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

Huang, Yaodong; Song, Xintong; Ye, Fan; Yang, Yuanyuan; Li, Xiaoming

doi:10.1109/icdcs.2017.151

Cited by 48 publications

(14 citation statements)

References 20 publications

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“…Specifically, minimizing the delay due to advent of layered-video encoding techniques such as the scalable video coding (SVC) is NP-hard, so the authors transformed the caching problem and derived pseudopolynomial-time optimal caching solution. Moreover, several video caching schemes in vehicular networks and mobile edge networks are proposed [24] under various scenarios and caching optimizations with classical approaches in information centric network (ICN) were proposed in [25] and [26].…”

Section: A Optimization For Cachingmentioning

confidence: 99%

Self-adaptive power control with deep reinforcement learning for millimeter-wave Internet-of-vehicles video caching

Kwon¹,

Kim

Mohaisen

et al. 2020

J. Commun. Netw.

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Video delivery and caching over the millimeter-wave (mmWave) spectrum is a promising technology for high data rate and efficient frequency utilization in many applications, including distributed vehicular networks. However, due to the short handoff duration, calibrating both optimal power allocation of each base station toward its associated vehicles and cache allocation are challenging for their computational complexity. Heretofore, most video delivery applications were based on on-line or off-line algorithms, and they were limited to compute and optimize high dimensional objectives within low-delay in large scale vehicular networks. On the other hand, deep reinforcement learning is shown for learning such scale of a problem with an optimized policy learning phase. In this paper, we propose deep deterministic policy gradient-based power control of mmWave base station (mBS) and proactive cache allocation toward mBSs in distributed mmWave Internet-of-vehicle (IoV) networks. Simulation results validate the performance of the proposed caching scheme in terms of quality of the provisioned video and playback stall in various scales of IoV networks.

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Section: A Optimization For Cachingmentioning

confidence: 99%

Self-adaptive power control with deep reinforcement learning for millimeter-wave Internet-of-vehicles video caching

Kwon¹,

Kim

Mohaisen

et al. 2020

J. Commun. Netw.

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“…[12][13][14][15] For content delivery networks (CDN), cooperative storage is used to reduce the number of duplicated files. 16 For mobile computing, cooperative storage allows storage-limited terminals to read data from nearby terminals by exploiting device-device communication 13,17 to reduce the amount of cellular traffic. 18 For edge computing, edge-side cooperative storage techniques are used to cache data, thereby reducing network traffic between terminals and Internet datacenters.…”

Section: Terminal Layermentioning

confidence: 99%

“…Terminal-side cooperative storage techniques let terminals share data with nearby terminals by exploiting device-to-device communication. 13,17,[33][34][35] Because of users' mobility, it is challenging to efficiently place data on mobile terminals. Thus, Wang et al 13 exploited the social impact among users in social network services (SNSs) to predict users' mobility patterns, and then used the pattern to guide content delivery to satisfy all users' delay requirements.…”

Section: Related Workmentioning

confidence: 99%

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Cooperative storage by exploiting graph‐based data placement algorithm for edge computing environment

Jin

Luo

2018

Concurrency and Computation

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Edge computing is a new computing paradigm that performs data processing at the edge of the network (ie, edge servers) to lower data processing latency. Prior research significantly focused on offloading tasks from terminals to edge servers, yet most ignored how to store task's necessary data (such as databases and pretrained machine-learning models) on edge servers. Today, as data-intensive tasks such as deep learning and augmented reality become common, large data storage and powerful computation resources are needed. This is a cumbersome challenge, because many lightweight edge servers have limited resources. If an edge server does not have a task's necessary data, then it needs to offload the task to cloud datacenters or download the necessary data from the cloud. Either case could increase data processing latency. To address this problem, this paper proposes an edge-side collaborative storage framework called Edge-side Cooperative Storage (ECS). In ECS, edge servers collaboratively store and process data-intensive tasks's necessary data. Here, we particularly focus on how to effectively place data on ECS, using an approach that differs from existing works (that model data placement problems as linear/integer programming problems). Our work models cooperative storage as a graph and solves the data placement problem by using a graph-based iterative algorithm. This algorithm easily extends to a distributed version, so distributed ECSworks efficiently without a centralized scheduler. We also evaluate ECS's effectiveness and convergence through simulations. Simulation results show that ECSis 2× better than a traditional nonshared storage framework in terms of the cache hit rate. KEYWORDScooperative storage, data placement, edge computing INTRODUCTIONEdge computing 1,2 is a new computing paradigm that performs data processing at the network's edge (ie, edge servers). In an edge computing system, terminals with limited computation and storage resources can offload tasks to edge servers to improve terminals' performance. As Figure 1 shows, a typical edge computing system consists of three layers, ie, the terminal layer, edge server layer, and cloud datacenter layer. Terminals, such as Raspberry Pi, mobile phones, and Google Glass, are small end devices that have limited computation, storage, and energy resources. 3 Recently, applications running on terminals, such as object detection, 4 indoor localization, 5,6 and encrypting, 7,8 are increasingly data-intensive and latency-critical.For example, Google Glass needs to classify the objects in pictures it takes, and augmented reality games need indoor localization information to render real-life environments on mobile phones. Because the terminals' computation and storage resources cannot satisfy these applications' requirements, often, computation tasks are offloaded to nearby edge servers. Edge servers are small computing facilities, such as smart network routers, base stations, and small datacenters, which are located near terminal devices. Edge servers connect via ...

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“…In this paper, peer-to-peer data sharing (PDS), is proposed to enable edge devices to find out what data exists in nearby peer nodes and to robustly and effectively retrieve data of interest. The problems solved in the literature [15,16] are all in the pervasive edge computing environment, and this paper will also be based on the research in this environment. In the pervasive edge computing environment, all mobile devices (including smartphones, tablets, laptops and other mobile devices) are edge nodes that can provide computing resources.…”

Section: Introductionmentioning

confidence: 99%

DPODA: Differential Privacy-based Online Double Auction for Pervasive Edge Computing Resource Allocation

Sun

et al. 2020

Proceedings of the 2nd ACM International Symposium on Blockchain and Secure Critical Infrastructure

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In recent years, edge computing transforms the network edge into an intelligent platform to provide better services for users by making storage, computing, control and network function closer to end-users, things, and sensors. However, due to the limitation of edge resources and the diversity of edge resource providers, there is no good mechanism to solve the resource allocation problem in the edge computing resource allocation market. Aiming at the problem of resource allocation in edge computing, this paper proposes a double auction scheme-DPODA. This scheme establishes the auction market of edge computing resources, which can improve the social welfare of auction to the maximum extent. Moreover, as the private information of users in the auction market may affect market transactions, in order to solve this problem, this paper uses differential privacy technology to effectively protect the privacy of both sides of the auction, so as to avoid the unfairness in the auction market. A large number of simulation experiments show that the algorithm proposed in this paper not only achieves the research purpose but also ensures the privacy information security of the auctioneer. CCS CONCEPTS • Security and privacy Human and societal aspects of security and privacy; • Networks Cloud computing; • Humancentered computing Ubiquitous and mobile computing; • Theory of computatio Online algorithms;

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

Cited by 48 publications

References 20 publications

Self-adaptive power control with deep reinforcement learning for millimeter-wave Internet-of-vehicles video caching

Self-adaptive power control with deep reinforcement learning for millimeter-wave Internet-of-vehicles video caching

Cooperative storage by exploiting graph‐based data placement algorithm for edge computing environment

DPODA: Differential Privacy-based Online Double Auction for Pervasive Edge Computing Resource Allocation

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