Multi-view Learning Based Edge Storage Management Strategy

Gao, Quanli; Gao, Ling; Xue, Tao; Zhu, Xiaoyan; Zhao, Xueqing; Cao, Rui

doi:10.1109/cbd.2018.00072

Cited by 4 publications

(5 citation statements)

References 9 publications

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“…In practical scenarios, there are obstacles in vehicular environments, such as buildings, trees and hills, which will affect the success of data transmission [75].…”

Section: ) Harsh Channel Environmentmentioning

confidence: 99%

“…SCeNB are a recent technology that has been deployed closer to mobile users in some scenarios to offer improved services at low cost, decreased energy and high data rates [75]. Monetary incentives have been exploited to the SCeNBs in this approach if they perform the computing process for the UEs, which are attached to other SCeNBs.…”

Section: Challenges and Future Workmentioning

confidence: 99%

“…Thus, a very challenging problem is taking into account the difference in communication latency, the availability of resources in the target MEC server, the computational load and the performance and overhead cost of relocation with the optimization of the QoS, and the heterogeneity of applications can exacerbate the challenge. ML has been considered as a helpful technology that can be applied to the management of the MEC vehicular network to solve the low efficiency of traditional optimization methods and relocation problems [50], and various machine learning methods, including reinforcement learning (RL), deep Qnetworks model [73], and traditional ML approaches [75], have been introduced to optimize the system performance.…”

Section: Challenges and Future Workmentioning

confidence: 99%

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A Survey of Multi-Access Edge Computing and Vehicular Networking

Hou

Gregory

2022

IEEE Access

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With the introduction of 5G and the Internet of Things, Multi-access Edge Computing (MEC) has become an evolving distributed compute and storage capability at the network edge. MEC will support task offloading, mobility, resource allocation, and management of inter-server communications to improve quality of service and satisfy real-time applications that require low latency. Thus, MEC is regarded as a crucial capability that will provide computation and storage at the network edge as an extension of the more traditional cloud. By placing MEC at the network edge, a broad range of new applications and services can be offered in close proximity to users, including support for vehicular networks. This paper provides a current and comprehensive review of MEC-enabled vehicular networks. It first introduces MEC by providing a definition, architecture, applications, and challenges. The paper then investigates MEC support for vehicular network applications and services and identifies current research and future challenges.

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“…In practical scenarios, there are obstacles in vehicular environments, such as buildings, trees and hills, which will affect the success of data transmission [75].…”

Section: ) Harsh Channel Environmentmentioning

confidence: 99%

Section: Challenges and Future Workmentioning

confidence: 99%

Section: Challenges and Future Workmentioning

confidence: 99%

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A Survey of Multi-Access Edge Computing and Vehicular Networking

Hou

Gregory

2022

IEEE Access

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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%

“…The study [12] proposed an effective model for edge-side collaborative storage in data-intensive edge computing, which can forward the offload requests to the with-data edge nodes. The study [7] proposed an edge storage method based on multi-view learning, which predicted the resources that need to be pre-stored based on multi-feature linear discriminant analysis, including their real-time performance and user's location. The study [13] presented a three-layer architecture model for data storage management on edge, including an adaptive algorithm that dynamically finds a trade-off between providing high forecast accuracy necessary for efficient realtime decisions.…”

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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