Advantages of edge computing reduce data center costs
Moving compute to the edge may seem like a big jump, but the costs associated with centralized data processing drive many organizations to edge computing.
As the physical world grows ever more digitized with data emanating from billions devices and sensors, the advantages edge computing meet organizations' demands for better performance, reliability and security.
谁能控制或访问传统上集中确定的问题，何时，何时，如何以及基于哪些参数问题，因为设备和物理硬件在历史上一直无法使用此类编程。云计算和集中数据处理可能是当今主要的建筑范式，但是有原因的the world's largest cloud organizations heavily invest in the edge. As we push intelligence outwards, value chains will shift.
经济转变
转移到更多distributed computingframeworks, including hybrid, will affect business models for cloud service providers, as well as millions of business adopters across every industry. Organizations today generate about 10% of their data outside a traditional data center or cloud, but Gartner predicts that number will increase to 75% within just five years. The exponential data growthcompounds the demand实际使用实时和长期战略决策的数据，但也会减少未来的财务损失。
Kaleido Insights' research analysis found the advantages of edge computing start deep in the stack, but will spread far beyond and drive the shift from centralized data processing.
Transitioning to the edge reduces the data volume sent to the cloud. Transmitting data from an endpoint to the cloud doesn't come free; the costs include bandwidth, distance traveled, associated network hardware, man-hours to configure and monitor, never mind security data in transit. Less volume translates to less traffic.
The exponential data growth compounds the demand to actually use the data in real-time and for longer-term strategic decision-making, but also will decrease financial losses in the future.
Storing, managing and extracting value from that data consumes significant energy. In fact, data centers accounted for 2% the total U.S. energy consumption in 2014, according to a study by the U.S. government on data center energy use。激活边缘引入限制和逻辑进行数据传输，并可以利用其他本地发电机，例如光，动力学，热或RF支持低功率应用。
Arguably more interesting than financial advantages edge computing alone is how they could shift deeply ingrained business practices. For example, take the standard practice sending personal data to the cloud, which most organizations do to extract personalized insights via computationally intensive analysis and machine learning. The resulting honeypots these sensitive data have exacerbated the privacy crisis, increased personally identifiable information data breaches, concerned consumers and risked compliance with GDPR.
Consider the win-win when edge-level intelligence manages the ability to extract insight for personalization and avoids the vulnerability a centralized cloud repository. Consumers' personal data remains more secure without sacrificing functionality, and organizations can continue to deliver personalization and reduce associated risks, latency and costs. This same shift applies in other areas too, such as sharing compute with external entities, contributing to data marketplaces or configuring compliance into device performance.
