Edge computing remains a growing and exciting new architecture for enterprises across multiple industries. Distributing computing power across a decentralized network gives new opportunities for AI- and IoT-based applications. Demand is such that Gartner predicts by 2025, 75% of enterprise-generated data will be created and processed on the edge.
However, the cost and latency of sending all this data back to the cloud for processing are significant. To retain profitability, enterprises will need to keep as much data on the edge as possible while also moving processing to the edge. The benefits of these requirements are many, including:
- Real-time processing, lower latency, faster answers
- Resilience and availability, not relying on a stable connection to the cloud
- More resource- and cost-efficient, less data shipping
I believe organizations achieve these requirements and realize these benefits via a concept I call the cloud-to-edge data plane.
A cloud-to-edge data plane unifies cloud and edge into a single solution that allows you to define your data and business logic once, package it up as a service and simply deploy it without having to worry if it should run in the cloud or out at the edge (which might change over time). This frees up the data plane automatically and adaptively to move services around, optimizing data access, coordination and replication, ensuring physical co-location of data, compute and user for the lowest possible latency with the highest throughput and availability.
Choosing between cloud and edge is not a binary, black-and-white decision but has a lot of shades of gray in between. From an architectural perspective, the edge consists of many hierarchical layers between the cloud and the devices; each layer is further away from the cloud but closer to the end-users—a continuum.
Where you need your services to run in this continuum is very much use-case-dependent and might change over the lifetime of an application, depending on usage patterns or changes in features and business requirements. Critical parameters like latency, throughput, consistency, availability, scalability, compute resources, etc. change drastically as you move through this continuum. This results in both new opportunities and challenges:
One of the new services enabled by this architecture is mobile location transparent services. This new class of services is a thing of beauty. It means services are free to run anywhere and collaborate with other services without restrictions. It also allows the underlying platform to optimize overall system behavior by relocating services as needed, based on changes in usage, resources available, communication patterns, failure scenarios, and more.
If services are not location-transparent, then the system’s runtime topology needs to be part of the design and encoded into the services themselves. In practice, this means hardcoding decisions such as:
- If the communication between services is local or distributed—allowing for concurrency or distribution
- If services are to be considered stationary or mobile—pinned to a specific physical address or free to be relocated
- How the services can respond to failure—self-healing bulkheads that can be migrated to other nodes if needed or tied at the hip with the failed node
Locking in decisions like this upfront will make the system harder to run efficiently, evolve, adapt and maintain over time. These should not be design or development decisions, not deployment decisions, but runtime decisions, not affecting the code.
It’s hard to predict the future. This includes knowing how an application will be used over time, how changes in usage and load patterns will evolve, and how future features or changes in business requirements will impact the initial design, assumptions, and runtime/operational characteristics.
Not having to take these decisions or worry about how they will be maintained efficiently but only focusing on API, data model, and business logic reduces time to market and overall risk substantially.
Here’s my vision for data…
Data always exists wherever and whenever needed, but only for the time it is needed.
- Having the latest and correct data always available to you right when you need it and wherever you happen to be located—in the central cloud or at the far edge—sounds like a developer’s dream.
- On the other hand, it’s been said that “laziness is a virtue,” which is very much true of data management in a distributed system. Replicating data to locations where it will never be needed is a loss (unless needed for resilience/failover). The same is true for holding on to data longer than needed (holding resources hostage and forcing needless consistency mechanisms).
- Balancing this is very hard and should ideally be done by the platform adaptively at runtime at a global system level, rather than by the developer.
Data is always co-located with processing and end-user—ensuring ultra-low latency, high throughput and resilience.
- If not, as in traditional three-tier stateless architecture, data is often somewhere other than where you need it to be and therefore has to be fetched before being used and pushed out to storage after modification. This increases latency, reduces throughput and puts the service at the mercy of the availability of the storage service.
Data and compute move adaptively together with the end-user.
- Many use cases at the edge serve users on the move (e.g., cell phones, cars). To serve these users as efficiently as possible, their data and compute need to move geographically with them.
Data is injected into the services on an as-needed basis—automatically, timely, efficiently and intelligently.
- Data storage management is often very intrusive in application design and business logic: How to map domain objects to DB tables (OR-mapping), transaction management, storage API calls for fetching and updating the state, etc.
- Externalizing all of this to the platform and instead having the data injected into the service when new data is available (just as we are used to with events in a pub/sub system) makes the code easier to design, write, understand and maintain.
- Additionally, if the service is doing all data management itself, then it’s a black box to the platform. The platform has no idea what access patterns have been used, on what data, when data can be safely cached, replicated, sharded, prefetched, shared, etc. On the contrary, if the platform is doing all the data management, then it can look broadly across all services and safely optimize data management for the overall system holistically.
I believe RedMonk analyst Steve O’Grady’s concept of vertical integration fits well into this narrative. It all leads to what I feel is a distillation of a cloud-to-edge programming model (a continuum, as described previously). This model is about abstracting away everything that is not core business value, leaving the three things that can never be delegated: Data model, API and business logic.
As Timothy Keller said, “Freedom is not so much the absence of restrictions as finding the right ones, the liberating restrictions.”
By using a serverless developer experience (DX), I believe the answer to extending real-time data and computing to the edge can be realized in a fully managed developer PaaS model. That model abstracts away all infrastructure into a single declarative programming model and effectively unifies cloud-native and edge-native development. This is “vertical integration” and the cloud-to-edge data plane vision.
Conclusion
Today, cloud computing and edge computing are typically treated as separate domains. However, rapidly evolving demands for intelligent low-latency processing of edge-generated data are creating requirements for a new approach combining cloud and edge into a single continuum. With cloud-native applications, architects and developers have already embraced the concepts required to build services that run in an essentially infinitely scalable (but logically centralized) cloud infrastructure. Extending this thinking to a fully decentralized infrastructure, including the edge, is a natural next step in enterprise application architecture. Programming models that enable a data-centric, location-transparent approach combined with intelligent runtimes that automatically enable data and processing to be co-located where and when needed will allow developers to take full advantage of such a wholly distributed edge-to-cloud environment.
