My presentation for the UCLouvain Ph.D. Confirmation https://kkpradeeban.blogspot.com/2018/01/ucl-phd-confirmation.html

1. A Software-Defined Service-Oriented
Architecture for Distributed Workflows
Pradeeban Kathiravelu
Supervisors: Prof. Luís Veiga
Prof. Peter Van Roy
Prof. Marco Canini
LightKone M12 General Meeting, UNINOVA, Caparica, Portugal.
16/01/2018.

2. Agenda
●
Overview and Motivation
●
Goal of the Thesis
●
Current Work
– Composing Network Service Chains at the Edge
●
Ongoing and Future Work

3. 3/39
Overview and Motivation
I

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Services
●
A core element of the Internet ecosystem.
●
Various types of Services
– Web services and microservices
●
key in modern cloud applications.
– Network services / Virtual Network Functions
●
firewall, load balancer, proxy, ..
– Data services
●
data cleaning, data integration, ..
●
Interesting common research challenges:
– Service placement.
– Service instance selection.
– Service composition or “service chaining”.

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Why Service-Oriented Architectures
for our systems?
●
Beyond data center scale.
– Thanks to the fact that services are standardized.
●
SOA and RESTful reference architectures.
– Multiple implementation approaches such as Message-
Oriented Middleware.
●
Service endpoints to handover messages internally to the broker.
●
Publish/subscribe to a message broker over the Internet.
●
Flexibility, modularity, loose-coupling, and adaptability.

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Software-Defined Networking (SDN)
●
Enables global view of the data center network on a
single controller.
●
Separation of control-plane and data-plane
●
Improved configurability
– Bring the control of the network to the software developer!
●
Key technology enabling separation of mechanisms
from policies.

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SDN for Cross-layer Optimizations
(Program for: application ↔ network)

8. 8/39
Goal of the Thesis
II

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Our Vision
●
Optimal placement and migration of service chains beyond data
centers.
– Minimal latency and efficient bandwidth usage.
– Quality of Experience, adaptability, and resilience.
●
Bring the control back to the service user.
– As it was in the pre-cloud, pre-multi-tenancy era.
– Focus on users consuming several third-party services.
●
Services typically deployed in distributed clouds and edge.
●
An approach inspired by Software-Defined Networking (SDN).

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Our Proposal
●
An approach inspired by SDN for an adaptive placement and
resilient execution of service chains.
– Software-Defined Service Composition.
– Providing global awareness through a combined approach
●
at application-level (e.g. web service engines and service registries)
●
at network-level (SDN controller).
●
Challenge:
– We have a much larger scale and problem complexity.
●
Compared to the classic SDN.

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Thesis Contributions (1)
●
Web services
– An adaptive and resilient architecture for web service compositions and workflow
management in the wide area network, extending SDN.
(ICWS’16(ICWS’16 and a book chapter).and a book chapter).
– Reprogram easily for service failures or congestions.
●
Network Services
– Model and execute network services through a unified orchestrator
●
Deploy execution and simulation units through a coherent model.
●
(CoopIS’16, SDS’15, and IC2E’16 (short)).
– Resilience in multi-tenant environments.
●
NCA’16, IM’17 (short), and EI2N’16.

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Thesis Contributions (2)
●
SDN-inspired Approach for scalable user-driven
service compositions
– Chaining data services into big data processing workflow
((CoopIS’15CoopIS’15,, DMAH’16, DMAH’17, and a book chapter).).
– Microservice compositions at the edge clouds to enable
smart environments and Cyber-Physical Systems
((SDS’16SDS’16, M4IoT’15, and, M4IoT’15, and SDS’17SDS’17).).

13. 13/39
Composing Network Service Chains at the Edge:
A Resilient and Adaptive Software-Defined Approach
III

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Introduction
●
A network flow goes through various services.
●
Increasingly network services placed at the edge.
– Limitations in hosting all the network services on-premise.
– Closer to the user than centralized clouds.
– Various shades of the edge.
●
Heavy vs light edge, edge clouds, community clouds, ..

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Network Service Chaining (NSC)
●
Chaining a number of connected network services.
●
Dynamically composable.

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Challenges in achieving
Service Chaining at the Edge
●
Dependencies among the network services.
– Need to be accessible from each other.
●
Service Level Objectives of the service chain users.
– Latency, throughput, monthly cost, ..
●
Finding the optimal service chain for a user request.
– In general, an NP-hard problem.

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Service Chain: s1
→ s2
→ s3
→ s4
●
Goals
– Services close to the user.
– Services close to the following services in the chain.
– Satisfying user Service Level Objectives!

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Our Contribution: Évora
●
A graph-based algorithm to incrementally construct
and deploy service chains at the edge.
●
An Orchestrator in the user device or a surrogate, to
place and migrate service chains, adhering to the
user policies.
●
An architecture extending SDN to wide area to
efficiently support the service chains at the edge.

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Évora Approach
●
Initialize once per user device:
– Step 1) Construct a service graph.
●
Initialize once per a user’s service chain.
– Step 2) Find matching subgraphs for the user’s
service chain as partial, potential chains.
– Step 3) Complete matches → Potential Chains.
– Step 4) Service chain placement at the best fit
among the possible chains.
●
Execute following the service chain.

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1) Data center graph → service graph
●
Construct a service graph in the user device.
●
As a snapshot of the available services at the edge.

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2.1) Matching subgraphs in the service
graph for the service chain.
●
Matching subgraphs → partial chains.
●
Consider alternative representations.

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2.2) Find Potential Chains, in Parallel.
●
Construct matching subgraphs as potential chains.
– while noting the individual service properties
●
monthly cost, throughput, latency, ..
●
Incrementally calculate a “penalty value” for each
potential chain that is being constructed.

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2.3) Record the complete matches
with their penalty value
●
Évora defines a penalty value based on
– Total monthly cost (C) for the chain,
– End-to-end latency (L) for the chain,
– The inverse of throughput (T-1) (defined by the
minimal throughput service in the chain).
– Can be extended for additional properties such as
uptime.
●
Évora aims to minimize the penalty value.
– With user giving weight to each of the properties.

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Objective: Minimize the penalty value
●
Penalty function, with normalized values of C, L, and T.
●
Solve this as a Mixed Integer Linear Problem.
●
α,β,γ ← Non-negative integers specified by user.
●
The penalty function can be extended with powers.

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3) Complete matches →
Potential Service Chain Placements
●
Ability to place the entire service chain in the
matching subgraph.
– Complete matching subgraph, i.e. a potential service
chain placement is found.
●
Record.
●
Stop procedure once all the nodes are traversed.

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4) The service chain placement
●
Service chain is placed on the service composition
with the minimal penalty value among the alternatives
(matching subgraphs).
– Possible updates and migrations.
– Future service unavailability → choose the next.

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Solution Architecture: A federated
controller deployment for the edge
●
Extending the control offered by SDN Controllers
– from data centers to a multi-domain environment.
– With Message-Oriented Middleware.

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Notifications for Service Availability
●
Service chain placements and migrations.

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Evaluation
●
Microbenchmark how user policies are satisfied with
Évora for service chains among various alternatives.
– Complexity of the problem space.
– Algorithm effectiviness in satisfying user policies.
●
Efficacy: Closeness to optimal results
– minimizing penalty function results in improved quality of experience
●
Efficiency: execution times depending on problem space

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Problem Scale: Representation of the
service graph from the data center
graph
●
The number of links in this service graph grows
– linearly with the number of edges or links between the edge data centers
– exponentially with the average number of services per edge data center.

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User policies with two attributes
●
Location of the circles → Properties (C, L, and T).
●
Darker circles – chains with minimal penalty, the ones
that we prefer (circled).
T ↑ and C ↓ T ↑ and L ↓ C ↓ and L ↓
●
Results show user policies supported fairly well.

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●
Policies with three attributes: One given more prominence
(weight = 10), than the other two (weight = 3).
●
Results show efficient
support for multiple
attributes with different
weights.
Radius of the circles –
Monthly Cost

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●
Two attributes given more prominence (weight = 10),
than the third (weight = 3).
●
Results show efficient
support for multiple
attributes with different
weights.
Radius of the circles –
Monthly Cost

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Conclusion
●
More and more services hosted at the edge.
●
Service chains have more constraints than stand-alone
services.
●
Évora supports efficient chaining of network service.
– Leveraging a software-defined approach for services
●
Extending SDN.
●
Future Work:
– Evaluate the performance with network service deployments at
the edge with actual workload.

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Ongoing and Future Work
IV

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1) Software-Defined CPS workflows
in the light-weight edge
●
Can we tackle some operational and scale
challenges of Cyber-Physical Systems?
– By representing them as composable service chains at
the edge?
– Target: CLUSTER Journal.
●
Invitation for selected papers from SDS’17.
●
Deadline: Jan 31st.

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2) A Service-Oriented Workflow for Big
Data Research at the Edge
●
Analyse decentralized big data (TB-scale) with a service
based data access and virtual integration approach.
– Addressing data related optimizations as service chains.
●
Data cleaning, incremental data integration, and data analysis.
– Target: Distributed and Parallel Databases Journal.
●
Invitation for selected papers from DMAH’17.
●
Deadline: March 31st.

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3) Architecture to Realize the
Network Services at the Edge
●
Can the migrations between the third-party service
providers be seamless?
– An overlay network for anyone to join and offer services?
– Architectural alternatives such as Blockchain solutions.
– Target: CoNEXT (Tentative, June).

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The road ahead (2018)..
●
On-going journal and conferenec submissions.
– January – June 2018.
●
CAT, February 2018.
●
Thesis Defence, December 2018 or after.
Thank you!
pradeeban.kathiravelu@tecnico.ulisboa.pt

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~ Thanks ~

41. Additional Slides

42. What has Message-Oriented
Middleware got to do with the controller?
●
Expose the internals from controller (e.g. OpenDaylight)
– Through a message (e.g. AMQP as northbound) API
– Publish/Subscribe with a broker (e.g. ActiveMQ).
●
What can be exposed
– Data tree (internal data structures of the controller)
– Remote procedure calls (RPCs)
– Notifications.
●
Thanks to Model-Driven Service Abstraction Layer (MD-SAL) of
OpenDaylight.
– Compatible internal representation of data plane.

43. MILP and Graph Matching can be
computation intensive
●
But initialization is once per user service chain with a
given policy.
– This procedure does not repeat once initialized.
– unless updates received from the edge network.
●
New data center with the service offering at the edge.
●
An existing data center or a service offering fails to respond.

44. Algorithm

45. Algorithm

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User policies with two attributes
●
Location of the circles → Properties (C, L, and T).
●
Darker circles – chains with minimal penalty, the ones
that we prefer.
T ↑ and C ↓ T ↑ and L ↓ C ↓ and L ↓
●
Results show user policies supported fairly well.

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●
Policies with three attributes: One given more prominence
(weight = 10), than the other two (weight = 3).
Radius of the circles –
Monthly Cost
●
Results show efficient
support for multiple
attributes with different
weights.

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●
Two attributes given more prominence (weight = 10),
than the third (weight = 3).
Radius of the circles –
Monthly Cost
●
Results show efficient
support for multiple
attributes with different
weights.