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# Serverless: Past, Present and Future

Think serverless is just about basic functions? Discover how to mitigate cold starts, secure deployments with Terraform, and orchestrate complex Kubernetes workflows to eliminate idle cloud costs.

- **Speakers:** Oliver Arafat
- **Event:** WeAreDevelopers LIVE
- **Published:** September 4, 2020
- **Duration:** 1:01:02
- **URL:** https://www.wearedevelopers.com/videos/34-serverless-past-present-and-future

## Summary

The presentation traces the evolution of serverless computing from its early origins in managed object storage to today's expansive ecosystem covering compute, databases, APIs, and analytics. Defining serverless as an execution model where a third party manages the servers and billing is strictly tied to exact usage—eliminating idle infrastructure costs—the architecture enables highly scalable "glue code" across modern cloud environments. Event-driven serverless architectures naturally fit varied use cases, traversing IoT data processing and MapReduce batch operations to on-the-fly image transformations and dynamic API development.

To operationalize these concepts, the session illustrates how to deploy a serverless backend using declarative infrastructure as code with Terraform, paired with continuous integration pipelines like CircleCI. Key technical implementations include utilizing contextual temporary security tokens—rather than vulnerable hard-coded environment variables—to authorize function access to managed databases. Addressing inherent architectural constraints, developers are urged to keep function deployment artifacts extremely small (specifically under 50MB) to mitigate cold start latencies. However, the speaker cautions that "whenever you have a scenario where you need to load large data sets upfront... think again is this really the right service to use," advising that heavy state management isn't universally suitable for pure function-as-a-service models. Leveraging local debugging emulators through VS Code plugins is highly recommended to accelerate code iteration without incurring additional cloud overhead.

Looking beyond basic functions, the future of the serverless paradigm heavily involves orchestrating complex workflows—vital for managing distributed transactions with built-in rollback compensations across chained applications. Hybrid approaches utilizing frameworks like OpenWhisk and virtual kubelets bring the serverless application model directly into standard Kubernetes environments, effectively decoupling platform operations from application development. Moving forward, the serverless mindset is expected to enforce finer-grained billing across all managed datastores while paradoxically granting developers transparent access to underlying hardware for GPU-accelerated computing.

**Keywords:** serverless computing evolution, infrastructure as code, terraform cloud deployment, CI/CD pipeline integration, cold start mitigation, serverless function workflows, distributed transaction logic, temporary security tokens, API gateway integration, local debugging emulators, virtual kubelet kubernetes, event-driven glue code, serverless database billing, state management challenges, alibaba cloud function compute, openwhisk framework

## Chapters

1. **Defining the core concept of serverless computing** (02:44) — Understanding how an execution model manages workloads using fine-granular billing that focuses exclusively on actual usage.
1. **Discussing history and enterprise adoption of serverless** (04:58) — How major cloud providers introduced execution architectures and why companies are accelerating adoption to improve release velocity.
1. **Exploring distinct components within the serverless ecosystem** (08:27) — Reviewing various components like compute frameworks, data stores, API gateways, and analytics resources within a cloud provider environment.
1. **Architecting common use cases via serverless design** (12:31) — Designing architectures for batch processing, image manipulation workflows, and real-time event aggregation.
1. **Provisioning serverless resources via declarative infrastructure tools** (17:09) — Employing declarative configuration tools to orchestrate tables, define primary keys, and align execution endpoints.
1. **Managing runtime permissions and contextual event processing** (19:15) — Passing temporary credentials dynamically through context mechanisms to restrict component access and manage payload execution safely.
1. **Automating deployment pipelines for serverless components** (24:21) — Integrating continuous integration repositories to package application scripts and automate configuration deployments upon code commits.
1. **Examining cloud console features for interactive deployment** (26:30) — Navigating native browser tools to modify project files, test parameters, and construct specialized custom runtime dependencies.
1. **Identifying structural and operational constraints of serverless** (28:44) — Resolving challenges associated with state propagation, debugging across disconnected setups, and cold start impacts on data availability.
1. **Applying structural best practices for code validation** (36:45) — Utilizing local emulators within code editors to streamline validation while minimizing execution package dependencies.
1. **Coordinating complex scenarios via visual workflow tools** (39:26) — Designing complex sequence diagrams with compensation events to handle failure models across diverse transaction types.
1. **Implementing serverless abstractions natively on kubernetes infrastructure** (41:39) — Offloading virtualized node management while allowing engineering teams to leverage customized portable computing environments without infrastructure overhead.
1. **Forecasting shifts toward distinct hardware and abstraction paradigms** (47:03) — Anticipating granular resource assignment models that push towards distinct hardware specifications and integrated execution mechanisms.
1. **Answering queries regarding geographic operations and provisioned models** (51:25) — Addressing regional integration concerns and identifying tier behaviors affecting instance cold start penalties.

## Related Moments

- [Defining serverless computing capabilities and execution environments](https://www.wearedevelopers.com/videos/243-serverless-native-java-with-quarkus) (from "Serverless-Native Java with Quarkus")
- [Exploring hybrid models, monoliths, and serverless computing](https://www.wearedevelopers.com/videos/261-why-you-shouldn-t-build-a-microservice-architecture) (from "Why you shouldn’t build a microservice architecture ")
- [Real-world challenges in adopting serverless architectures](https://www.wearedevelopers.com/videos/56-end-the-monolith-lessons-learned-adopting-serverless) (from "End the Monolith! Lessons learned adopting Serverless")
- [Defining serverless and its evolutionary phases](https://www.wearedevelopers.com/videos/491-serverless-landscape-beyond-functions) (from "Serverless landscape beyond functions")
- [Overview of serverless architectures and function as a service](https://www.wearedevelopers.com/videos/814-serverless-architectures-with-spring-cloud-functions-and-knative) (from "Serverless Architectures with Spring Cloud Functions and Knative")
- [Recapping serverless architectures and Swift cloud integrations](https://www.wearedevelopers.com/videos/133-server-side-serverless-in-swift) (from "Server Side Serverless in Swift")

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