Existing Caching Algorithms Research Articles

A Universal SMR-aware Cache Framework with Deep Optimization for DM-SMR and HM-SMR Disks

To satisfy the enormous storage capacities required for big data, data centers have been adopting high-density shingled magnetic recording (SMR) disks. However, the weak fine-grained random write performance of SMR disks caused by their inherent write amplification and unbalanced read–write performance poses a severe challenge. Many studies have proposed solid-state drive (SSD) cache systems to address this issue. However, existing cache algorithms, such as the least recently used (LRU) algorithm, which is used to optimize cache popularity, and the MOST algorithm, which is used to optimize the write amplification factor, cannot exploit the full performance of the proposed cache systems because of their inappropriate optimization objectives. This article proposes a new SMR-aware cache framework called SAC+ to improve SMR-based hybrid storage. SAC+ integrates the two dominant types of SMR drives—namely, drive-managed and host-managed SMR drives—and provides a universal framework implementation. In addition, SAC+ integrally combines the drive characteristics to optimize I/O performance. The results of evaluations conducted using real-world traces indicate that SAC+ reduces the I/O time by 36–93% compared with state-of-the-art algorithms.

Echo State Network-Based Content Prediction for Mobile Edge Caching Networks

With the rapid development of internet communication and the wide application of intelligent terminal, moving the cache to the edge of the network is an effective solution to shorten the delay of users accessing content. However, the existing cache work lacks the comprehensive consideration of users and content, resulting in low cache hit ratio and low accuracy of the whole system. In this paper, the authors propose a collaborative caching model that considers both user request content and content prediction, so as to improve the caching performance of the whole network. Firstly, the model uses the clustering algorithm based on Akike information criterion to cluster users. Then, combined with the clustering results, echo state network is used as the machine learning framework to predict the content. Finally, the cache contents are selected according to the prediction results and cached in the cache unit of the small base station. Simulation results show that compared with the existing cache algorithms, the proposed method has obvious improvement in cache hit ratio, accuracy, and recall rate.

Joint Transcoding- and Recommending-Based Video Caching at Network Edges

Edge caching can significantly improve the quality of wireless video services by deploying cache servers at network edges. Recently, video conversion and recommendation have been introduced to improve the caching performance at the edges. Specifically, they work to produce lower quality versions of videos via video converting (for the former) or provide alternative similar videos when requested videos are not available by using video recommendation (for the latter). However, existing work in this aspect has utilized these two techniques separately, which largely limit their capabilities in providing improved video services. In this article, we study how to jointly utilize these two techniques in edge caching for improved caching performance. The objective is to maximally reduce the video delivery delay while satisfying users’ requirements. We first formulate the optimal video caching problem in this case and derive its NP-hardness. We, then, propose an effective transcoding- and recommending-based caching algorithm (TRBA). The TRBA works in a greedy manner to iteratively buffer the most valuable video versions, one video version each time, until no more video version or cache space is available. We define the value of a video version as the reduced delivery delay if this version were buffered divided by the extra cache space required for its buffering. The computational complexity of TRBA is deduced as <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$O(|V|^3|Q|^3)$</tex-math></inline-formula> , where <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$|V|$</tex-math></inline-formula> and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$|Q|$</tex-math></inline-formula> represent the total number of videos and the number of versions per video, respectively. Numerical results demonstrate that, compared with existing caching algorithms, TRBA can significantly improve the caching performance.

PPVC: Online Learning Toward Optimized Video Content Caching

Today’s Internet traffic has been dominated by video contents. To efficiently serve online videos for millions of users, it is essential to cache frequently requested videos on various devices such as edge servers, personal computers, etc. Existing caching algorithms are mainly designed by leveraging the information of past request records. However, it is insufficient to only use simple statistics of past request records, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">e.g.</i> , video popularity which ignores the occurrence time of past video request events and the discrepancies among individual devices. In this paper, we propose a radically different learning-based video caching algorithm for Internet devices, called <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">PPVC (Point Process based Video Cache)</i> which can take exact video request patterns into account. By utilizing Hawkes Process (HP), we are able to link the future video request rates of a device with three kinds of historical records, namely, historical requests for the same video launched by the device itself, historical requests for the same video from other devices with a similar request pattern, and historical requests for other similar videos from the same device. The video request patterns can be efficiently computed via Singular Value Decomposition (SVD). Parameters linking these historical events can be determined by maximizing the likelihood of historical events. To further reduce the computation load, we also propose an online version of PPVC, which can timely update cached videos with the incremental arrival of events. One nice property of PPVC is that it can adapt to the change of video popularity very fast and is also applicable to Internet devices at different levels, ranging from the high-level servers that serve a large population of users to the low-level user devices ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">e.g.</i> , personal computers). Finally, we conduct extensive simulations with real traces and the results show that PPVC can always achieve the best video caching performance in terms of the hit rate on all levels of Internet devices, and quickly adapt to the dynamics of video requests.

Fair and Efficient Caching Algorithms and Strategies for Peer Data Sharing in Pervasive Edge Computing Environments

Edge devices with sensing, storage, and communication resources (e.g., smartphones, tablets, connected vehicles, and IoT nodes) are increasingly penetrating our daily lives. Many novel applications can be created through sharing data among nearby peer edge devices. In such applications, caching data at some edge devices can greatly improve data availability, retrieval robustness, and delivery latency. In this paper, we study the unique problem of caching fairness in edge computing environments. Due to the heterogeneity of peer edge devices, load balance is a critical issue that affects the fairness in caching. We propose fairness metrics to characterize this issue and formulate the caching fairness problem as an integer linear programming problem, which is shown as the summation of multiple Connected Facility Location (ConFL) problems. We provide an approximation algorithm by leveraging an existing ConFL approximation algorithm, and prove that it preserves a 6.55 approximation ratio. We further develop a distributed algorithm where devices exchange data reachability information and identify popular candidates as caching nodes. Finally, we update the fairness metric and apply it to algorithms for making continuous caching decisions over time. Our extensive evaluation results show that compared with existing caching algorithms for wireless networks, our proposed algorithms significantly improve the data caching fairness while keeping the contention induced latency comparable to the best existing algorithms.

Adaptive probabilistic caching technique for caching networks with dynamic content popularity

This paper presents a new adaptive probabilistic cache algorithm (AProb) for modern caching networks. AProb is based on three main techniques: (1) dynamic probabilistic caching; (2) ghost list; and (3) adaptive probing and protection. It enables caching systems to quickly adjust their cached data to dynamic content popularity without intervention of network administrators and synchronization. The criteria of this adjustment are based on hit events occurring in AProb data structures. By using AProb, a caching system continuously adapts a caching probability and the ratio between probing and protection partitions of its cache. AProb has constant time complexity and its space overhead is minimal. Extensive computer simulations, which consider various network topologies and traffic traces, show that AProb offers improvement in terms of server-hit ratio, footprint distance, and caching time compared with those provided by several existing cache algorithms.

Caching for Heterogeneous Small-Cell Networks With Bandwidth Allocation and Caching-Aware BS Association

This letter studies the caching problem for heterogeneous small-cell networks with bandwidth allocation and caching-aware base station association. The caching control and bandwidth allocation problem aims at minimizing the request miss ratio. To solve this problem, we propose a line-search-based-iterative (LSBI) algorithm which determines the solution by combining the line-search algorithm to obtain the optimal bandwidth allocation with the iterative caching algorithm to acquire a caching solution. Numerical results demonstrate that the LSBI algorithm significantly outperforms existing caching algorithms, and is on a par with a performance bound.

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.

ARC-H: Adaptive replacement cache management for heterogeneous storage devices

Heterogeneous storage architectures combine the strengths of different storage devices in a synergistically useful fashion, and are increasingly being used in mobile storage systems. In this paper, we propose ARC-H, an adaptive cache replacement algorithm for heterogeneous storage systems consisting of a hard disk and a NAND flash memory. ARC-H employs a dynamically adaptive management policy based on ghost buffers and takes account of recency, I/O cost per device, and workload patterns in making cache replacement decisions. Realistic trace-driven simulations show that ARC-H reduces service time by up to 88% compared with existing caching algorithms with a 20Mb cache. ARC-H also reduces energy consumption by up to 81%.

Guest editorial: Networked television

This special issue on networked television consists of a selection of the best papers of the 7th European Interactive TV Conference (EuroITV 2009), which was held in Leuven, Belgium, in June of 2009. Networked television is a new concept, which is about TV sets becoming yet another node in many IP-based networks, such as managed IPTV networks and in-home networks. This increased level of connectivity allows for new types of services and will eventually result in the seamless integration of interactive TV into the wide range of other services that people have at their disposal. The papers in this special issue consider various challenges to make this vision a reality, such as scalability of catch-up TV services and the integration of interactive TV services and home control automation. The goal of EuroITV 2009 was to provide a discussion and dissemination platform for topics like these, both for industry and academia, so as to fuel new business and worldclass research in networked television. The conference program consisted of a technical track, a business track, and a track on user studies, embodying EuroITV’s unique multidisciplinary approach. The conference acceptance rate was 41% out of 88 submissions, which illustrates that it is a selective and popular event. The presented papers were from researchers and practitioners from Europe, Asia, and the Americas. EuroITV 2009 was attended by a total of 197 people, many of whom also contributed to the pre-conference workshops, the industry track, the tutorials, and the doctoral consortium. From the papers presented in Leuven, we invited the best six to submit an extended version of their work for this special issue of Multimedia System Journal, with the requirement that they should top up their EuroITV paper with at least 30% of new results. After a multi-round reviewing process with three or four experts reviewing each paper, we were able to retain three papers, which are the ones that are now part of this special issue. In the first paper, Avramova, De Vleeschauwer, Wittevrongel, and Bruneel present a rigorous performance analysis of a distributed caching algorithm for catch-up television. The authors developed the algorithm to avoid overloads in managed IPTV networks and evaluated it through extensive simulations. They conclude that their new algorithm clearly outperforms existing caching algorithms and also investigate how to configure the algorithm’s parameters for optimal performance. The second paper, by Viana and de Lucena, is about a software platform that seamlessly integrates interactive TV and home control services. The main advantage of their work is that the platform allows developers to easily write mixed interactive TV-home control applications. Such applications also facilitate new user experiences, such as synchronizing home appliances with the audio/video streams of a movie. The novelty of their work is that the platform transparently combines Ginga middleware technology (for interactive TV) with OSGi (for home control), while at the same time exploiting the declarative part of the Ginga middleware. C. Hesselman (&) Novay, P.O. Box 589, 7500AN Enschede, The Netherlands e-mail: cristian.hesselman@novay.nl