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Improving Cache Performance for Large-Scale Photo Stores via Heuristic Prefetching Scheme

IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS 2019年第9期30卷 2033-2045页

作者： Zhou, Ke Sun, Si Wang, Hua Huang, Ping He, Xubin Lan, Rui Li, Wenyan Liu, Wenjie Yang, Tianming Huazhong Univ Sci & Technol Wuhan Natl Lab Optoelect Key Lab Informat Storage Syst Intelligent Cloud Storage Joint Res Ctr Wuhan 430073 Hubei Peoples R China Tencent Wuhan 430073 Hubei Peoples R China Temple Univ Philadelphia PA 19122 USA Tencent Inc Shenzhen 518057 Peoples R China Huanghuai Univ Zhumadian 463000 Peoples R China

Photo service providers are facing critical challenges of dealing with the huge amount of photo storage, typically in a magnitude of billions of photos, while ensuring national-wide or world-wide satisfactory user experiences. Distributed photo caching architecture is widely deployed to meet high performance expectations, where efficient still mysterious caching policies play essential roles. In this work, we present a comprehensive study on internet-scale photo caching algorithms in the case of QQPhoto from Tencent Inc., the largest social network service company in China. We unveil that even advanced cache algorithms can only perform at a similar level as simple baseline algorithms and there still exists a large performance gap between these cache algorithms and the theoretically optimal algorithm due to the complicated access behaviors in such a large multi-tenant environment. We then expound the reasons behind this phenomenon via extensively investigating the characteristics of QQPhoto workloads. Finally, in order to realistically further improve QQPhoto cache efficiency, we propose to incorporate a prefetcher in the cache stack based on the observed immediacy feature that is unique to the QQPhoto workload. The prefetcher proactively prefetches selected photos into cache before they are requested for the first time to eliminate compulsory misses and promote hit ratios. Our extensive evaluation results show that with appropriate prefetching we improve the cache hit ratio by up to 7.4 percent, while reducing the average access latency by 6.9 percent at a marginal cost of 4.14 percent backend network traffic compared to the original system that performs no prefetching.

关键词： caching algorithm distributed storage photo storage cloud computing

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A block-level caching optimization method for mobile transparent computing

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PEER-TO-PEER NETWORKING AND APPLICATIONS 2018年第4期11卷 711-722页

作者： Tang, Yayuan Guo, Kehua Tian, Biao Cent South Univ Sch Informat Sci & Engn Changsha 410083 Hunan Peoples R China Hunan Univ Sci & Engn Sch Elect & Informat Engn Yongzhou 425199 Peoples R China Minjiang Univ Key Lab Informat Proc & Intelligent Control Fujia Fuzhou 350108 Fujian Peoples R China

In mobile transparent computing, a large number of concurrent data requests from heterogeneous clients via the network need to be processed in a timely fashion, and servers have to repeatedly fetch (search and read) the data from storage, which may cause numerous I/O costs. Generally, disk access speed is more limited than memory;therefore, massive I/O operations at servers may become a bottleneck for the system, and transport delay of the total network caused by the limitation of wireless bandwidth and stability may lead to poorer user experience. Hence, caching method plays a significant role in performance improvement of transparent computing systems. In this paper, we propose a block-level caching optimization method for the server and client by analyzing the system bottleneck in mobile transparent computing. We first analyze the storage format of the data file and the three-layer structure in the server according to the characteristics of requesting data from the client to the server and propose a block-level cache based on the access time and access frequency for the server. Second, considering the restriction of bandwidth and stability of the wireless network, we analyze network boot processes from the client's startup and propose a client block-level cache optimization combined with local storage access technology. Finally, experimental results demonstrate that the server block-level cache optimization can effectively reduce the amount of server disk I/O, improving the concurrent ability of the server. In addition, the client block-level cache can significantly increase startup speed of the client, reduce network traffic and improve user experience.

关键词： I/O request Block-level cache caching algorithm Mobile transparent computing

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Fast media caching for geo-distributed data centers

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COMPUTER COMMUNICATIONS 2018年第May期120卷 46-57页

作者： Zhang, Wei Wen, Yonggang Liu, Fang Chen, Yiqiang Fan, Rui Nanyang Technol Univ Sch Comp Sci & Engn 50 Nanyang Ave Singapore 639798 Singapore Chinese Acad Sci Inst Comp Technol Beijing 100190 Peoples R China Shanghai Tech Univ Sch Informat Sci & Technol Shanghai 200031 Peoples R China

Recent years have witnessed a phenomenal increase in video traffic. Virtual content delivery networks (vCDNs) coordinate video content delivery through the use of computing and storage resources from the cloud and distributes content to edge nodes near consumers to reduce network traffic and improve service experience. An important objective of vCDNs is operation cost minimization. Since cloud data centers are geo-distributed, content transfer costs vary significantly with different data centers, i.e., the cost is high for retrieval from distant data centers and lower for nearby retrievals. Many popular caching algorithms in use today, such as LRU, do not consider cost when making caching decisions, and as a result, suffer from high data transfer costs and increased network congestion. On the other hand, cost-aware caching algorithms such as LANDLORD [1] are computationally inefficient, with time complexity scaling linearly to the amount of content in the vCDN. Such algorithms are unable to keep pace with the exponential growth in video content over time. In this paper, we propose FMC (fast media caching), a cost-aware and highly efficient caching algorithm for vCDN delivery over geo-distributed data centers. The load cost of each content item is determined by both the item's size and distance from the data center it is loaded from. We first prove that FMC is k/k-h+1 competitive under the resource augmentation paradigm, where FMC and the optimal offline adversary have k and h amount of cache, resp., and k >= h. Also, we show our algorithm is straightforward and efficient, requiring only O(log m) time per cache access, where m is the number of data centers and is a small constant in practice. We conduct experimental studies on MC using both synthetic and YouTube traces. Our results show that INC has on average 50% and up to 66.7% lower cost than tau. Besides, we show PMC is much faster than IANDLORD, and the speedup scales linearly with cache size.

关键词： CDN caching algorithm Cloud computing Geo-distributed data centers

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On Resource Pooling and Separation for LRU caching

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PROCEEDINGS OF THE ACM ON MEASUREMENT AND ANALYSIS OF COMPUTING SYSTEMS 2018年第1期2卷 1–31页

作者： Tan, Jian Quan, Guocong Ji, Kaiyi Shroff, Ness Ohio State Univ 2015 Neil Ave Columbus OH 43210 USA

caching systems using the Least Recently Used (LRU) principle have now become ubiquitous. A fundamental question for these systems is whether the cache space should be pooled together or divided to serve multiple flows of data item requests in order to minimize the miss probabilities. In this paper, we show that there is no straight yes or no answer to this question, depending on complex combinations of critical factors, including, e.g., request rates, overlapped data items across different request flows, data item popularities and their sizes. To this end, we characterize the performance of multiple flows of data item requests under resource pooling and separation for LRU caching when the cache size is large. Analytically, we show that it is asymptotically optimal to jointly serve multiple flows if their data item sizes and popularity distributions are similar and their arrival rates do not differ significantly;the self-organizing property of LRU caching automatically optimizes the resource allocation among them asymptotically. Otherwise, separating these flows could be better, e.g., when data sizes vary significantly. We also quantify critical points beyond which resource pooling is better than separation for each of the flows when the overlapped data items exceed certain levels. Technically, for a broad class of heavy-tailed distributions we derive the asymptotic miss probabilities of multiple flows of requests with varying data item sizes in a shared LRU cache space. It also validates the characteristic time approximation under certain conditions. These results provide new insights on improving the performance of caching systems.

关键词： caching algorithm LRU Miss probability Memcached

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caching algorithms for broadcasting and multicasting in disruption tolerant networks

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WIRELESS COMMUNICATIONS & MOBILE COMPUTING 2016年第18期16卷 3377-3390页

作者： Tian, Feng Liu, Bo Zhou, Haibo Rui, Yun Chen, Jiacheng Xiong, Jian Gui, Lin Shanghai Jiao Tong Univ Dept Elect Engn Shanghai Peoples R China Deakin Univ Sch Informat Technol Melbourne Vic Australia Univ Waterloo Dept Elect & Comp Engn 200 Univ Ave West Waterloo ON N2L 3G1 Canada Chinese Acad Sci Shanghai Adv Res Inst Beijing Peoples R China

In delay and disruption tolerant networks, the contacts among nodes are intermittent. Because of the importance of data access, providing efficient data access is the ultimate aim of analyzing and exploiting disruption tolerant networks. caching is widely proved to be able to improve data access performance. In this paper, we consider caching schemes for broadcasting and multicasting to improve the performance of data access. First, we propose a caching algorithm for broadcasting, which selects the community central nodes as relays from both network structure perspective and social network perspective. Then, we accommodate the caching algorithm for multicasting by considering the data query pattern. Extensive trace-driven simulations are conducted to investigate the essential difference between the caching algorithms for broadcasting and multicasting and evaluate the performance of these algorithms. Copyright (c) 2016 John Wiley & Sons, Ltd.

关键词： delay and disruption tolerant networks caching algorithm broadcasting multicasting

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Eigenvalues of LRU via a linear algebraic approach

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OPERATIONS RESEARCH LETTERS 2018年第2期46卷 193-198页

作者： Tang, Dengwang Subramanian, Vijay Univ Michigan Ann Arbor MI 48109 USA

In this short paper we analyze the Markov Chain associated with Least Recently Used (LRU) cache replacement scheme. The eigenvalues and their multiplicities of this chain are determined using basic linear algebra alon... 详细信息

关键词： caching algorithm Markov chains Linear algebra

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On resource pooling and separation for LRU caching 18

On resource pooling and separation for LRU caching

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2018 ACM International Conference on Measurement and Modeling of Computer Systems, SIGMETRICS 2018

作者： Tan, Jian Quan, Guocong Ji, Kaiyi Shroff, Ness Ohio State University ColumbusOH United States

ISBN: (纸本)9781450358460

caching systems using the Least Recently Used (LRU) principle have now become ubiquitous. A fundamental question for these systems is whether the cache space should be pooled together or divided to serve multiple flows of data item requests in order to minimize the miss probabilities. In this paper, we show that there is no straight yes or no answer to this question, and depends on complex combinations of critical factors, including, e.g., request rates, overlapped data items across different request flows, data item popularities and their sizes. To this end, we characterize the performance of multiple flows of data item requests under resource pooling and separation when the cache size is large. Analytically we show that it is asymptotically optimal to jointly serve multiple flows if their data item sizes and popularity distributions are similar, and their arrival rates do not differ significantly;the self-organizing property of LRU caching automatically optimizes the resource allocation among them asymptotically. Otherwise, separating these flows could be better, e.g., when data sizes vary significantly. We also quantify critical points beyond which resource pooling is better than separation for each of the flows when the overlapped data items exceed certain levels. These results provide new insights on the performance of caching systems. © 2018 Copyright held by the owner/author(s).

关键词： miss probability memcached LRU caching algorithm

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A POMDP-based caching algorithm for RAM of Steaming Media Servers

A POMDP-based Caching Algorithm for RAM of Steaming Media Se...

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31st Chinese Control Conference

作者： Wu Xumin Yin Baoqun Zhang Guorong Zhang Haipeng Univ Sci & Technol China Key Lab Anhui Network Commun Syst & Control Hefei 230027 Peoples R China Chinese Acad Sci Inst Acoust Natl Network New Media Engn Res Ctr Beijing 100190 Peoples R China

ISBN: (纸本)9789881563811

Streaming media servers need to acquire data from local disk frequently. Because of the much lower reading/writing speed of disk than random access memory (RAM), good RAM caching policy can efficiently reduce disk I/O. The traditional technique is using a greedy and suboptimal algorithm based on the 0-1 knapsack problem model. In this paper we propose a partially observable Markov decision process (POMDP) model for the caching problem. Based on the model, the method of event-based optimization is applied in search of optimal randomized policy. The disk I/O, as the long-run average performance measure of a Markov chain, is optimized via applying the policy-gradient algorithm. The simulation results illustrate that the requiring data could be predicted better and lower disk I/O could be achieved by using the proposed method.

关键词： Event-based optimization POMDP caching algorithm

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Lazy caching: A Novel Proxy caching algorithm for Peer-to-Peer Live Streaming

Lazy Caching: A Novel Proxy Caching Algorithm for Peer-to-Pe...

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IEEE International Conference on Communications (ICC)

作者： Zhai, Haibin Wong, Albert K. Lu, Li Jiang, Hai Li, Jun Li, Zhongcheng Chinese Acad Sci Inst Comp Technol Beijing Peoples R China Hong Kong Univ Sci & Technol Hong Kong Hong Kong Peoples R China Grad Univ Chinese Acad Sci Beijing Peoples R China China Telecom Guangzhou Res Inst Guangzhou Peoples R China

ISBN: (纸本)9781457720536

Peer-to-Peer (P2P) live streaming systems are becoming popular and are imposing a heavy burden on Internet Services Providers (ISPs). Proxy caching has been shown to be an effective means of reducing operation costs for ISPs. Although many caching algorithms for conventional web applications and P2P file sharing systems have been proposed and deployed, there have been few works on the caching for P2P live streaming. Data requests in P2P live streaming have distinct characteristics;for example, they are concentrated in a limited window which progresses rapidly with time in a monotonic mode. In this paper, we propose a novel caching algorithm for P2P live streaming called Lazy caching (LC), which is designed based on the characteristics of P2P live streaming. In LC, data requests that encounter cache misses are stored in a Request Buffer (RB) that is processed periodically. LC can better utilize the cache space than traditional caching algorithms at the cost of introducing a response latency which is at most one RB processing period. Experimental results show that the hit ratio of LC, at a processing period of 0.5 second, can be as much as 20 percent higher than that of SLW, and 9 percent higher than that of OPT. We believe that it is worthwhile to achieve this higher hit ratio at the cost of a small response latency increase.

关键词： peer-to-peer live streaming proxy cache caching algorithm

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Fast Tucker Factorization for Large-Scale Tensor Completion 18

Fast Tucker Factorization for Large-Scale Tensor Completion

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18th IEEE International Conference on Data Mining Workshops (ICDMW)

作者： Lee, Dongha Lee, Jaehyung Yu, Hwanjo Pohang Univ Sci & Technol Pohang South Korea

ISBN: (纸本)9781538691595

Tensor completion is the task of completing multi-aspect data represented as a tensor by accurately predicting missing entries in the tensor. It is mainly solved by tensor factorization methods, and among them, Tucker factorization has attracted considerable interests due to its powerful ability to learn latent factors and even their interactions. Although several Tucker methods have been developed to reduce the memory and computational complexity, the state-of-the-art method still 1) generates redundant computations and 2) cannot factorize a large tensor that exceeds the size of memory. This paper proposes FTCOM, a fast and scalable Tucker factorization method for tensor completion. FTCOM performs element-wise updates for factor matrices based on coordinate descent, and adopts a novel caching algorithm which stores frequently-required intermediate data. It also uses a tensor file for disk-based data processing and loads only a small part of the tensor at a time into the memory. Experimental results show that FTCOM is much faster and more scalable compared to all other competitors. It significantly shortens the training time of Tucker factorization, especially on real-world tensors, and it can be executed on a billion-scale tensor which is bigger than the memory capacity within a single machine.

关键词： tensor completion Tucker factorization coordinate descent caching algorithm disk-based data processing

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