An Effective Model for Edge-Side Collaborative Storage in Data-Intensive Edge Computing

Li, Yunhao; Luo, Junzhou; Jin, Jiahui; Xiong, Runqun; Dong, Fang

doi:10.1109/cscwd.2018.8465306

Cited by 10 publications

(8 citation statements)

References 3 publications

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“…There were studies concentrated on the storage at the edge. Which includes collaborated with the cloud, forecasted for pre-store, self-adaptive according to specific situations, such as [7,10,12,13]. The study [10] proposed a single namespace and resource-aware federation file system called EDGESTORE for edge nodes, which can aggregate storage namespace and federate resources of edge nodes to enable high resource-sharing in the federation.…”

Section: Related Workmentioning

confidence: 99%

DECS: Collaborative Edge-Edge Data Storage Service for Edge Computing

Zhou

Chen

2021

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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With the development of IoT and 5G, data are generated by the numerous smart end devices at each moment. Simultaneously, as the improvement of the hardware's performance, computing and storage are partly transferred to the edge of the Internet. However, the core cloud and massive data centers are still responsible for management and coordination. In more and more local-area and small-scale scenarios such as a parking lot, an office building, or a college campus, these scenarios also need the edge nodes to offload computing and storage tasks. Moreover, in order to decrease costs and be lightweight, these scenarios need to decouple with the core cloud partly. In this paper, we proposed a collaborative edge-edge data storage service called DECS for edge computing in local-area scenarios. DECS can make the edge nodes collaborate with others. Such as trade-off to pick the most appropriate edge node to offload storage or computing tasks. DECS can also replicate data or generate forwarding rules in advance by predicting data's popularity proactively.In this paper, we evaluated DECS at two real scenarios compared with state-of-the-art research. The experiment results proved that DECS was more suitable for the local-area edge cluster. Which lowered the access latency, saved the total bandwidth, and improved the resource utilization of the whole edge cluster.

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Section: Related Workmentioning

confidence: 99%

DECS: Collaborative Edge-Edge Data Storage Service for Edge Computing

Zhou

Chen

2021

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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“…This paper's conference version has shown the basic idea of the vertex‐centric iterative refinement approach. This paper extends the conference version in the following aspects.…”

Section: Related Workmentioning

confidence: 99%

“…This work was supported by the National Key R&D Program of China 2017YFB1003000; the National Natural Science Foundation of China under grants 61702096, 61632008, 61320106007, 61572129, 61602112, 61502097, and 61702097; the International S&T Cooperation Program of China under grant 2015DFA10490; the Natural Science Foundation of Jiangsu Province under grants BK20170689 and BK20160695; Jiangsu Provincial Key Laboratory of Network and Information Security under grant BM2003201; the Key Laboratory of Computer Network and Information Integration of Ministry of Education of China under grant 93K‐9; and partially supported by the Collaborative Innovation Center of Novel Software Technology and Industrialization and Collaborative Innovation Center of Wireless Communications Technology. A preliminary version appeared in The Proceedings of 2018 IEEE 22nd International Conference on Computer Supported Cooperative Work in Design (CSCWD 2018) as a regular research paper.…”

Section: Acknowledgmentsmentioning

confidence: 99%

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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“…Using many-core systems for each edge server can be costly in a smaller IoT system. An edge-side company storage framework was also proposed to store IoT data [10]. Edge servers also usually have limited resources and small amounts of storage.…”

Section: Introductionmentioning

confidence: 99%

“…When an edge server lacks a storage space, the edge-side company storage framework shares the storage of other near-edge servers to avoid long delays when offloading data to a cloud server. While the aforementioned study [10] concerns the efficient management of the storage space, this paper focuses on the data filtering issue.…”

Section: Introductionmentioning

confidence: 99%

Management of IoT Sensor Data Using a Fog Computing Node

Yoon¹,

Choi

Lee

et al. 2019

Journal of Sensors

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As IoT systems spread, transmissions of all data from various sensing devices to a remote OM (Operation and Management) server through the Internet can lead to many problems, such as an explosion of network traffic and delayed responses to data. Fog computing is a good means of resolving these problems in an IoT system environment. In this paper, a management method for sensor data in a fog computing node is proposed. The monitoring node monitors data from sensor devices using a data pattern from the OM server, which dynamically generates and updates the pattern. The monitoring node reports only the data beyond the normal range of the pattern to the OM server rather than sending all data to the OM server. The monitoring node can control the operations of sensor devices remotely according to the requests of the OM server.

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An Effective Model for Edge-Side Collaborative Storage in Data-Intensive Edge Computing

Cited by 10 publications

References 3 publications

DECS: Collaborative Edge-Edge Data Storage Service for Edge Computing

DECS: Collaborative Edge-Edge Data Storage Service for Edge Computing

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

Management of IoT Sensor Data Using a Fog Computing Node

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