Edge computing is computational processing at the edge of the network, at or near the source of the data, instead of centralized infrastructure. By processing closer to the end user, communication can be done faster and latency can be reduced.
The benefits of edge computing include flexibility, scalability and on-demand delivery of applications and data. Demand for edge computing is being driven by the exponential growth of data and customer’s expectations for faster and more personalized experiences.
Cloud computing concentrates resources in a small number of large, remote data centers. While space and utilities are cheaper in these locations, these data centers are also more distant from end users.
While cloud computing is good for general purpose applications that require centralized IT, computing and storage, processing power for machine learning, hyper scale capabilities, and broad reach, it can also lead to the following challenges:
Latency: As more data is produced at more remote locations, the distance between the source of the data and the processing of the data often grows. This results in greater latency and a less optimal user experience (UX) and customer experience (CX).
Data and bandwidth: More data requires more bandwidth, ingestion, and egress, all of which increase costs. In addition, not all data that is generated has the same value and needs to be processed or stored in the cloud.
Privacy and security: Consumer privacy is becoming increasingly important and is at odds with sending and storing all data in a central place. Companies need to protect their own data, as well as consumers’ personally identifiable information (PII).
Limited resilience: If connection to the cloud goes down, the app or the site goes down as well, resulting in a bad customer experience and lost revenue.
Edge computing can help solve these constraints. However it’s important to note that edge computing and cloud computing are complementary — each has its strengths and appropriate applications.
As consumers come to expect increasingly personalized and immersive experiences, applications require more data processing and logic in real time. Edge computing helps mitigate these obstacles by decentralizing the data processing. At its core, edge computing is the processing and synthesis of data closer to end users, where the data is created and compiled, rather than in the central cloud.
This distributed approach enables developers to execute complex logic at the edge for faster and more personalized user experiences. Edge computing also supports increasingly common API-heavy workflows by acting as a central tool to fetch data from multiple backends and services and stitching them together into one cohesive experience. The edge computing approach mitigates many of the challenges of cloud computing.
Speed: Processing data closer to the source results in faster performance and response times with virtually no latency, which is necessary to enable real-time decision making.
Cost: Offloading workloads from the origin and reducing roundtrips and ingress/egress costs helps reduce public cloud spend, which continues to grow with data exponentially.
Privacy and security: Identifying and authenticating traffic well before it gets to your network ensures privacy of PII and sensitive corporate and customer data.
Resilience: Edge computing has the ability to process some data even if the connection to the core goes down, so sites and apps can continue to deliver a seamless customer experience and generate revenue.
Edge computing is an integral part of evolving, future-looking technology such as self-driving cars, smart cities, and more. However, it also has many practical business cases for enterprises looking to gain a competitive advantage in the market:
A/B testing: An ecommerce company looking to provide more personalized experiences could move A/B testing to the edge. This would allow them to push out new test pages faster and make rapid adjustments based on user engagement, all while reducing traffic back to their origin servers.
Edge authentication: A digital publisher could move subscriber authentication to the edge allowing them to deliver high value content to subscribers faster, while securing their data. With fewer calls to their origin servers to authenticate, they could also lower infrastructure costs.
Waiting rooms: An online travel agency could handle seasonal traffic bursts by assigning waiting room tokens to customers at the edge. This translates to a better customer experience and reduced infrastructure costs, with less traffic to origin.
Content stitching: A video streaming service could provide more personalized viewing experiences for customers without sacrificing performance. By stitching together user-specific content (like ads with video files) and serving them from the edge, they could achieve faster load times and reduced infrastructure costs.
Edge computing offers computing capabilities that weren’t previously available, while using less computing resources, reducing costs and enabling better user experiences.