Networking for Big Data: A Survey

Yu, Shui; Meng, Lingyun; Dou, Wanchun; Liu, Xiting; Zhou, Sanming

doi:10.1109/comst.2016.2610963

Cited by 191 publications

(58 citation statements)

References 147 publications

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“…Such cyber configurations can pose serious complications with respect to privacy, security and integrity of both physical as well as ICT subsystems. Moreover, the multiplicities of Big Data applications in contemporary CPSs motivates to solicit the concept of Big Data networking, its formation, features, mathematical and statistical intricalities [30].…”

Section: Big-data To Knowledge (B2k) Frameworkmentioning

confidence: 99%

Federated Cloud Analytics Frameworks in Next Generation Transport Oriented Smart Cities (TOSCs) - Applications, Challenges and Future Directions

Hussain¹,

Alam²,

Beg³

2018

EAI Endorsed Transactions on Smart Cities

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Electric, plug-in electric and plug-in hybrid electric vehicles (xEVs) are receiving a global attention from automotive industries, vehicle vendors, R&D organizations, power sectors and policymakers in the intelligent transportation era. Penetration of xEV fleet into the contemporary charging infrastructure(s) in the absence of robust integration network imbalances the power grid and potentially jeopardize the execution of emerging distributed generation systems. However, smart grid technologies in collaboration with smart charging management strategies can circumvent such operational disparities, thus enabling a reliable, efficient, consistent and optimal electric energy management in the power system. This work employs the notion of Cloud of Things (CoT) to propose a comprehensive cloud aware Transport Oriented Smart City (TOSC) framework intended to provide intelligent solutions to the contemporary transportation infrastructures in the emerging sustainable smart cities. The proposed work also demonstrates a commercially viable vehicle to cloud (V2C) fleet charging framework for charging management of xEVs through micro grids/ smart grid. The unprecedented data breeding across V2C, cloud to grid (C2G) and grid to vehicle (G2V) bidirectional communication interfaces elucidates the need for computationally efficient analytics. A state-of-the-art Big-Data to Knowledge (B2K) workflow structure is thus proposed for translating the generated data into efficient knowledge for noble decisions and inferences. Finally, the substantial Mobility as a service (MaaS) adoption challenges and data science prospects are outlined along with the emerging technologies that can co-work with the proposed framework to ensure commercial viability and optimal implementation in emerging TOSCs.

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Section: Big-data To Knowledge (B2k) Frameworkmentioning

confidence: 99%

Federated Cloud Analytics Frameworks in Next Generation Transport Oriented Smart Cities (TOSCs) - Applications, Challenges and Future Directions

Hussain¹,

Alam²,

Beg³

2018

EAI Endorsed Transactions on Smart Cities

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“…The relationship between Big Data and network performance is generally explored to understand what is the best way to have networks that support Big Data applications …”

Section: State Of the Artmentioning

confidence: 99%

“…The relationship between Big Data and network performance is generally explored to understand what is the best way to have networks that support Big Data applications. 5,6 Few are the examples of works that investigate how Big Data techniques can be used to perform network monitoring or RCA, directly targeted to the improvement of the network infrastructure. Among them Cui et al and Trevisan et al 7,8 explore how Big Data can be used to perform traffic engineering and classify specific kinds of traffic when using Software-defined networking (SDN).…”

Section: State Of the Artmentioning

confidence: 99%

A Big Data and machine learning approach for network monitoring and security

Maccari

Passerini

2018

Security and Privacy

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In the last decade the performances of 802.11 (Wi‐Fi) devices skyrocketed. Today it is possible to realize gigabit wireless links spanning across kilometers at a fraction of the cost of the wired equivalent. In the same period, mesh network evolved from being experimental tools confined into university labs, to systems running in several real world scenarios. Mesh networks can now provide city‐wide coverage and can compete on the market of Internet access. Yet, being wireless distributed networks, mesh networks are still hard to maintain and monitor. This paper explains how today we can perform monitoring, anomaly detection and root cause analysis in mesh networks using Big Data techniques. It first describes the architecture of a modern mesh network, it justifies the use of Big Data techniques and provides a design for the storage and analysis of Big Data produced by a large‐scale mesh network. While proposing a generic infrastructure, we focus on its application in the security domain.

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“…If a device intends to get the wireless service, an AP (Access Point) and the corresponding radio frequency as well as the time slots are then all chosen by the device. Next, a routing path [49] from the chosen AP to target service is constructed. Thus the access request demands not only the AP and radio resource, but also network functions, routers, switches and corresponding bandwidth in backhaul and fronthaul.…”

Section: The Framework Of Smart Networkmentioning

confidence: 99%

Wireless big data: transforming heterogeneous networks to smart networks

Huang

Tan

Liang

2017

J. Commun. Inf. Netw.

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In HetNets (Heterogeneous Networks), each network is allocated with fixed spectrum resource and provides service to its assigned users using specific RAT (Radio Access Technology). Due to the high dynamics of load distribution among different networks, simply optimizing the performance of individual network can hardly meet the demands from the dramatically increasing access devices, the consequent upsurge of data traffic, and dynamic user QoE (Quality-of-Experience). The deployment of smart networks, which are supported by SRA (Smart Resource Allocation) among different networks and CUA (Cognitive User Access) among different users, is deemed a promising solution to these challenges. In this paper, we propose a framework to transform HetNets to smart networks by leveraging WBD (Wireless Big Data), CR (Cognitive Radio) and NFV (Network Function Virtualization) techniques. CR and NFV support resource slicing in spectrum, physical layers, and network layers, while WBD is used to design intelligent mechanisms for resource mapping and traffic prediction through powerful AI (Artificial Intelligence) methods. We analyze the characteristics of WBD and review possible AI methods to be utilized in smart networks. In particular, the potential of WBD is revealed through high level view on SRA, which intelligently maps radio and network resources to each network for meeting the dynamic traffic demand, as well as CUA, which allows mobile users to access the best available network with manageable cost, yet achieving target QoS (Quality-of-Service) or QoE.

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Networking for Big Data: A Survey

Cited by 191 publications

References 147 publications

Federated Cloud Analytics Frameworks in Next Generation Transport Oriented Smart Cities (TOSCs) - Applications, Challenges and Future Directions

Federated Cloud Analytics Frameworks in Next Generation Transport Oriented Smart Cities (TOSCs) - Applications, Challenges and Future Directions

A Big Data and machine learning approach for network monitoring and security

Wireless big data: transforming heterogeneous networks to smart networks

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