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Elastic Computing and Engineering
Elastic Applications in the CloudSummer SOC, Crete, 2 July 2013
Schahram Dustdar and Hong-Linh Truong
Distributed Systems Group
TU Vienna
http://dsg.tuwien.ac.at/research/viecom/
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Acknowledgements
NOTE: The content includes some ongoing work
Includes some joint work with Kamal Bhattacharya, Muhammad Z.C. Candra,
Georgiana Copil, Daniel Moldovan, Mirela Riveri, Ognjen Scekic
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Outline
Part 1: Elastic Computing
Motivation for multi-dimensional elasticity
Quality/cost/benefits analytics
HBS cloud concepts
Conclusions
Part 2: Engineering Elastic Applications in the Cloud
Programming hybrid services for solving (in)dependent tasks
Programming incentives
Controlling and monitoring elasticity
Conclusions
Part 3: Demonstration of elasticity control and monitoring
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PART 1 – ELASTIC COMPUTING
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“Industrie 4.0” – German industrial CPS
VDE Dialog 2/2013, S.15
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Industrial Internet
Industrial Internet –
Pushing the Boundaries of Minds and Machines, Nov. 2012
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1. “Resources” provided as services
2. Illusion of infinite resources
3. Usage-based pricing model ->
New and connected business models
Cloud Computing?
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Overprovisioning – Provider perspective
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Underprovisioning – Provider perspective
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Elastic Provisioning
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eHealth &
Smart Health
networksGame Machine
Telephone
PC
DVD
Audio
TV
STBDVC
Smart
Homes
Smart eGovernments &
eAdministrationsSmart Energy
Networks
Smart Evolution – People, Services,Things
Elastic Systems
Smart Transport
Networks
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The goal: Elastic System
Event Analyzer on
PaaS
Peak Operation
Other stakeholders
...
events stream
Normal Operation
Human Analysts
Peak OperationNormal Operation
Machine/Human
Event Analyzers
Crtical
situation 1
Experts
SCU
(Big) Data analytics
Wf. A
Wf. B
Critical
situation 2
Cloud DaaS
Data analytics
M2M PaaS
Cloud IaaS
Operation
problem
Maintenance
process
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A New Perspective is Required
“The older is not always a reliable model for the newer, the smaller for the larger, or the simpler for the more complex….
Making something greater than any existing thing necessarily involves going beyond experience.”
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Marine Ecosystem: http://www.xbordercurrents.co.uk/wildlife/marine-ecosystem-2
Think Ecosystems:
People, Services, Things
Diverse users with complex networked dependencies and intrinsic adaptive behavior – has:
1. Robustness mechanisms: achieving stability in the presence of disruption
2. Measures of health: diversity, population trends, other key indicators
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Evolution of
Large-Scale &
Collective
Problem solving
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Autonomic
Nervous
System
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Computing Models
Machine-based
Computing
Human-based
Computing
Things-based
computing
Grid
Pro
cessin
g
Unit
Arc
hitectu
reC
om
m.
SMP
S. Dustdar, H. Truong, “Virtualizing Software and Humans
for Elastic Processes in Multiple Clouds – a Service
Management Perspective”, in International Journal of Next
Generation Computing, 2012
Ad hoc networks Web of things
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Co-Evolution
Wealth
Collabo-ration
Communication
Impact on
Society &
Institutions
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Institutions
vs.
Collaboration
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Example: Quality evaluation in scientific
multi-scale simulation
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Context, Structure, and Dynamics
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Humans as a service
Elastic Computing – conceptualizing HBS
Clouds
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IT Service Delivery Factory
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Tacit work
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Problem statement
• Within the scope of transactional work, BPM is not suitable to structure the type of work
that is required to be done
• The variability of underlying systems and potential actions to be taken further prevent a
structured BPM-type approach
Area’s in red circles indicate work that requires
insights into:
(a) the system,
(b) how others have resolved issues
(c) how defects could be prevented at a larger
scale
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Virtualizing individual and team capabilities
Individual capabilities and team capabilities can
be virtualized using the service model
Individual Compute Unit (ICU) for individuals
Social Compute Unit (SCU) for teams
Apply service unit models for ICU/SCU to
support seamless integration between human-
based computing elements and software-based
computing elements
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Humans in a programming paradigm
VolunteersProfessionalsTeamIndividual
Service-based Middleware
CommunicationMonitoringCapability/Profile
Management
Provisioning/Negotiation/Execution API
Abstraction of Human-based Compute Units
SCUICUICUICU SCU
Program languages and programming models
Program
elements
Software
Compute
Units
• program human actions
and dependencies
• program incentive
condition and rewarding
action
• program result
evaluation method
Human-to-middleware
interfaces:
•visualization of collective tasks
•embedding of common forms
•mobile app
Hong-Linh Truong, Schahram
Dustdar, Kamal Bhattacharya
"Programming Hybrid Services
in the Cloud", 10th International
Conference on Service-oriented
Computing (ICSOC 2012),
November 12-16, 2012, Shanghai,
China. Best Paper Award.
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IncentivesStimulate (motivate) or discourage certain worker activities before the actual execution of those activities.
RewardsAny kind of compensation for services rendered or retribution for wrongdoing exerted upon workers after the completion of activity.
Incentive MechanismA set of rules for assigning rewards and applying incentives.
Incentives and Rewards
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Incentives for humans
We need incentive
programming features
Incentive strategies
being composed from
general components
Integrated into human-
based service (HBS)
clouds
Ognjen Scekic, Hong-Linh Truong, and Schahram
Dustdar. 2013. Incentives and rewarding in social
computing. Commun. ACM 56, 6 (June 2013), 72-82.
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Modeling Incentives
We identified 7 basic
incentive mechanisms in
use today and their
constituent elements.
New mechanisms can be
built by composing and
customizing well-known
incentive elements.
Portable, reusable,
scalable
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Incorporate humans into a
programming paradigm
• Abstracting human compute units as program elements
• Extending programming languages to support human compute units
• Data/control flows via extensible APIs
Programming languages
• Shared memory (e.g., human –software – human), message passing (human-to-human), artifact-centric, etc., via APIs working atop the compute unit abstraction layer
Multiple programming
models
• Computing capability /profile management: human computing power, reputation and incentive models
• Monitoring and enforcing incentives/rewards, quality of results, availability
• Communication between human-middleware, among Individual Compute Units (ICU)/Social Compute Units (SCU) for exchanging artifacts and comprehensing l tasks
Execution environment
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Cloud of hybrid service units
Cloud of HBS: A cloud of HBS includes HBS that can be provisioned, deployed, and utilized on-demand based on different pricing and incentive models.
Cloud of hybrid services: A cloud of hybrid services includes SBS and HBS that can be provisioned, deployed and utilized on-demand based on different pricing andincentive models.
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HBS Communication Interface
Based on well known technologies for integrating
human requests/responses into SBS
Emails, web interfaces, SMS, MOM Web services, etc.
Can support message passing and shared memory
models
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Human Power Unit (HPU)
Humans are determined via skills/skill levels
A particular HBS cloud can define its own sets of
skills (CS) and skill levels (SK)
Similar to Amazon defining its own EC unit
Different ways to make sure skill and skill levels
declared in a cloud are consistent (e.g., via testing and
monitoring).
HPU Definition: HPU is a value describing the computing power of an HBS measured in an abstract unit. A cloud of HBS has a pre-defined basic power unit, hpuθ, corresponding to the baseline skill bsθ of the cloud.
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Human Power Unit (HPU) – our starting point
Given an HBS, its theoretical HPU can be larger or
smaller than its real HPU – unlike SBS
HPU for a particular (skill,
skill level)
HPU for a set of (skill, skill
level), no weighted factors
Decomposition/composi-
tion of HPU for a shared
HBS
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Human Services - Conclusions (1)
Elastic computing is needed in different aspects
Scaling software, services, and people in the same
application
Not just “resource elasticity”
Resource, costs/benefits and quality
Hybrid systems of software, humans and things
Existing quality and cost/benefits monitoring and
analytics need to be extended for hybrid service
units
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Human Services - Conclusions (2)
Several open research questions for realizing elastic
processes of hybrid computing elements
Novel models and APIs are needed for integrating
humans into program paradigms
Novel concepts for clouds of human-based services
Techniques for combining human-based services with
software-based services
Programming elements/constructs/patterns for hybrid
services
Hybrid service life-cycle management
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What do we know about “elasticity”?
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Elasticity in physics
It is related to the form (the structure) of something
“Stress” causes the elasticity (structure deformation)
“Strain” measures what has been changed (amount of deformation)
In the context of computing: given a process or a system
What can be used to represent “Stress” and “Strain”?
When does a “strain” signals a “dangerous situation”?
How to be elastic under dynamic “stress”?
“elasticity (or stretchiness) is the physical property of a material that returns to
its original shape after the stress (e.g. external forces) that made it deform
or distort is removed” – http://en.wikipedia.org/wiki/Elasticity_(physics)
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Elasticity in economics
price elasticity of demand
price elasticity of supply
income elasticity of demand
elasticity of substitution
between factors of production
elasticity of inter-temporal
substitution
“elasticity is the measurement of how changing one economic variable
affects others” – http://en.wikipedia.org/wiki/Elasticity_(economics)
Elasticity of a function:
elasticity of y with respect to x
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Elasticity in computing (1)
“Elastic computing is the use of computer resources which
vary dynamically to meet a variable workload” –http://en.wikipedia.org/wiki/Elastic_computing
“What elasticity means to cloud users is that they should
design their applications to scale their resource
requirements up and down whenever possible.“, David
Chiu – http://xrds.acm.org/article.cfm?aid=1734162
“Clustering elasticity is the ease of adding or removing
nodes from the distributed data store” –http://en.wikipedia.org/wiki/Elasticity_(data_store)
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Elasticity ≠ Scaleability
Resource elasticity Software / human-based
computing elements,
multiple clouds
Quality elasticityNon-functional parameters e.g.,
performance, quality of data,
service availability, human
trust
Costs & Benefit
elasticityrewards, incentives
Elasticity
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The Vienna Elastic Computing Model
Multi-dimensional
Elasticity
Service computing
models
Cloud provisioning
models
Schahram Dustdar, Hong Linh Truong: Virtualizing Software and Humans for Elastic Processes in Multiple Clouds- a Service
Management Perspective. IJNGC 3(2) (2012)
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Elasticity in computing – broad view
1. Demand elasticity
Elastic demands from consumers
2. Output elasticity
Multiple outputs with different price and quality
3. Input elasticity
Elastic data inputs, e.g., deal with opportunistic data
4. Elastic pricing and quality models associated
resources
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Diverse types of elasticity requirements
Application user: “If the cost is greater than 800 Euro, there should be
a scale-in action for keeping costs in acceptable limits”
Software provider: “Response time should be less than amount X
varying with the number of users.”
Developer: “The result from the data analytics algorithm must reach a
certain data accuracy under a cost constraint. I don’t care about how
many resources should be used for executing this code.”
Cloud provider: “When availability is higher than 99% for a period of
time, and the cost is the same as for availability 80%, the cost should
increase with 10%.”
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Vienna Elastic Computing Model
Multi-dimensional elasticity
Resource, quality, cost and rights
Please see “Principles of elastic processes” paper
Elasticity in hybrid systems of human-based and
software-based computing resources
Software and human capabilities as computing
resources
Multi clouds
End-to-end approach
For science and business complex applications
dsg.tuwien.ac.at/research/viecom
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Things
as a service
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Some 50 billion devices and sensors exist for M2M applications
IoT and Cloud Computing enable smart services ecosystem and
collaboration opportunities
Managed services
• Portfolio
management
• Event management
• Analytics
Provisioning• Services
• SIM profile
configuration
• Network
configuration
Controls• Activation
• Deactivation
• Privacy
• Security
Transaction Mgmt.• Visibility
• Billing
• Reporting
Integration
framework
Algorithm
engineChart
builder
Predictiv
e
modeling
Incidents
manager
Expert rule
engine
FDD Service
Mgmt
Storage
policies
Databas
e
mangerOperations
manager
Portfolio
Mgmt Analyic
s
engine
Blackbo
x
module
Location
awarenes
s
GUI
builde
r
Event
mgmt
Data
mining
Resource
mgmt.
Regressio
n engine
Open
integratio
n platform
Resource
manager
Point
metering
framewor
kNumerous Forms Of Smart Services…
Access
control
Environment
Compliance
Street Light
ManagementFood Transfer
Process
Public
Safety
Industrial
process
parameter
s
Parking
ControlWaste
Management
Facilities
ControlHealthCare
Power
Quality ControlLighting
Control
KIOSK
Monitoring
CCTV
Monitoring
Hospitality Sector Healthcare Sector
Education SectorTransport Sector
Datacenters
Government Sector
Industrial SectorFinance Sector
Utilities and Smart Grid
Airports, ports and
Critical Infrastructure
Ubiquitous Managed Services Solution Across Business Verticals
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Vertical IoT solutions today
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Service delivery platform for
Smart Cities
Smart Vertical solutions tomorrow?
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Emergency Hub
Command Control Center
Vehicle tracking
system
Logistics
Management
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ICT for energy
savings in buildings
Command Control Center
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Command Control Center for Managed
Services
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HVAC (Heating, Ventilation, Air Conditioning) Ecosystem
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Water Ecosystem
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Air Ecosystem
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Monitoring
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Chiller Plant Analysis Tool
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Measurement &
Verification
Continuous
Commissioning
Carbon Footprint Analysis
& GHG Accounting
Fault Detection
& Diagnostics
Asset Performance
Management
Energy Analysis
Maintenance
Management
Alarm Management
Dashboards &
Reports
Boilers
UPS Pumps Generators
HVTS
Signage
ATMs
Chillers
Database Enterprise Application
Smoke
Detector
Occupancy
Sensor
Humidity
Sensor
Pressure
Sensor
Power
Meter
KW
Meter
Temperature
Sensor
Cameras
Vehicle
Tracking
Device
PresentationIntegration
End Users
Facilities
Management
Vehicle Tracking
System
Fire Alarms
ICT Network
OEM
Third Party Application
Developers
Service
Providers
SIM
FAHU
AHU
KW
Meter
Flow
Meter
Security System
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DATA CENTER
VERTICAL
RETAIL
VERTICAL
LIFE & SAFETY
VERTICAL
SECURITY
& SURVEILLANCE
HOTELS
VERTICAL
INDUSTRIAL
VERTICAL
HEALTH
VERTICAL
BUILDINGS
EDUCATIONAL
VERTICAL
ENERGY
VERTICAL
TRANSPORT
VERTICAL
AIRPORT
VERTICAL
Remote Service Maintainence
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Elasticity in smart environments
1. Elastic human demands: utilizing human-based
services with Social Compute Units
2. Output elasticity: using multi-scale analytics + data-as-
a-service as elastic resources
3. Elastic data inputs for urban mobility and energy
systems: using data-as-a-service as elastic data
resources
4. Elastic pricing and quality models: utilizing bots and
cloud analytics
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Thanks for your attention!
Prof. Dr. Schahram Dustdar
Distributed Systems GroupTU Wien
dsg.tuwien.ac.at