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1 .Smart Workloads: Automated Routing,
Scaling of K8s and Serverless Functions
Enlin Xu, Director of Advanced Engineering, Turbonomic
2 .Agenda
• Who are we
• Challenges to deploy and operate applications in Cloud
• Smart Workloads in Edge Computing
• Demo
• Key Takeaways
3 .Who are we?
• Founded in 2009 We’re Hiring!
• Headquartered in Boston, MA, USA Join our engineering team as
• 500+ employees we build for the end game.
• $100m+ Revenue in 2017
Founded on the idea that software should manage IT resources, not people.
Our software uses a Common Abstraction with Economic Principles to unleash
automation!
IDC Innovator: Multicloud
Management, 2017
4 .I want to run some code in cloud.. And?
• Operational complexity
• VM – Configure machines, storage, network, OS, etc.
• Container – Configure application, scaling, etc.
• Serverless – Memory configuration, where to run, etc.
• Capacity management concerns
• Cloud has infinity capacity – what about your budget?
• Scalability – I want to have infinite scale!!
• But only pay for what you really need
5 .Serverless looks simple enough! Or is it?
6 . Serverless Use Cases
• An image upload to S3 triggers a Lambda to compute a thumbnail to
post on Twitter
• The same upload triggers another Lambda to analyze the image and
create metadata such as place, persons, etc.
• A code commit or PR merge triggers build jobs
• An IoT sensor on a garbage can detects its fullness and triggers a
function to put the garbage can on a pickup schedule
• An ML scheduler kicks off a series of MapReduce worker functions
• …
7 . Complex Serverless Chains
https://hackernoon.com/capture-and-forward-correlation-ids-through-different-lambda-event-sources-220c227c65f5?gi=48f5de1450fa
8 .Challenges
• Functions aren’t properly sized
• When undersized high response time manifests
• When oversized unnecessarily high cost accumulates
• Solution: Rightsizing
• Cross-region data transfer cost isn’t considered
• Place Lambda in every region (stateless, no charge without using)
• Solution: Re-route traffic to a different region to minimize overall transfer cost
• A Kubernetes cluster is overloaded
• Traditional solution: Scale out the cluster by adding more VMs
• Alternative Solution: Route traffic out to a different cluster => Traffic Engineering
9 .Smart Workloads with Edge Computing!
• Easier to place, size (short-lived, smaller in size)
• No need for continuous placement of function invocations
• Traffic Engineering capable (stateless)
• Multiple instances of the same function can be placed across
clusters and regions
• Client workload can be distributed over multiple function instances
• Edge Computing
• Moving intensive workloads from the Cloud out to the Edge of the
network.
10 . Demo: Route Control in Edge Computing
Client A
Client A
Client
Istio
Pushing Policy to route the traffic Push
routes
Turbonomic
Decision Making Engine to make real time
Kong routing decision based on application
API Gateway to forward to the correct endpoint performance
AWS
Knative
Lambda
Edge 1 Cloud
11 .The Serverless Platform
Public Cloud
Kubernetes-based
13 .Service Routing Layer
14 .Decision Engine
Applications Capacity Databases
Compute Public Cloud
• AppDynamics
• Cisco HyperFlex
• Cisco UCS Manager
Cisco • Cisco UCS Director
Storage • Cisco Tetration
• Cisco ACI
• Cisco CloudCenter
• …
15 .Abstraction: The Supply Chain Market
2• Services entities shop for the best overall
price for every commodity (resource)
they need to perform.
Service Entity
App, Container, VM, Physical
Machine, Fabric, etc.
Buy Sell
Commodity 3
CPU, Memory, Flow, IO, Within 1 hr. you
1• Everything in the data center is Storage, IOps, etc. see these
abstracted into a supply chain relationships
market. and metrics in
Turbonomic
16 .Analysis: Economic Supply, Demand, and Price
Demand Supply
Price after • Utilization (demand/supply)
demand
increase determines price.
Delay
• Workloads/service entities
Price
Current
Price
make scaling, placement,
and capacity decisions
based on all the resources
they need.
Utilization
Quantity
0% 100%
17 .Automation: Real-time Action
Continuous Optimization Capacity Management
Real-time actions drive continuous Quickly & accurately model what-if
health: scenarios:
Placement Workload growth
Sizing Add/remove hardware
Provisioning Cloud costs
19 .Key Takeaways
• Deploying in cloud is complicated, even serverless!
• Workload needs to be Smart!
• Resizable
• Scalable
• Routable
• Take advantage of Edge Computing!
• High bandwidth + Low latency
• Decision-making is critical!
• Performance
• Cost Efficiency
• Compliance
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