smartsociety – a platform for collaborative people-machine computation
TRANSCRIPT
SmartSociety – A Platform for
Collaborative People-Machine
Computation
Hong-Linh Truong
withOgnjen Scekic, Daniele Miorandi, Tommaso Schiavinotto,
Dimitrios I. Diochnos, Alethia Hume, Ronald Chenu-Abente, Michael Rovatsos, Iacopo Carreras, Schahram Dustdar, Fausto
Giunchiglia
Distributed Systems Group, TU Wien
http://dsg.tuwien.ac.at/staff/truong/
8th IEEE SOCA, 20 Oct, 2015, Rome, Italy 1
Outline
Background
Motivation
The SmartSociety service platform
Prototype and Examples
Conclusions and Future work
8th IEEE SOCA, 20 Oct, 2015, Rome, Italy 2
Background –
Computing Elements
Machine-based
Computing
Human-based
Computing
Things-based
computing
Grid
Pro
cessin
g
Unit
Pro
cessin
g
Unit
Arc
hitectu
reA
rchitectu
reC
om
m.
Com
m.
SMP
Ad hoc networks Web of things
38th IEEE SOCA, 20 Oct, 2015, Rome, Italy
Human and machine for solving
complex problems
Collaborative working environments
provide a lot of services but it is up to the user to
use them (e.g., in FP6 projects)
Crowdsourcing
try to “program”/harness human capabilities to solve
problems that machines/software cannot do
Cognitive computing
(want fully automatically to) replace human work by
machines/software, e.g., IBM Watson
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We want machines/humans to coexist – have a fair sharing job
distribution
We want machines/humans to coexist – have a fair sharing job
distribution
Our approach: Collective Adaptive
Systems and Hybridity
Hybridity
People AND software complement each other
Collectiveness
Socio-technical systems/social machines
In future also ‘things’ (sensors, actuators)
Collectives as the first class entity
Adaptiveness
Respond/adapt to ad-hoc situations
Favor collaboration patterns instead of predefined workflows
Human-driven adaptation
Embrace uncertainty
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Motivation (1)
Users/Customers:
need complex collaborative human-machine tasks
(e.g., in predictive maintenance and complex data
analytics)
Peers
software or human services, individually or
collectively providing solutions for solving complex
problems
Human-machine intelligences but “collaboration” is
the main aspect to be supported
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Motivation (2)
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How to support users with task execution and workforce management
functionality?
How to allow peers to collaborate with a fair working condition?
Enable collaboration and adaptiveness
Not purely crowdsourcing(e.g., workflows of human tasks) or
cognitive algorithms (replace human work by machines)
peers & collectives
User-specfic business logic
(arbitrary code)
get peers and plans
Composition
Manager
collective-based task
negotiate a planNegotiator
execute a plan & monitor
ExecutorCommunication
aggregate resultsUser-specfic
arbitrary code
etc.
( . . Peer Manager )
. .
Incentive Manager
)
User ApplicationTask Request
peers & collectives
Application
Motivation (3): Complex services and
interactions
8th IEEE SOCA, 20 Oct, 2015, Rome, Italy
AP
IsA
PIs
8
SmartSociety Platform - core
functions
Enabling platform services
Collectives Management
Information and Collective lifecycle management
Communication
Composition and Execution
Monitoring, Incentive Management, and Provenance
Supporting diverse types (and models) of
applications to be developed
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Not a tightly coupled integrated system for a single purpose
enable the development and programming of applications
Not a tightly coupled integrated system for a single purpose
enable the development and programming of applications
SmartSociety Platform - Key
service engineering issues
System scalability and extensibility
Decouple functions with REST-based open APIs
Loose couple and scalable communication among
services and humans
Asynchronous interactions for humans
Extensive connectors/plug-ins
Human-machine diversity and hybridity
Need many algorithms and protocols and interfaces
Software platforms enable interactions among
people and software services (machines)
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SmartSociety Platform Architecture
crowd of human andmachine peers
privacy model
virtualization & communication
orchestration
collective formation
virtualization & communication
runtime control
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Peer Abstraction & Management
Maintaining information about humans, software
services and their collectives
Utilizing the entity-centric semantic enhanced model
Incorporating several search algorithms from the
Information Retrieval domain
Trading off privacy: disclosing private data needed for
decision making vs. restricted functionality
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Communication Middleware
Virtualization — supporting heterogeneous human and machine
service units as uniformly addressable entities; Supporting ‘collective’ as
a first-class, dynamically-defined entity.
Heterogeneity — supporting various types of communications
channels/protocols/formats between the platform and service units as well
as among service units/collectives, transparently to the platform user.
Communication — providing functionalities of message
transformation, routing, delivery with configurable delivery and privacy
options
Persistence — message persistence and querying.
Scalability — ability to handle large number of intermittently available
service units.
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SMARTCOM Middleware
8th IEEE SOCA, 20 Oct, 2015, Rome, Italy
Philipp Zeppezauer, Ognjen Scekic, Hong Linh Truong, Schahram Dustdar:
Virtualizing Communication for Hybrid and Diversity-Aware Collective Adaptive Systems. ICSOC Workshops 2014: 56-67
Philipp Zeppezauer, Ognjen Scekic, Hong Linh Truong, Schahram Dustdar:
Virtualizing Communication for Hybrid and Diversity-Aware Collective Adaptive Systems. ICSOC Workshops 2014: 56-67
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Orchestration Manager
Composition
Runtime, various different algorithms: Agent-based,
Ant Colony, FCFS, Greedy
Based on different criteria for different types of tasks
Negotiation
Guided by different protocols but driven by humans
Execution
self-enacted by human peers or orchestrated by
software peer(s)
• monitored for constraint and QoR satisfaction
• elastic management of collectives at runtime
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SmartSociety - current implementation
Peer management: PostgreSSQL and NodeJS
Communication
MongoDB for persisting messages, Apache ActiveMQ for
message queuing
Adapters for email, Dropbox, REST, Facebook, etc.
Orchestration: several algorithms, NodeJS
Provenance and Incentive: Python, Django
REST services with cloud deployment using VMs and
Docker.
But not everything has been integrated
8th IEEE SOCA, 20 Oct, 2015, Rome, Italy
https://gitlab.com/smartsociety/
http://www.smart-society-project.eu/publications/deliverables/
16
SmartShare
17
• Open call: generate rich plans
• Services: recommendation, reputation
and orchestration negotiation
8th IEEE SOCA, 20 Oct, 2015, Rome, Italy
• Experiments: Israel, involving
150 students and staff of the
Ben Gurion University.
• Plan for Italy scenarios
AskSmartSociety!
Machine peers:
Answering collectives from Google and Twitter
Human peers
Provide answers or filter results
AskSmartSociety! is able to actively (on-demand) locate
and engage those individuals or software services
provide answers of required quality.
Quality criteria determine the composition of the
collective, i.e., different service matching and ranking
Machine-centric versus human-centric
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https://www.youtube.com/watch?v=Jr9z2Coqc6M
Performance - Communication
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Windows 7
64-bit, Intel Core2 Duo @ 2.53 GHz, 4.00 GB RAM.
Conclusions and Future work
• Hybrid collective adaptive systems
• Need scalable service platforms to enable the development and
execution of human-machine collaborative complex tasks
• The SmartSociety platform
• Provide various services, protocols and algorithms to enable the
development of human-machine collaborative tasks
• But an open system requires an continuous design and
engineering plan
• Future work
• Orchestration and execution management algorithms and
external services integration
• Platform evaluation in large-scale settings
• Programming models
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Thanks for your attention!
Hong-Linh Truong
Distributed Systems GroupTU Wien
dsg.tuwien.ac.at/staff/truong
218th IEEE SOCA, 20 Oct, 2015, Rome, Italy