Edge Computing Briefing Paper
The term “Edge Computing” covers a wide range of technologies, including peer-to-peer, grid/mesh computing, fog computing, Blockchain, and content delivery network. Edge Computing brings the processing of data closer to the edge of the network and lets organizations analyze important data in near real time. While many of today’s always-connected technology devices take advantage of Cloud Computing, Internet of Things manufacturers and application developers are starting to discover the benefits of doing more computing and analytics on the devices themselves. Edge Computing on Internet of Things devices is about processing device generated data that is meaningful at the time it is collected. Cloud Computing is about using the vast amount of collected data using centralized computing and storage for further analysis using such technologies like Big Data.
Edge computing allows data produced by internet of things (IoT) devices to be processed closer to where it is created instead of sending it across long routes to data centers or clouds. Doing this computing closer to the edge of the network lets organizations analyze important data in near real-time – a need of organizations across many industries, including manufacturing, health care, telecommunications and finance. Edge computing is a “mesh network of micro data centers” that process or store critical data locally and can push all received data to a central data center or cloud storage repository for further analysis. Given the nature and sensitivity of the data collected at the edge by the plethora of IoT devices security must be taken very seriously. Think of the data collected, analyzed and used while riding a self-driving car. All this happens at the edge of the network and any breach of security could lead to a life and death scenario. Data encryption, access control and use of virtual private network tunneling are important elements in protecting edge computing systems.
Edge Computing offer a solution to the swelling data dilemma by bringing processors to where the data is located, not moving data to where the processors are. Many vendors are leveraging edge computing for day to day tasks. Edge Devices are able to run full-fledged operating systems and are battery-powered. For example Android, or iOS smartphones. Google and Apple are performing in device analysis of locally captured data (like voice, photo, video, etc) before sending a subset of it to the cloud for further analytical and procedural work. Another example is IBM. Today’s IBM Watson Cloud offering already has most of the crucial intelligence to extract information from conversations. Watson Tone Analyzer can extract conversation tones from textual transcripts and Watson Speech-to-Text can easily convert recorded spoken conversations into text. The adoption of edge computing is accelerating rapidly as more and more Internet of Things (IoT) devices are deployed in the physical world.
Implications for Departments
Edge Computing can enable the Government of Canada to quickly process large amounts of data where it is collected to improve service delivery timeliness and usefulness to Canadians. Potential benefits of this technology will be realized at border crossing, airports, weather stations, etc. For example, this could be in the form of a video camera performing real-time facial expression recognition in an airport to alert border agents in case it detect suspicious attitudes or behaviours.
With the deployment of Edge Computing on Internet of Things devices Shared Services Canada will need to look at the impact on end-state data centre compute requirements once these devices start to process information locally. As more and more data is processed on Internet of Things devices a sizeable reduction of computing needs is expected in end-state data centres. Shared Services Canada will also need to assess the security and privacy implications of computing data locally on Internet of Things devices outside its data centres.
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