The future of serverless is STATE!

1. The Future of
Serverless is STATE!
Ryan Knight - Grand Cloud
@knight_cloud

2. Limited View of
Serverless Today

3. What is Serverless Today?
● Fully Managed Services - from application run times to
databases to security
● Offloading Server Management Tasks to the Cloud
Provider
● Focused on ease of operation and development

4. Current Value of Serverless
● Goes beyond ease of running and deploying an application
● Value in Security with the “least-privilege approach”
○ I.e. function is only given permission to talk to a single
database or service
○ Isolates blast radius to that limited set of services
● Netflix utilizes a servless model to fill the “through” - i.e. run
background jobs like building ML models when resource
utilization is low / off-peak
● GraalVM optimizing cold starts and image size

5. Example of Serverless - Knative + Tekton
● Building Blocks to simplify deploying and running functions
on Kubernetes and Istio (or any L7 Load Balancer)
● Git Source -> Container -> Deploy -> Run -> Manage
● Auto Scaling / Scale to 0
● Load balancing and request routing to efficiently utilize
resources
● Monitoring to make sure the service is still running well.
● Migration to new instances as they become available.
● Downsides - YAML

6. Focus on both new economic
models and ease of development
and operations
What should serverless be?

7. Beyond Today’s Serverless
● We need a better name - not about serverless or even the
abstraction over servers.
● Serverless about a model that matches supply with
demand - James Ward

8. Berkeley View on Serverless Computing
● Decoupled computation and storage AND computation and
storage are also provisioned and priced independently
● Executing code without managing resource allocation
● Paying in proportion to resources used instead of for
resources allocated
Cloud Programming Simplified: A Berkeley View on Serverless
Computing

9. Achilles Heel of
Serverless

10. Challenges in True Serverless
● Almost impossible to auto scale data store
● Pricing model of many back-end services doesn’t match
pricing model of application runtimes

11. Where is serverless userful today?
● Parallel processing tasks—invoked on demand & intermittently,
● Low traffic applications—enterprise IT services, and spiky workloads
● Stateless web applications—serving static content form S3 (or similar)
● Orchestration functions—integration/coordination of calls to third-party
services
● Composing chains of functions—stateless workflow management,
connected via data dependencies
● Job scheduling—CRON jobs, triggers, etc.

12. Serverless has become
anonymous with
Stateless Functions

13. Common Limitation of Serverless?
● Functions are stateless, ephemeral, short-lived: expensive to lose
computational context & rehydrate
● Durable state is always “somewhere else”
● No co-location of state and processing
● No direct addressability—all communication over external storage
● Limited options for managing & coordinating distributed state
● Limited options for modelling data consistency guarantees

14. Primary Limitation?
NO
STATE!

15. We need Stateful
Serverless!

16. Abstract over State
● Similar to how Spark Abstracts State
● Currently we abstract over computing resources and
communication with Kubernetes and Istio
● Future of Serverless will be Abstracting over State
● State will be automatically managed on IN and OUT
● State Managed by a Framework
● Monitor state management for automation

17. Traditional CRUD
Database Models won’t
work

18. Traditional CRUD Database Access Won’t Work
● Difficult to Impossible to manage DB Connection Pools
● Unconstrained DB access => hard to automate operations
● Hard to understand the intention of each access.
● Is this operation a read or write?
● Can it be cached?
● Can consistency be relaxed, or strong consistency needed?
● Can operations proceed during partitions/failures?

19. Better Models for Distributed State
● CRDTs
● Counters
● Registers
● Sets
● Maps
● Event Sourcing
● Append only
Logging

20. What are CRDTs?
Data types that guarantee convergence to the
same value in spite of network delays, partitions
and message reordering
http://book.mixu.net/distsys/eventual.html

21. CRDT’s Provide a Distinct View of Data
● Not just a place to dump values like a traditional Data Store
● Abstraction of the data type
● Data Structure that tells how to build the value

22. CRDT Convergent Operations
● Associative (a+(b+c)=(a+b)+c) - grouping doesn't matter
● Commutative (a+b=b+a) - order of application doesn't
matter
● Idempotent (a+a=a) - duplication does not matter

23. Value of CRDTs in Distributed Systems
● Replicate data across the network without any
synchronization mechanism
● Avoid distributed locks, two-phase commit,
● etc.
● Consistency without Consensus

24. Event Sourcing in Stateful Serverless
● Event Sourcing is an ideal model for Serverless Computing
● Event Log tracks which events have been applied and what
is the current state
● Snapshotting to avoid replay of entire event history

25. State needs to be ACID 2.0
● Associative
● Commutative
● Idempotent
● Distributed

26. Depends on Shape of Data and Level of Consistency

27. Primary Models for Stateful Service Architecture
● Actor Based Model
● Stateful Entites
● Workflow Paradigm

28. Future is Stateful
Serverless

29. Azure Durable Functions
● https://docs.microsoft.com/en-us/azure/azure-functions/durab
le/durable-functions-overview
● Executed using Azure Function Runtime
● Built using Durable Task Framework
● Maintain execution state via Event Sourcing
● State is backed by Azure Store Tables
● Uses an Actor Like Programming Model

30. Lightbend Cloud State
● https://cloudstate.io/
● Built on top of Akka Actors with Actor Persistence and
Clustering
● Leverages familiar paradigm of Actor Programming Model

33. Cloud State - Low-Latency & High Throughput
● Managing in-memory durable session state across
individual requests
● Low-latency serving of dynamic in-memory models
● Real-time stream processing
● Distributed resilient transactional workflows
● Shared collaborative workspaces
● Leader election, counting, voting

35. Enables new Programming Models
The promise of Stateful Serverless is revolutionary and will
grow to dominate the future of Cloud - Jonas Bonér

36. Resources
● Lightbend Cloud State
● Azure Durable Functions
● Flink Stateful Functions
● Microsoft Dapr
● Towards Stateful Serverless - Jonas Bonér
● Akka Distributed Data and CRDTs - Ryan Knight

37. Thank you!
Ryan Knight - Grand Cloud
@knight_cloud