Key features of Using Service Discovery and Service Proxy: *Automated registration of microservices *Find microservices by logical name *Supports broad multiple APIs including Eureka

1. Using Service Discovery and
Service Proxy
A Simple Microservices Example

2. 2
Monolithic application Microservices
Scaling Scaling
Moving from Monolithic Applications to Microservices
Microservices:
are an architecture style, in which
large complex software applications
are composed of one or more services
can be deployed independently of
one another and are loosely coupled
should be small and focused on
completing a single task
typically communicate through
language-neutral APIs like an
Hypertext Transfer Protocol (HTTP)-
based resource API using REST

3. Benefits of Microservices for Developers
• Enables you to avoid working on large code bases
• Easier to maintain or add to features
• Use existing skills, or the most optimal language
• Improves deployment and load times in your development tools
• Makes debugging easier
• Enables teams to work more independently of each other
• Simplifies tracking code dependencies
• Enables complete ownership by a self-contained single team, from
definition through development, deployment, operations, and sunseting
• Makes it easier to address performance bottlenecks
3

4. Service Discovery, Service Proxy and Microservices
4
• Service Discovery enables developers to easily
register and find instances of their microservices
Service
Proxy
Service
Proxy
Service
Proxy
Rules
Service
Proxy
Rules
Service
Discovery
Service
Discovery
Client codeClient code
Service
A
Service
A
Service
B
Service
B
Service CService C
Service Registration
Heartbeats
Requests
Locate
service
Rules
to apply
Call requested
services using
rules
• Service Proxy automatically balances workloads
across instances to improve performance
Together, these two services provide essential capabilities for
creating and using microservices

5. A Simple Microservices example
5
Maureen
DevOps Pro
Maureen has an online store
application with a front end that
generates a web UI, and a backend that
checks the inventory to see if items are
out of stock.
Her original design was a single
monolithic application but she has
decided to redesign it with a
microservices based architecture to
improve scalability, reliability, and
business agility. She’s going to
split her app into three services –
WebUI, Inventory and Database.
Monolithic
WebUI + Inventory +
Database
WebUI
Inventory
Before: Monolithic app After: Using microservices
Database

6. Service Discovery – Automates Service Registration
6
Let’s assume Maureen’s new architecture is
container based so each instance of a service will
be run in a container.
A key task in transitioning to a microservices
architecture is to register each instance of a
service.
Service Discovery automatically
-Registers each instance of a service
-Makes it easy to find instances of services once
registered
-Periodically checks the health of each service
Inventory
Helper Script:
Register
(“Inventory
Instance 1”)
Helper Script:
Register
(“Inventory
Instance 2”)
Helper Script:
Register
(“Inventory
Instance 3”)
Service
Discovery

7. Service Discovery – Finding Instances Easily
7
Once instances of services are registered,
Service Discovery makes it easy to find them.
Service Discovery provides a simple REST based
API to find instances by logical name.
Without Service Proxy, its up to the client code to
determine which instance from the list to call.
For example you could call the first instance
returned.
Web UI
Service
Discovery
Query(“Inventory”)
Response:
“Inventory Instance 1”,
“Inventory Instance 2”,
“Inventory Instance 3”
Web UI
Call “Inventory Instance 1”()
Inventory
1
2
1
2
Return response of
“Inventory Instance 1”()

8. Service Proxy – Microservices Load Balancing
8
Service Proxy automatically balances workload across active instances
Service Proxy calls Service Discovery under the covers and then performing basic load
balancing of instances through a round robin process without the client writing code.
Service Proxy also logs the performance of the microservices it proxies, allowing you to track
the performance of your microservices without the client writing code.
Web UI
Service
Proxy
CallProxy(“Inventory”) Service
Discovery
Query(“Inventory”)
Response:
“Inventory Instance 1”,
“Inventory Instance 2”,
“Inventory Instance 3”Inventory Call “Inventory Instance 2”()
based on round robin.
And log its processing time
Return response of
“Inventory Instance 2”()
1
2
1
2

9. Service Proxy – Making your solution more reliable
Service Proxy also lets Maureen improve the
quality of her app by making it easy to inject
delays and failures during testing without writing
code
Injecting delays will help Maureen simulate
performance bottlenecks
Injecting failures will help her test how well the
application recovers from various error situations
Web UI
If WW Inventory
request takes more
than 120 ms, switch to
Regional Inventory
instead
WW
Inventory
Regional
Inventory
Service
Proxy
Configure Proxy to Inject
delays to WW Inventory
to simulate WAN delays
Web UI
If Inventory is
not available,
use Local
Cache
InventoryService
Proxy
Configure Proxy to fail
some calls to Inventory
to simulate network
failure
Local
Cache
1
2
1
2

10. Service Proxy – Future Enhancement (Gremlin)
The initial public beta of Service Proxy will support two simple flavors of injection testing:
•Delays that are always injected through Proxy
•Failures that are randomly injected based through Proxy on a percentage (25% of the time,
50% of the time, etc…)
IBM Research has created a testing framework (Gremlin) that supports scripted orderly
testing via a recipe, and beyond beta we wish to extend Service Proxy to support integration
with this.
This will provide the capability to script specific failures for orderly resiliency testing that
helps clients to:
•Create repeatable sophisticated test cases via Gremlin recipes for delays, failures, and
scrambled communication across multiple microservices
•Separate synthetic testing load for testing instances from real user traffic running on
production instances

11. • Maureen:
– I just deployed a new version of the “WebUI” service. I want to make sure my
service can tolerate failures of other services and the environment.
• Release manager:
– I want to make sure each service is resilient to failures before a new version
release
11
Gremlin - simple use case

12. Inventory Database
Service
proxy
WebUI
Gremlin Vision
12
fault injection
rules
Not affected
Bluemix
Logging
Service
Logs
Synthetic Load
Real user traffic
Bluemix Tenant
Gremlin ServiceMaureen
Release
Manager
Scenario
Run
scenario Result What went wrong?
Service
Overload
❌
Inventory did not trigger
circuit breaker when DB
service was overloaded
Network
Instability
…
Service Status
WebUI
✅
Inventory ❌
Database ✅
LaunchLaunch
LaunchLaunch
Failure
Orchestratio
n
Behavior
Validation
Subjected to failures
Fallback
cache

13. Creating Your Own Gremlin Recipe
run_recipe.py topology.json gremlins.json checklist.json
BoundedRetries: Check if
WebUI retried its API calls to
RegionalInventory, no more
than 5 times, with 30ms gap
Fake “Overload”: Gremlin will intercept all
API calls to RegionalInventory
and return HTTP 503/429
Failure injected only if request has HTTP
header
“X-Gremlin-ID: delayTest-<val>”

14. Invitation to Join Our Beta Program
Interested in learning more? Join our beta program
Both Service Discovery for Bluemix and Service Proxy for Bluemix are in Beta in 1Q2016 and
available on the public Bluemix catalog: www.bluemix.net
For more information, contact
Doug Rothert, IBM Cloud Offering Manager
drothert@us.ibm.com
@DougRothert).

15. Notices and Disclaimers
15
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initial publication and could include unintentional technical or typographical errors. IBM shall have no responsibility to update this information. THIS DOCUMENT IS
DISTRIBUTED "AS IS" WITHOUT ANY WARRANTY, EITHER EXPRESS OR IMPLIED. IN NO EVENT SHALL IBM BE LIABLE FOR ANY DAMAGE ARISING FROM THE
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products and services are warranted according to the terms and conditions of the agreements under which they are provided.
Any statements regarding IBM's future direction, intent or product plans are subject to change or withdrawal without notice.
Performance data contained herein was generally obtained in a controlled, isolated environments. Customer examples are presented as illustrations of how those
customers have used IBM products and the results they may have achieved. Actual performance, cost, savings or other results in other operating environments may vary.
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16. Notices and Disclaimers Con’t.
16
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