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检索条件"主题词=distributed caching"

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Optimal caching for Device-to-Device Content Distribution in 5G Networks

Optimal Caching for Device-to-Device Content Distribution in...

引用

IEEE Global Communications Conference (GLOBECOM)

作者： Malak, Derya Al-Shalash, Mazin Univ Texas Austin WNCG Austin TX 78712 USA Huawei Technol Shenzhen Guangdong Peoples R China

ISBN: (纸本)9781479974702

Content distribution using direct device-to-device D2D communication is a promising approach for optimizing the utilization of air-interface resources in 5G network. Research in this area has indicated, perhaps unexpectedly, that the optimal caching distribution for D2D content does not match the distribution of user demands/requests. Therefore, in order to achieve the best utilization of network resources, the network should bias the distribution of cached content. The optimal caching distribution is dependent on, but not identical to, the content demand distribution. In this paper we model transmitting and receiving devices as member of a homogeneous Poisson Point Process (PPP), and use results from stochastic geometry to derive the probability of successful content delivery in the presence of D2D interference and noise. We formulate an optimization problem, and find the best caching distribution that maximizes the total probability of content delivery, assuming user demands/requests are modeled by a Zipf distribution with exponent gamma(r). Our results show that the optimal caching distribution follows a similar trend as the demand distribution, and can also be modeled as a Zipf distribution. The exponent gamma(c) of the optimal caching distribution is related to gamma(r) through a simple expression involving the path loss exponent.

关键词： distributed caching D2D communications device-to-device content distribution

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Performance Analysis of On-board Content caching and Retrieval for High-Speed Railways 2

Performance Analysis of On-board Content Caching and Retriev...

引用

IEEE 2nd 5G World Forum (5GWF)

作者： Wu, Yu Fang, Xuming Yan, Li Southwest Jiaotong Univ Sch Informat Sci & Technol Chengdu Peoples R China

ISBN: (纸本)9781728136271

With the rapid development of high-speed railway (HSR), more and more people tend to choose High-Speed Train (HST) for the benefit of its high efficiency and safety. During long-distance journeys, passengers expect to have real-time and high-quality multi-media services for working and entertainment. However, direct cellular connections between on-board User Equipments (UEs) and wayside Base Stations (BSs) are hard to provide continuous and assured Quality of Service (QoS) due to the high mobility of HST and high penetration loss of carriages. The multi-hop network architecture and in-network caching enabled by information-centric networks (ICN) are regarded as efficient methods to solve above problems. Based on the above observations, to improve quality of experience (QoE) for passengers and content dissemination in HST, we propose an on-board content caching and retrieval scheme for a multi-hop HST WiFi network architecture, in which multi-media contents are optionally cached in HSTs through a distributed or centralized method. Simulation results suggest that the caching policy is necessary, and the centralized caching policy outperforms the distributed one in terms of cache hit ratio.

关键词： High-Speed Train (HST) Content caching scheme distributed caching Centralized caching Cache hit ratio

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Large Scale caching and Streaming of Training Data for Online Deep Learning 12

Large Scale Caching and Streaming of Training Data for Onlin...

引用

12th Workshop on AI and Scientific Computing at Scale using Flexible Computing Infrastructures (FlexScience)

作者： Liu, Jie Nicolae, Bogdan Li, Dong Wozniak, Justin M. Bicer, Tekin Liu, Zhengchun Foster, Ian Univ Calif Merced Merced CA 95343 USA Argonne Natl Lab Chicago IL USA

ISBN: (纸本)9781450393096

The training of deep neural network models on large data remains a difficult problem, despite progress towards scalable techniques. In particular, there is a mismatch between the random but predetermined order in which AI flows select training samples and the streaming I/O patterns for which traditional HPC data storage (e.g., parallel file systems) are designed. In addition, as more data are obtained, it is feasible neither simply to train learning models incrementally, due to catastrophic forgetting (i.e., bias towards new samples), nor to train frequently from scratch, due to prohibitive time and/or resource constraints. In this paper, we study data management techniques that combine caching and streaming with rehearsal support in order to enable efficient access to training samples in both offline training and continual learning. We revisit state-of-art streaming approaches based on data pipelines that transparently handle prefetching, caching, shuffling, and data augmentation, and discuss the challenges and opportunities that arise when combining these methods with data-parallel training techniques. We also report on preliminary experiments that evaluate the I/O overheads involved in accessing the training samples from a parallel file system (PFS) under several concurrency scenarios, highlighting the impact of the PFS on the design of the data pipelines.

关键词： deep learning distributed caching data pipelines reuse or training data

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Cooperative caching for peer-assisted video distribution

引用

13th International Multimedia Modeling Conference (MMM 2007)

作者： Guo, Pan-Hong Yang, Yang Guo, Hui Beijing Univ Sci & Technol Dept Comp Sci & Technol Beijing 100083 Peoples R China The Hong Kong Polytech Univ Elect & Informat Engn Dept Kowloon Hong Kong Peoples R China

ISBN: (纸本)9783540694281

In this paper, we described a framework for peer-assisted multi-path video distribution combined with cooperative caching. The target of this framework is to aggregate peers' storage and bandwidths to facilitate video-on-demand streaming. To achieve this goal, we employ segment-based video caching and the segments are distributed in respective peers. Specifically, the source bit stream is based on layered scalable video coding for cost-effective video distribution. For achieving low-cost collaboration, a utility-based partial caching scheme is proposed and detailed discussed. Extensive simulations on large, Internet-like topologies were performed to demonstrate the effectiveness of this proposed framework.

关键词： cooperative caching peer-to-peer networks segment-base caching distributed caching

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A Study on User Mobility in Device to Device (D2D) Networks through Distrubted Catching

A Study on User Mobility in Device to Device (D2D) Networks ...

引用

IEEE International Conference on Power, Control, Signals and Instrumentation Engineering (IEEE ICPCSI)

作者： SubbaRayudu, Yerragudipadu Reddy, R. Obulakonda Anjaiah, P. Inst Aeronaut Engn Dept CSE Hyderabad India

ISBN: (纸本)9781538608142

D2D communication in cellular networks is defined as direct communication between two mobile users without traversing the Base Station (BS) or core network. D2D communication is generally non-transparent to the cellular network and it can occur on the cellular frequencies (i.e., inband) or unlicensed spectrum (i.e., outband).The main aim of this paper is To consider a distributedcaching (Device-to-Device) D2Dnetwork. in which an Owner's file of interest is cached as several parts in the storage of other devices in the network. Here The Owner think that he needs to achieve all these file parts, the portions cached farther away naturally become the performance bottle-neck. This is due to the trust that Main interferers may be near to the receiver than the serving device. Using a simple stochastic geometry model, we concretely demonstrate that this bottleneck can be loosened if the Owners are mobile. Gains obtained from mobility (the ability to move or be moved freely and easily)are quantified in terms of coverage probability.

关键词： distributed caching D2Dnetwork Cellular networks mobility stochastic geometry Poisson point process coverage probability

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Hierarchical Matching with Peer Effect for Latency-Aware caching in Social IoT

Hierarchical Matching with Peer Effect for Latency-Aware Cac...

引用

2nd IEEE International Conference on Smart Internet of Things (SmartIoT)

作者： Wang, Bowen Sun, Yanjing Li, Song Cao, Qi Chen, Yan Xu, Jie China Univ Min & Technol Sch Informat & Control Engn Beijing Peoples R China

ISBN: (纸本)9781538685426

The Internet of Things (IoT) promises to be a source of great benefits to users, operators and manufactures in different application scenarios. To allow smart objects to have their own social networks and share contents via device-to device (D2D) communications in the IoT, the combined social IoT can potentially provide information and services more efficiently. This paper focuses on the latency optimization in the social IoT with D2D based caching. To achieve the optimization object, we formulate the combinatorial problem as a hierarchical matching game, which can be decoupled into a content sharing problem and a resource allocation problem. For solving the content sharing problem, a one-to-one stable matching algorithm is proposed by considering both social and physical characteristics. As for the resource allocation, we model this problem as a many-to-one matching with peer effect and propose a distributed algorithm to achieve a two-sided exchange-stable matching. In this way, the combinatorial problem can be solved by the proposed hierarchical stable matching algorithm. Simulation results show that our proposed algorithms are stable, converging and are capable of offering a better performance than other benchmarks.

关键词： Device-to-device communications social internet of things distributed caching hierarchical stable matching

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EclipseMR: distributed and Parallel Task Processing with Consistent Hashing

EclipseMR: Distributed and Parallel Task Processing with Con...

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IEEE International Conference on Cluster Computing (CLUSTER)

作者： Sanchez, Vicente A. B. Kim, Wonbae Eom, Youngmoon Jin, Kibeom Nam, Moohyeon Hwang, Deukyeon Kim, Jik-Soo Nam, Beomseok UNIST Ulsan South Korea Korea Mil Acad Seoul South Korea Myongji Univ Seoul South Korea

ISBN: (纸本)9781538623268

We present EclipseMR, a novel MapReduce framework prototype that efficiently utilizes a large distributed memory in cluster environments. EclipseMR consists of double-layered consistent hash rings - a decentralized DHT-based file system and an in-memory key-value store that employs consistent hashing. The in-memory key-value store in EclipseMR is designed not only to cache local data but also remote data as well so that globally popular data can be distributed across cluster servers and found by consistent hashing. In order to leverage large distributed memories and increase the cache hit ratio, we propose a locality-aware fair (LAF) job scheduler that works as the load balancer for the distributed in-memory caches. Based on hash keys, the LAF job scheduler predicts which servers have reusable data, and assigns tasks to the servers so that they can be reused. The LAF job scheduler makes its best efforts to strike a balance between data locality and load balance, which often conflict with each other. We evaluate EclipseMR by quantifying the performance effect of each component using several representative MapReduce applications and show EclipseMR is faster than Hadoop and Spark by a large margin for various applications.

关键词： MapReduce distributed caching Consistent Hashing distributed Hash Table

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STASH : Fast Hierarchical Aggregation Queries for Effective Visual Spatiotemporal Explorations

STASH : Fast Hierarchical Aggregation Queries for Effective ...

引用

IEEE International Conference on Cluster Computing (IEEE CLUSTER)

作者： Mitra, Saptashwa Khandelwal, Paahuni Pallickara, Shrideep Pallickara, Sangmi Lee Colorado State Univ Dept Comp Sci Ft Collins CO 80523 USA

ISBN: (纸本)9781728147345

The proliferation of sensors and observational instruments enable scientists to explore natural, spatiotemporal phenomena via explorative analysis and advanced modeling. Geospatial visualization, in particular, is an intuitive tool to identify patterns, enhance understanding of the data, and plan for subsequent analysis. However, seamless interactions between end-user devices and the sheer volume of data have been a challenge due to the limited bandwidth and data access latencies. In this paper, we introduce STASH, a distributed, in-memory cache for hierarchical aggregation and query evaluations. STASH is a middleware which can be loaded on top of a distributed file system. Users perform queries from a lightweight visualization interface at the front-end and the evaluations occur over the back-end storage system housing the raw data over which summarization and subsequent visualizations are to be performed. STASH facilitates fast exploratory analytics by caching relevant past query results based on their frequency and freshness to assist similar, future queries and avoid expensive disk I/O and network usage, thus reducing their latency. Additionally, STASH handles any hotspot that might result from a spike in user requests due to the spatial and temporal locality of their access patterns. Our empirical benchmarks show that a STASH-enabled system reduces query latency of a basic system by over 5-folds and brings it down to interactive speed even for large country-sized spatiotemporal queries. We have contrasted STASH with existing cache-enabled analytics engines, such as ElasticSearch, and found that our STASH-enabled system reduced the aggregation query latency up to similar to 70%. STASH also alleviated skewed workloads through its dynamic replication scheme and improved throughput by similar to 40% in hotspot scenarios.

关键词： in-memory storage distributed caching aggregation query visual analytics exploratory analytics

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Device-to-Device Content Distribution: Optimal caching Strategies and Performance Bounds

Device-to-Device Content Distribution: Optimal Caching Strat...

引用

IEEE International Conference on Communications (ICC)

作者： Malak, Derya Al-Shalash, Mazin Univ Texas Austin WNCG Austin TX 78712 USA Huawei Technol Hyderabad Telangana India

ISBN: (纸本)9781467363051

Content distribution using direct device-to-device (D2D) communication is a promising approach for optimizing the utilization of air-interface resources in 5G network. To achieve the best utilization of network resources, the network should bias the distribution of cached content. The optimal caching distribution depends on the content demand distribution. In this paper, we model the locations of transmitting and receiving devices by a homogeneous Poisson Point Process (PPP), and use results from stochastic geometry to derive the probability of successful content delivery in the presence of D2D interference and noise. We formulate an optimization problem to maximize the total probability of content delivery, assuming user demands are modeled by a Zipf distribution. We also develop strategies to optimize content caching, and bound the total coverage probability for multiple file caching scenarios.

关键词： distributed caching device-to-device communications content distribution Poisson Point Process coverage

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IEEE/CIC International Conference on Communication in China (ICCC)

作者： Long, Yanshan Cai, Yueming Wu, Dan Xu, Xiaoming PLA Univ Sci & Technol Coll Commun Engn Nanjing Jiangsu Peoples R China

ISBN: (纸本)9781509021437

This paper focuses on the performance study of distributed cache-enabled Device-to-Device (D2D) content delivery network A novel content-related interference model is proposed to describe the concurrent interferers in the network Accordingly, the exact content-related coverage probability is derived to characterize the probability of a requesting user being served successfully. Moreover, two special cases with familiar cache deployments are studied to further show the generalization of our model and analysis. Finally, numeral simulation results confirm the validity of the theoretical conclusions and show that the model we proposed is more general than the existing ones.

关键词： D2D content delivery network distributed caching stochastic geometry interference model content-related coverage probability

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