Fog computing: Difference between revisions
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== Motivation == |
== Motivation == |
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There are many factors that make this an interesting paradigm to study. |
There are many factors that make this an interesting paradigm to study. |
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Each client/edge device in the past several years as become powerful in its capabilities. For instance, the original iPhone had a single core 412 MHz ARM processor with 128 MB RAM and 8 GB storage space. The current iPhone 5S on the other hand carries a dual-core 1.3 GHz Apple A7 processor with 1GB RAM, 64 GB storage space and enhanced GPU capabilities. Intel’s mobile chip Atom and Nvidia’s Tegra too promise near similar specifications. The increase in strength and capabilities implies complex functionality such as CPU/GPU intensive gaming, powerful location/context tracking sensors and enhanced storage |
Each client/edge device in the past several years as become powerful in its capabilities. For instance, the original iPhone had a single core 412 MHz ARM processor with 128 MB RAM and 8 GB storage space. The current iPhone 5S on the other hand carries a dual-core 1.3 GHz Apple A7 processor with 1GB RAM, 64 GB storage space and enhanced GPU capabilities. Intel’s mobile chip Atom and Nvidia’s Tegra too promise near similar specifications. The increase in strength and capabilities implies complex functionality such as CPU/GPU intensive gaming, powerful location/context tracking sensors and enhanced storage. |
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Fog computing can be much more cognitive of end user application experience since it is much “closer” to the data then traditional cloud computing. Due to the rise of encrypted traffic and multipath-TCP in the core network, the network provider and the cloud service provider are often unable to extract and understand features specific to the users. Fog applications are not as limited since they may often reside on or near the end user, granting it access to raw user data. |
Fog computing can be much more cognitive of end user application experience since it is much “closer” to the data then traditional cloud computing. Due to the rise of encrypted traffic and multipath-TCP in the core network, the network provider and the cloud service provider are often unable to extract and understand features specific to the users. Fog applications are not as limited since they may often reside on or near the end user, granting it access to raw user data. |
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Revision as of 21:26, 22 June 2016
Fog computing[1] or fog networking, also known as fogging,[2][3] is an architecture that uses one or a collaborative multitude of end-user clients or near-user edge devices to carry out a substantial amount of storage (rather than stored primarily in cloud data centers), communication (rather than routed over the internet backbone), and control, configuration, measurement and management (rather than controlled primarily by network gateways such as those in the LTE core network).
Fog computing can be perceived both in large cloud systems and big data structures, making reference to the growing difficulties in accessing information objectively. This results in a lack of quality of the obtained content. The effects of fog computing on cloud computing and big data systems may vary; yet, a common aspect that can be extracted is a limitation in accurate content distribution, an issue that has been tackled with the creation of metrics that attempt to improve accuracy.[4]
Fog networking consists of a control plane and a data plane. For example, on the data plane, fog computing enables computing services to reside at the edge of the network as opposed to servers in a data-center. Compared to cloud computing, fog computing emphasizes proximity to end-users and client objectives, dense geographical distribution and local resource pooling, latency reduction for quality of service (QoS) and edge analytics/stream mining, resulting in superior user-experience[5] and redundancy in case of failure.[6][7][8][9]
Fog networking supports the Internet of Everything (IoE), in which most of the devices that we use on a daily basis will be connected to each other. Examples include our phones, wearable health monitoring devices, connected vehicle and augmented reality using devices such as the Google Glass.[10][11][12][13][14]
ISO/IEC 20248 provides a method whereby the data of objects identified by edge computing using Automated Identification Data Carriers [AIDC], a barcode and/or RFID tag, can be read, interpreted, verified and made available into the "Fog" and on the "Edge" even when the AIDC tag has moved on.
References
- ^ Bar-Magen Numhauser, Jonathan (2013). Fog Computing introduction to a New Cloud Evolution. Escrituras silenciadas: paisaje como historiografía. Spain: University of Alcala. pp. 111–126. ISBN 978-84-15595-84-7.
- ^ IoT, from Cloud to Fog Computing: [1]
- ^ fog-computing: [2]
- ^ Bar-Magen Numhauser, Jonathan (August 25, 2013). "XMPP Distributed Topology as a Potential Solution for Fog Computing". MESH 2013 The Sixth International Conference on Advances in Mesh Networks.
- ^ Cisco RFP-2013-078. Fog Computing, Ecosystem, Architecture and Applications: [3].
- ^ What Comes After the Cloud? How About the Fog?: [4].
- ^ Is There a Buzz Over Fog Computing?: [5].
- ^ New Solutions on the Horizon—“Fog” or “Edge” Computing?: [6].
- ^ Cloud Evolution: Back to the Future?: [7].
- ^ Bonomi, F., Milito, R., Zhu, J., and Addepalli,S. Fog Computing and its Role in the Internet of Things. In Proc of MCC (2012), pp. 13-16.[8].
- ^ Cisco-Delivers-Vision-of-Fog-Computing-to-Accelerate-Value-from-Billions-of-Connected-Devices: [9].
- ^ IoT: Out Of The Cloud & Into The Fog: [10].
- ^ Distributed intelligence and IoT fog: [11].
- ^ Fog Computing Keeps Data Right Where the Internet of Things Needs It: [12].