sharing blockchain knowledge, performance data, benchmark

1. Sharing Blockchain
Performance Knowledge for
Edge Service Development
Filip Rydzi
Independent, Slovakia
Hong-Linh Truong,
Department of Computer Science
http://rdsea.github.io

2. Content
▪ Motivation
▪ GIAU (knowledge for blockchaIn Applications and Utilities)
▪ Experiments
▪ Conclusion and future works
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3. Motivation
Edge service development:
complex software topologies,
multiple types of components,
interactions, and protocols
Blockchain application
development:
▪ different blockchain
systems
▪ complex interactions
▪ depending on the
application, knowledge
requirement can be very
intensive
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blockchain
ops
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4. Motivation
▪ Very challenging in mastering knowledge of both edge
services and blockchain systems
▪ Key concerns in our work for collaborations among
developers
▪ impacts of blockchain deployment on performance of edge services
▪ impacts of structures of edge services on performance of blockchain
operations
▪ selection of blockchain systems for coupling with edge services
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5. Example, e.g. report obstacles in the road
▪ choosing blockchain
technologies and
operation
performance (e.g., if
just use blockchain-
as-a-service)
▪ managing backend
deployment of
blockchain systems
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6. Contributions
▪ Which blockchain features/software artefact
should be selected and deployed for which
topologies?
▪ Contribution: extensible, sharing knowledge
services about benchmarks, performance
monitoring, and testing for developer
collaboration
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7. Types of information
▪ Blockchain information
▪ operations (e.g., mining, creating transaction, …)
▪ blockchain deployment (blockchain nodes, locations and resources)
▪ Edge services
▪ IoT/mobile components
▪ edge services
▪ cloud services
▪ Metrics
▪ time, resources, availability, failure, etc.
▪ Dependencies must be captured!
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8. GIAU Architecture
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Infrastructures
External
software
artefact
repositoriesEdge
deployment
Metrics
Application

9. Linking
blockchain
knowledge
with service
&
infrastructure
knowledge
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10. Capturing deployment patterns
▪ Deployment patterns
▪ important knowledge when a developer also must develop and
operate the blockchain systems
▪ optimization of operations given deployment of blockchain systems
▪ Our consideration
▪ deployment of blockchain is represented in graphs (similar to
edge/cloud deployment model)
▪ capture it through deployment description
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11. Representing blockchain software
artefacts
▪ We support two levels:
▪ blockchain nodes and operations
▪ Blockchain nodes at the blockchain system level
▪ represent as a whole: an executable blockchain software artefact
that take a role as a node/component in the blockchain systems
▪ e.g., a Hyperledger Fabric peer node, ETH full/light/archive nodes
▪ Blockchain operations at the application level
▪ mapped to typical operations in blockchain applications
▪ operations creating, signing and submitting a transaction
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12. Examples
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{
name: 'hyperledger-fabric peer',
implementation: 'hyperledger',
feature: 'creator',
executionEnvironment: 'docker',
imageTag: 'hyperledger/fabric-peer',
configuration: {
organization {
peer_name: "peer1",
domain: "org1.example.com"
},
environment_variables: {
CORE_PEER_ID: "peer1",
CORE_PEER_TLS_ENABLED: false,
CORE_PEER_GOSSIP_USELEADERELECTION: true,
CORE_PEER_GOSSIP_ORGLEADER: false,
...
}
}
}
Node level information Operation level information
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13. Modeling experiments information
▪ We manage shared information in terms of experiment
management
▪ allow capturing different runtime information of the same
deployment structure under different benchmarks/tests
▪ there are a lot of contextual information associated with blockchain
and performance
▪ An experiment includes
▪ topology of blockchain-based edge deployment
▪ benchmark/monitoring information
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14. Incorporating benchmark and
monitoring data
▪ Key issue for the success of any sharing knowledge service
▪ Our approach is generic:
▪ allow different benchmark and monitoring information to be stored
▪ It is not our goal to say “standard” or “best information” but
provide useful information
▪ Combine both document database and graph database to
store information
▪ documents: software artefact and infrastructure information from
the benchmarks
▪ graphs: deployment pattern
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15. Searching knowledge
▪ Apply basic search
▪ input: a deployment pattern
(derived from the application’s
topology)
▪ output: a recommendation to
the developer about a suitable
deployment
▪ Support TOSCA
▪ we derive node types from
existing TOSCA Cloudify node
types
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From provided deployment pattern we
create interaction pairs and use them
to search possibilities (not optimal but
work)

16. Prototype
▪ Microservices
architecture
▪ Typescript/NodeJS
▪ MongoDB/Neo4J
for knowledge
store
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https://github.com/rdsea/blockchainbenmarkservice/tree/master/giau
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17. Experiments
▪ Challenging: difficult to find a real performance for blockain
based edge services
→ we have also developed our own benchmark (not in this talk)
https://www.researchgate.net/publication/333388734_Benchmarking_Blockchain_Interaction
s_in_Mobile_Edge_Cloud_Software_Systems
▪ Use both realistic data and emulated data
▪ real data: based on 324 benchmarks we run with Hyperledger and
Ethereum using cloud resources
▪ emulated data: 250 diverse deployment patterns, generated
randomly by following a normal distribution
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18. Example of searching knowledge
node_templates:
edge_dev1:
type: giau.nodes.rsu
relationships:
- target: iot1
type: giau.relationships.nodes_network
edge_node:
type: giau.nodes.edge
relationships:
- target: edge_dev2
type: giau.relationships.nodes_network
- target: edge_dev1
type: giau.relationships.nodes_network
- target: iot3
type: giau.relationships.nodes_network
……
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Input
Output visualized by Cloudify
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19. Input & output search example
https://github.com/rdsea/blockchainbenmarkservice/tree/master/
giau/tests/data/examples
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20. Performance Evaluation
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Configuration: Intel Core i7-6820HQ CPU, 16GB RAM memory,
Ubuntu 18.04 during the testing
Data: the most complex
emulated deployment
patterns is composed of 196
nodes (99 thing node types,
43 edge dev, 41 edge node,
12 cloud).
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21. REST APIs
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Enable benchmark
data ingestion
pipelines e.g.,
Logstash, Apache Nifi,
and Python/JavaScript
programs
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22. Example of ingesting benchmark
results
https://github.com/rdsea/blockchainbenmarkservice/tree/master/
experiments/results/benchmarks_results
https://github.com/rdsea/blockchainbenmarkservice/tree/master/
utilities/results_parser
https://github.com/rdsea/blockchainbenmarkservice/blob/master
/utilities/results_parser/allResultsToGIAU.sh
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23. Example of using APIs for search
https://github.com/rdsea/blockchainbenmarkservice/tree/master/
giau/src/examples
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24. Conclusions and future work
▪ Sharing knowledge about blockchain and edge deployment
▪ crucial for blockchain-based edge development
▪ GIAU is a generic microservice framework
▪ generic knowledge models and experiment management, extensible
APIs for ingestion and search
▪ Future work
▪ prototype improvement, search features & integration with DevOps
▪ Prototype is under continuous development:
▪ https://github.com/rdsea/blockchainbenmarkservice/tree/master/giau
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25. Thanks!
Hong-Linh Truong
Department of Computer Science
rdsea.github.io
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We are hiring PhD students/Postdocs! Pls. contact me if you are
interested!