8,755 research outputs found
FogGIS: Fog Computing for Geospatial Big Data Analytics
Cloud Geographic Information Systems (GIS) has emerged as a tool for
analysis, processing and transmission of geospatial data. The Fog computing is
a paradigm where Fog devices help to increase throughput and reduce latency at
the edge of the client. This paper developed a Fog-based framework named Fog
GIS for mining analytics from geospatial data. We built a prototype using Intel
Edison, an embedded microprocessor. We validated the FogGIS by doing
preliminary analysis. including compression, and overlay analysis. Results
showed that Fog computing hold a great promise for analysis of geospatial data.
We used several open source compression techniques for reducing the
transmission to the cloud.Comment: 6 pages, 4 figures, 1 table, 3rd IEEE Uttar Pradesh Section
International Conference on Electrical, Computer and Electronics (09-11
December, 2016) Indian Institute of Technology (Banaras Hindu University)
Varanasi, Indi
Adaptive fog service placement for real-time topology changes in Kubernetes clusters
Recent trends have caused a shift from services deployed solely in monolithic data centers in the cloud to services deployed in the fog (e.g. roadside units for smart highways, support services for IoT devices). Simultaneously, the variety and number of IoT devices has grown rapidly, along with their reliance on cloud services. Additionally, many of these devices are now themselves capable of running containers, allowing them to execute some services previously deployed in the fog. The combination of IoT devices and fog computing has many advantages in terms of efficiency and user experience, but the scale, volatile topology and heterogeneous network conditions of the fog and the edge also present problems for service deployment scheduling. Cloud service scheduling often takes a wide array of parameters into account to calculate optimal solutions. However, the algorithms used are not generally capable of handling the scale and volatility of the fog. This paper presents a scheduling algorithm, named "Swirly", for large scale fog and edge networks, which is capable of adapting to changes in network conditions and connected devices. The algorithm details are presented and implemented as a service using the Kubernetes API. This implementation is validated and benchmarked, showing that a single threaded Swirly service is easily capable of managing service meshes for at least 300.000 devices in soft real-time
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