Edge computing is a distributed computing topology that focuses on bringing computation as close as possible to the data source, to minimize the latency and bandwidth use. This results in needing to run fewer processes in the cloud. These can also be moved to local places such as an edge server or an end user’s machine.
This is done to minimize costs by having processing done locally, and also reducing the data that needs to be sent to a cloud based location. Edge computation minimizes the amount of long distance communication between clients and the server.
The “edge” in edge computing, refers to the point where a user’s device connects to the internet, or the local network used by the end users to connect to the internet. The edge is close to the device geographically. In contrast, origin servers and cloud servers can be many hundreds or thousands of kilometers away from the clients they serve. The distance introduces latency to the service. This travel distance can be minimized by using edge computing while still maintaining the centralized nature that is offered by cloud computing.
Topology of edge computing
Data creators like sensores, mobile phones, etc are endpoints in the topology. They form the network’s extreme edge. Some of these endpoints have embedded compute power like mobile phones, or they may just generate information and transmit it like sensors. The endpoints with compute capabilities form edge compute.
The data that they generate can flow through nearby gateways or through wifi or LAN. These then coalesce into acquisition systems, whose purpose is to aggregate and condense data. These systems have some amount of processing capability to collect data and send it upstream. They may also perform some data filtering and analytics.
From the acquisition systems, data is sent to edge data centers or to a CDN. The edge data centers perform a majority of the computation and drive real time or near real time analytics. They result in new output data being generated. These outputs are either sent back down to the endpoints to perform some actions or sent further upstream to centralized data centers (i.e. public cloud).
The public cloud can be thought of as centralized storage and compute. Data warehousing is done and big data analytics can be run with the massive amounts of data stored.
Applications of edge computing:
- Network optimization: Edge computing may aid in network performance optimization by analyzing performance for users throughout the internet and then using analytics to select the best reliable, low-latency network channel for each user’s data. Edge computing is effectively utilized to “steer” traffic across the network for maximum time-sensitive traffic throughput.
- Self driving cars: autonomous vehicles need to have instantaneous reactions. The latency introduced in communicating to a distant server may mean the difference between an accident and a safe drive.
- Medical monitoring: monitoring systems perform a crucial role, and need to respond in real time to situations without waiting for a response from the server. The amount of patient data acquired by devices, sensors, and other medical equipment has grown tremendously in the healthcare business. This massive data volume necessitates the use of edge computing to access the data, disregard “normal” data, and detect issue data so that physicians may take rapid action to assist patients prevent health catastrophes in real time.
- Video conferencing: if backend processes are moved closer to video source, the lag and latency for video conference calls can be reduced.
- IoT devices: These devices can benefit from running code locally, rather than in the cloud for more efficient user interactions.
- Manufacturing: An industrial producer used edge computing to monitor manufacturing, enabling real-time analytics and machine learning at the edge to detect manufacturing mistakes and enhance product quality. Edge computing enabled the installation of environmental sensors throughout the production plant, providing information about how each product component is manufactured and kept — as well as how long the components remain in stock. The manufacturer can now make more accurate and timely business judgments on the factory facility and manufacturing activities.
- Retail: Retail firms may generate massive amounts of data through surveillance, stock monitoring, sales statistics, and other real-time business insights. Edge computing may assist in the analysis of this diversified data and the identification of business prospects, such as an effective endcap or campaign, the prediction of sales and the optimization of vendor ordering, and so on. Edge computing can be an efficient option for local processing at each store since retail enterprises might differ considerably in local contexts.
- Farming: Consider a company that produces vegetables indoors without the use of sunshine, soil, or pesticides. The technique cuts grow times by more than 60%. The use of sensors allows the company to measure water consumption, fertilizer density, and ideal harvest. Data is gathered and evaluated in order to determine the impacts of environmental conditions, optimize agricultural growing algorithms, and guarantee that crops are harvested in optimal condition.
Advantages of edge computing
Edge computing minimizes bandwidth usage and the use of server resources. These are finite resources and cost money. *Reduced latency is another major advantage of edge computing. Communication with a distant server introduces a delay, which can become sizable depending on the distance. With edge computing we can eliminate this latency. It allows us to process and store data faster, which allows for more efficient real time applications that are vital companies.
Drawbacks of edge computing
However, edge computing doesn’t come without its own flaws. Data at the edge can be a worry from a security standpoint. This is especially true when it’s being handled by several devices that may not be as secure as centralized servers. This creates new attack vectors for malicious attackers to compromise edge devices. Edge computing in general also needs more local hardware. However, using edge servers would go a long way in overcoming the requirement for additional hardware.
In conclusion, edge computing is an exciting new paradigm that allows us to serve data to our end users faster than ever before. Things have become even more efficient with edge computing.As a result, the quality of business operations has become higher. Edge computing is a viable solution for data-driven operations that require lightning-fast results and a high level of flexibility, depending on the current state of things.
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