently. Combining these above three techniques, the proposed FTL-based cache management approach is superior to several existing methods, as demonstrated by extensive experiments running on realistic on-board remote sensing processing I/O traces.

Adaptive Wildcard Rule Cache Management for Software-Defined Networks

Software-Defined Networking enables flexible flow control by caching rules at OpenFlow switches. Wildcard rule caching enables management of traffic aggregates, reduces flow setup queries, and simplifies policy management. However, to guarantee correct packet matching, some rules that depend on the requested rule need to be cached as well, which leads to unnecessary flow table bloat and potential overflow. We have proposed a scheme called CAching rules in Buckets (CAB) to mitigate the dependency issue by partitioning the field space into buckets and caching rules associated with the requested buckets. In this paper, we propose the Adaptive Cache ManagEment (ACME) for CAB, which dynamically adjusts the sizes and shapes of buckets according to incoming traffic to achieve more efficient flow table utilization. The improvement also includes preloading rules that span a wide field space to reduce bandwidth usage in the control channel. We formalize the caching policies for CAB-ACME to guarantee the semantic correctness of packet classification. We evaluate the performance of CAB-ACME through software-based simulations and a prototype built with the OpenDaylight controller and hardware switches from multiple vendors. The results show that, compared with other rule caching schemes, CAB-ACME reduces the cache miss rate by one order of magnitude and the control channel bandwidth usage by a half. ACME also helps maintain a steadier performance under dynamic traffic changes compared with the baseline CAB design.

Toward Efficient Short-Video Sharing in the YouTube Social Network

The past few years have seen an explosion in the popularity of online short-video sharing in YouTube. As the number of users continue to grow, the bandwidth required to maintain acceptable quality of service (QoS) has greatly increased. Peer-to-peer (P2P) architectures have shown promise in reducing the bandwidth costs; however, the previous works build one P2P overlay for each video, which provides limited availability of video providers and produces high overlay maintenance overhead. To handle these problems, in this work, we novelly leverage the existing social network in YouTube, where a user subscribes to another user’s channel to track all his/her uploaded videos. The subscribers of a channel tend to watch the channel’s videos and common-interest nodes tend to watch the same videos. Also, the popularity of videos in one channel varies greatly. We study real trace data to confirm these properties. Based on these properties, we propose SocialTube, which builds the subscribers of one channel into a P2P overlay and also clusters common-interest nodes in a higher level. It also incorporates a prefetching algorithm that prefetches higher-popularity videos. To enhance the system performance, we further propose the demand/supply-based cache management scheme and reputation-based neighbor management scheme. Extensive trace-driven simulation results and PlanetLab real-world experimental results verify the effectiveness of SocialTube at reducing server load and overlay maintenance overhead and at improving QoS for users.

On Caching and Routing in Information-Centric Networks

With the exponential growth of content in recent years, users are primarily interested in obtaining particular content and are not concerned with the host housing the content. By treating content as a first class citizen, information- centric networks (ICN) seek to transform the Internet from a host-to-host communication model to a content-centric model. A key component of ICN is to cache content at storage-enabled routers. By caching content at in-network routers, network performance can be improved by delivering content from routers closer to the user and not from the origin servers (content custodians). In this article, we provide an overview of the state-of-the-art cache management and routing policies in ICN. We present a small comparison study of some state-of-the-art algorithms on a real Internet topology to help the reader appreciate how the different strategies compare against one another. Our simulation results demonstrate that it is hard to pick a winner among existing policies. We conclude the article with a discussion of open research questions.

An Adaptive Insertion and Promotion Policy for Partitioned Shared Caches

Cache replacement policies in chip multiprocessors (CMP) have been investigated extensively and proven able to enhance shared cache management. However, competition among multiple processors executing different threads that require simultaneous access to a shared memory may cause cache contention and memory coherence problems on the chip. These issues also exist due to some drawbacks of the commonly used Least Recently Used (LRU) policy employed in multiprocessor systems, which are because of the cache lines residing in the cache longer than required. In image processing analysis of for example extra pulmonary tuberculosis (TB), an accurate diagnosis for tissue specimen is required. Therefore, a fast and reliable shared memory management system to execute algorithms for processing vast amount of specimen image is needed. In this paper, the effects of the cache replacement policy in a partitioned shared cache are investigated. The goal is to quantify whether better performance can be achieved by using less complex replacement strategies. This paper proposes a Middle Insertion 2 Positions Promotion (MI2PP) policy to eliminate cache misses that could adversely affect the access patterns and the throughput of the processors in the system. The policy employs a static predefined insertion point, near distance promotion, and the concept of ownership in the eviction policy to effectively improve cache thrashing and to avoid resource stealing among the processors.

An efficient in‐network caching decision algorithm for Internet of things

SummaryRecently, content‐centric networking (CCN) has become one of the important technologies for enabling the future networks. Along with its recognized potentialities as a content retrieval and dissemination solution, CCN has been also recently considered as a promising architecture for the Internet of things (IoT), because of 2 main features such as named‐based routing and in‐network caching. However, IoT is characterized by challenging features: small storage capacity of resource‐constrained devices due to cost and limitation of energy and especially transient data that impose stringent requirements on the information freshness. As a consequence, the intrinsic caching mechanisms existing in CCN approach do not well suit IoT domains; hence, providing a specific caching policy at intermediate nodes is a very challenging task. This paper proposes an effective multiattribute in‐network caching decision algorithm that performs a caching strategy in CCN‐IoT network by considering a set of crucial attributes including the content store size, hop count, particularly key temporal properties like data freshness, and the node energy level. Simulation results proved that our proposed approach outperforms 2 cache management schemes (probabilistic least recently used and AlwaysCache–first in first out in terms of improving total hit rate, reducing data retrieval delay, and enhancing content reusability in IoT environment).