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Modeling Based Engineering for Safe and Sustainable Body Area Network and Data Centers. CSE 591 Green Computing Course. Models. Model is an abstract representation of a selected part of the system Models of phenomenon – fluid flow models Models of data – regression models

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modeling based engineering for safe and sustainable body area network and data centers

Modeling Based Engineering for Safe and Sustainable Body Area Network and Data Centers

CSE 591 Green Computing Course

models
Models
  • Model is an abstract representation of a selected part of the system
    • Models of phenomenon – fluid flow models
    • Models of data – regression models
  • Model can represent an entire theory with theorems and laws.
    • Newton’s model for gravitation
    • Bohr’s atomic model
  • We concentrate on the first type of models.
types of models
Types of Models
  • Architectural model - the primary aim is to illustrate a specific set of tradeoffs inherent in the structure and design of a system or ecosystem. 
  • Behavioral model – models the interaction of the different components of a system
    • Control flow – Algorithmic view of the operation of a system
    • Data flow – Input / output view of different components along with the data flow paths.
    • State machines – Event based execution of a system
background on model based verification analysis
Background on Model based Verification/Analysis
  • Model based analysis normally used to verify critical systems such as avionics.
    • no need for actual scenario generation putting lives/property at risk.
  • Formal models for abstraction of the system behavior.
  • Expected system properties depend on the requirements.
  • Formal models analyzed through model checking to verify the system properties.

System Behavior

System Requirements

Formal Models

Expected Properties

Model

Checking

Property Verification

Requirement Verification

body area networks bans

EEG

Body Area Networks (BANs)

EKG

BP

SpO2

Base

Station

Wearable Sensor Nodes

Base Station

Thermal Map of

Human Body

Heating effects (Unintended interactions)

  • Issues:
  • Thermal safety – keeping human body temperature within safe limits
  • Sustainability – un-interrupted operation with energy scavenging

Motion

Sensor

Communication Range

Aggregate Effects

Communication Range (Intended Interactions)

Body Sensor Network (BSN)

model based communication in ban
Model Based Communication in BAN
  • Use generative models for data
    • A light version in the sensor
    • A full version in the base station
  • Low communication overhead
  • Low storage requirements
  • Ensure required accuracy for clinically relevant data
data centers
Data Centers

Heat Recirculation (Aggregate Effect)

  • Computing Units –
  • Server racks arranged in rows
  • CRAC unit supplies cold air from underneath the floor
  • Cold Ailse near server inlets
  • Hot aisle at the outlets

Data Center

Hot Air coming out of chassis (Unintended Interaction)

  • Issues:
  • Thermal Safety – schedule tasks into servers so that their inlet temperatures do not exceed manufacture specified redline temperature
  • Sustainability – Energy efficiency, Heat activated cooling

Racks

Cold Aisle

CRAC

Cool Air coming from CRAC (Intended Interaction)

Hot Aisle

Raised Floor

  • Interactions –
  • Intended – CRAC cold air cooling off racks
  • Unintended – re-circulated heat causing hot spots

Ayan Banerjee, Tridib Mukherjee, Georgios Varsamopoulos, and Sandeep K. S. Gupta Integrating Cooling Awareness with Thermal Aware Workload Placement for HPC Data Centers , Elsevier Comnets Special Issue in Sustainable Computing (SUSCOM) 2011 (Accepted for publication).

slide10

Cyber-Physical Systems

  • A cyber-physical system is a system which has a computing units embedded in a physical environment
  • The computing unit is constantly interacting with its environment in two ways –
    • Intentionally – for execution of system operations
    • Unintentionally – through side effects of its operation
  • Interactions may have aggregate effects during networked operation of the CPS

Computing node

Space in physical

environment interacted

by single node

Aggregate impact in space

because of interactions

from multiple nodes

Cyber-physical interactions

Cyber-Physical System (CPS)

system requirements
System Requirements
  • Safety – Safety of any system is defined as ensuring the impact of the interactions is within desirable limits.
    • E.g. - keeping the temperature of the servers within redline
  • Sustainability - Sustainability is defined as the ability of the CPS to operate by scavenging energy from the environment.
    • In a BSN the sensor nodes operate by scavenging energy from human body
cps modeling perspective
CPS Modeling Perspective
  • Network of Local CPSs
  • Effect of interactions are limited spatially
  • Intended Interactions – ROIn
  • Unintended Interaction – ROIm
  • Network of computing units imply a network of Local CPSs
  • Each Local CPS can affect the ROIm or ROIn of other Local CPSs
    • leads to complex aggregate effects of interactions

A. Banerjee, S. Kandula, T. Mukherjee, and S.K.S. Gupta BAND-AiDe: A Tool for Cyber-Physical Oriented Analysis and Design of Body Area Networks and Devices , ACM Transactions in Embedded Computing Systems, Special Issue on Wireless Health 2010, Accepted for publication

example scenario bsn thermal safety
Example Scenario BSN Thermal Safety

Computing Unit – Atom based Sensor node running health monitoring workload

Physical Unit – Human body

Interaction – Heat dissipation due to computation causes temperature rise at different parts of the human body.

The thermal effect of a sensor is governed by Penne’s bioheat equation

Sensors close to each other have aggregate effect on the skin temperature – the heat accumulated gets summed up

Heat by

metabolism

Heat by power

dissipation

Heat

accumulated

Heat transfer

by convection

Heat transfer

by conduction

Heat by

radiation

mapping to cps modeling perspective
Mapping to CPS modeling perspective

Human Body

Thermal Effects

Sensors

GCPS

LCPS2

LCPS1

Aggregate effects

Computing Unit

Physical Unit

Governed by Penne’s Equation

ROIm

aadl implementation
AADL Implementation
  • Industry standard Advanced Architecture Description Language
    • Pros -
    • Used in the embedded industry and can model complex systems such as aircrafts
    • Specific constructs for modeling the embedded computing devices
    • Hierarchical model specification – matches with the CPS view
    • Cons –
    • No support for modeling the physical system
    • Cannot represent dynamic variations of physical properties in terms of differential equations in AADL
ban model in aadl
BAN Model in AADL

system BAN

. . .

end BAN;

processimplementation application

subcomponents

algorithm: thread algorithm.imp1;

end application;

systemimplementation BAN.ins1

subcomponents

Sensor1: system CompUnit.Sensor1;

EnergySource: system EnergySource.impl;

Body system PhysicalUnit.skin;

. . .

connections

connection between subcomponents

end BAN.ins1;

threadimplementation algorithm.imp1

modes

. . .

properties

. . .

end algorithm.imp1;

system implementation EnergySource.impl

. . .

end EnergySource.impl;

system CompUnit

features

port specification for connections

properties

Computing Properties

Physical Properties

end CompUnit;

system PhysicalUnit

features

port specification for information transfer

properties

Physical properties

end PhysicalUnit;

system implementation CompUnit.Sensori

subcomponents

P1: process application;

C1: system subcomponents;

connections

inter-connections between the subcomponents

end CompUnit.Sensori;

system implementation PhysicalUnit.Skin

Specify physical dynamics with the help of

annexes

end PhysicalUnit.Skin;

modeling in aadl computing units
Modeling in AADL – Computing Units

system Computing

subcomponents

P1: process SignalProcApp.impl;

C1: system Radio.impl;

end Computing;

  • Computing Units – Embedded System Constructs
    • system – sensors nodes in BAN
    • subcomponents – sensor components (e.g. radio, processor, display device etc.)
    • threads – application specific processes (e.g. FFT computation for signal processing applications
    • property sets
      • computing properties (e.g. operating frequency of processor)
      • physical properties (e.g. power dissipation of subcomponents or threads)

system implementation Radio.impl

properties

ComputingProperty::current => 18 mA;

end Radio.impl

processimplementation SignalProcApp.impl

subcomponents

FFT: thread FFT_algorithm.imp1;

end SignalProcApp.impl;

threadimplementation FFT_algorithm.imp1

modes

RadioOn: initialmode ;

RadioOff: mode ;

properties

ComputingProperty::current => 19.56 mA

inmodes (RadioOn);

ComputingProperty::current => 1.0 mA

inmodes (RadioOff);

end FFT_algorithm.imp1;

networks of computing units
Networks of computing units

dataimplementation Comp2CompData.impl

subcomponents

SignalStrength: data behavior::float;

ParentID: data behavior::integer;

end Comp2CompData;

  • system - used for defining the network
  • subcomponent – used for modeling the individual computing units (sensor nodes)
  • port group – used for modeling connections between computing units

portgroup Comp2CompPG

features

Packet: inoutdataport Comp2CompData.impl;

end Comp2CompPG;

system CompUnit

features

C2C: port group Comp2CompPG;

end CompUnit;

systemimplementation CompUnit.Sensori

. . .

end CompUnit.Sensori;

Use of arrays required, not supported in AADL 1.0

systemimplementation BAN.ins1

subcomponents

Sensor1: system CompUnit.Sensor1;

Sensor2: system CompUnit.Sensor2;

connections

portgroup Sensor1.C2C -> Sensor2.C2CR;

. . .

end BAN.ins1;

Replicate code for each sensor – scalable ??

model to analyze sustainability
Model to analyze Sustainability

system implementation computing.sensor1

properties

ComputingProperty::Voltage=> 2.3V

end computing.sensor1;

processimplementation SignalProcApp

subcomponents

FFT: thread FFT_algorithm.imp1;

end SignalProcApp;

thread FFT_algorithm

properties

ComputeProperty::Compute_Execution_Time => 2138 ms .. 2140 ms;

ComputeProperty ::Frequency => 30 Hz;

end FFT_algorithm;

threadimplementation FFT_algorithm.imp1

modes

RadioOn: initialmode ;

RadioOff: mode ;

properties

ComputeProperty ::current => 19.56 mA inmodes (RadioOn);

ComputeProperty ::current => 1.0 mA inmodes (RadioOff);

end FFTComputation_algorithm.imp1;

system BodyHeatSource

properties

ComputeProperty ::AveragePower=> 0.26W;

end BodyHeatSource;

  • Power consumption of the sensor nodes were modeled
  • Scavenging sources were modeled for available power
  • Duty cycling was performed on the sensor nodes to sustain their operation using the available power
    • The sensor radio was turned off at appropriate times
model to analyze side effects
Model to analyze side effects

Behavior annex properties must be constant requiring

separate property set definition for each annex

Real Value initialization not supported in behavior annex

  • Model the physical processes
    • Specify differential equations
  • Extended Behavior Annex
    • Dedicated variables for parsing the differential operators
    • Developed a parser to recognize the operators
    • Developed a plug-in to convert the parsed form into solvable form
    • Used FDTD solver to solve the equations

system implementation BAN is

subcomponents

Sensor: system CompUnit.impl;

Body: system PhysicalUnit.impl

connections

port group Sensor.C2P  Body.P2C;

end BAN;

system CompUnit

features

C2P: port group CyberPhysical;

properties

Physical Property - PowerDissipation

end CompUnit;

dataimplementation Comp2PhysData.impl

subcomponents

PowerDissipation: data behavior::float;

end Comp2CompData

system implementation CompUnit.impl

end CompUnit.impl;

portgroup CyberPhysical

features

Info: inoutdataport Comp2PhysData.impl;

end Comp2CompPG;

propertyset Coefficient is

SpecificHeat: constantaadlinteger =>3600;

Fixed_blood_Temp :constantaadlinteger => 37;

. . .

end Coefficient;

system PhysicalUnit

features

P2C: port group CyberPhysical;

end PhysicalUnit

systemimplementation PhysicalUnit.impl

subcomponents

Del1Tt: data behavior::integer;

Del2Tx: data behavior::integer;

annexbehavior_specification {**

states

s0 : initialcompletestate;

transitions

s0 -[ ]-> s0 {

Del1Tt := (value(Coefficient ::SpecificHeat) * Del2Tx + value(Coefficient ::blood_perfusion_constant) * (Coefficient.T - value(Coefficient ::Fixed_blood_Temp) + PowerDissipation);};

**};

end PhysicalUnit.impl;

CPS specification using the behavior annex

to represent differential equations

Multiple data subcomponents in port groups

cannot be accessed in the behavior annex

formal modeling
Formal Modeling
  • State space representation of the problem
  • Declare appropriate states as UNSAFE
  • Perform reachability analysis on the model

Theoretical Guarantee on Safety and Sustainabiltiy

Reduces Uncertainty of Simulation

  • Issues:
  • Current modeling techniques support dynamic variation in only one dimension
  • Spatio-Temporal variation of interaction effects (ROIn and ROIm) require modeling and analysis in multiple dimensions (one time and three space).
  • Scalability of the analysis technique on multiple dimensions
    • Algorithm error increases with large number of variables
    • Present day tools do not handle large number of variables.
system
System
  • We study systems which can be represented using a finite number of states (finite state systems).
  • Definition
    • A set of states
    • Set of initial states
    • A set of inputs
    • A transition relation
    • A set of outputs
    • An output map
finite state automata
Finite State Automata
  • If H maps each state in X to an yes no answer
  • The subset of inputs U for which the automata outputs yes is called the language
  • Examples: DFA, PDA, Turing Machine
dynamical system
Dynamical System
  • A dynamical system is a pair
  • set of continuous variables
  • is a set of differential equations
  • Often the real space is divided into equivalence classes Q
  • mapping of real space to equivalent classes
  • Concept of operating modes
example
Example
  • CRAC control system
  • The outlet temperature is the variable belonging to the set V
  • It follows the heat flow equation which is a member of the function set f
  • Equivalence classes can be defined on the real space to denote different operating regions of the CRAC
  • The COP varies in different operating regions
hybrid dynamical system
Hybrid Dynamical System
  • S is a finite state system
  • In is a set of invariants for each state
    • Invariants are conditions on the continuous variables
  • Gu is the set of guard conditions for each edge
  • Re is a reset function
    • If a state x is reached then what values will the continuous variables assume ?
  • {In,f} is a dynamical system.
timed automata
Timed Automata
  • Hybrid dynamical system
  • In consists of only operators
  • Gu can also consist of
  • Re can either retain the value of the variable or set it to 0
  • f can either be 0 or 1.
formal model for cps
Formal Model for CPS
  • Requirements:
    • R1: The states in the formal model should represent both continuous and discrete domain operation
    • R2: The state variables can have continuous dynamics with respect to both time and space, represented by complex partial differential equations
    • R3: State transitions can take place through events occurring in both time and space continuum
    • R4: Composition of individual formal models to derive models of the system should reflect the aggregate behavior

Hence a variation of hybrid automata which models spatio-temporal

spatio temporal hybrid automata
Spatio-Temporal Hybrid Automata

S1

S2

  • Discrete Time Computational States
  • Discrete Physical States

Discrete States

S1

S2

  • Continuous variables related to physical phenomenon

Continuous Variables

Initial State

  • To simulate the operation of the system in time and space

S1

S2

State Transitions

Guard Conditions

Spatio-Temporal

Threshold Equations

formal modeling for safety single sensor
Formal Modeling for Safety – single sensor

Single sensor node and its associated thermal effect

  • Notion of state is in space and time –
  • I1 is the state representing space in ROIm
  • N1 is the state representing space not in ROIm
  • UNSAFE state
  • Eq1 and Eq2 are the partial differential equations representing temperature rise in human body
  • State transitions occur due to events generated in space and time –
  • As we move through space if T1 < Tth a transition occurs from state I1 to N1
  • In time also if T1 < Tth a transition occurs from state I1 to N1
  • In any time at any particular state if T1 > Tsafe we go to unsafe state
single sensor thermal profile
Single sensor thermal profile
  • Thermal profile over time and space for a single sensor
composition of models
Composition of models
  • Given individual models how to determine the model of the system

Cartesian Product

State Space

Set of Continuous Variables

Union

Union including new functions to specify aggregate effects

S1

S2

S11

S21

Set of Functions

S12

S22

Union

S1

S2

Transitions

Retain old ones. If two models change state simultaneously then combine guard conditions using and operation

Guard Conditions

thermal safety example model composition
Thermal Safety Example – model composition

Unsafe

T1 > Tsafe

T2 > Tsafe

Multiple sensor nodes and their aggregate thermal effect

T2 > Tth

Agg > Tsafe

T1 > Tth

I1 I2

I1N2

N1I2

  • States are Cartesian products
  • Eq3 represents aggregate effect (summation of heat)
  • Transition from I1 ,I2 to state N1 ,N2 occurs due to a combination of events

Eq1,Eq2

Eq3 = f(Eq1 , Eq2 )

Eq1,Eq2

T2 < Tth

T1 < Tth

T2 > Tth

∩ T1 > Tth

T2 > Tth

T2 < Tth

∩ T1 < Tth

T1 > Tth

T2 < Tth

N1 N2

T1 < Tth

Eq1,Eq2

stha analysis
STHA Analysis
  • Requirements
    • System dynamics in both space and time has to be analyzed
    • Solving multi dimensional partial differential equations are required
    • Intersection of ROIm or ROIn has to be computed
    • Aggregate effects in the intersecting regions have to be computed
  • Issues
    • Tools performing reachability analysis can handle dynamics in only one dimension
    • Multidimensional analysis requires discretization in all but one dimension
    • This discretization introduces error in the analysis
    • Drastically increases the number of dynamic variables
      • Current tools cannot handle large number of variables
stha analysis procedure

U

U

U

U

U

U

U – denotes unsafe/unsustainable state

STHA Analysis Procedure

Reachability Analysis in successive time and space steps

S

State not yet reached

S3

S1

S1

S1

S1

S1

S1

S3

S3

S3

S3

S3

S2

S2

S2

S2

S2

S2

S

States that are reached

Usafe state Reachable

Halt Computation

CPS STHA modified to represent dynamics in y axis

CPS STHA

x=nΔx

x=(n-1)Δx

Space discretization along x axis

Hybrid System Reachability/Safety Analysis in continuous space

(along y axis)

x=3Δx

x=2Δx

x=Δx

x=0

Control Space

t = 0

t = Δt

t = 2Δt

t = 3Δt

t = 4Δt

Discretized Time

conclusion and future work
Conclusion and Future Work

Conclusions:

  • Spatio-Temporal Hybrid Automata for modeling CPS
  • Model composition rules to take into account the aggregate effect of cyber-physical interactions
  • Analysis algorithm for evaluating safety and sustainability of CPS
  • Application of the modeling and analysis technique to three diverse case studies
  • Implementation of the modeling and analysis technique using industry standard AADL

Future Work:

  • Apply STHA for medical device control systems
  • An accurate reachability analysis for STHA
  • Develop a STHA modeling and analysis tool
references
References
  • Frehse, G. 2005. Phaver: Algorithmic verification of hybrid systems past hytech. In HSCC. 258-273.
  • Bartocci et al, E. 2008a. Spatial Networks of Hybrid I/O Automata for Modeling Excitable Tissue. Electronic Notes in Theoretical Computer Science (ENTCS) 194, 3, 51-67.
  • Chow, T. 1978. Testing software design modeled by finite-state machines. Software Engineering, IEEE Transactions on SE-4, 3 (May), 178-187.
  • Henzinger, T. 1996. The theory of hybrid automata. Logic in Computer Science, Symposium on 0, 278.
  • Moser et al, L. E. 1990. Formal verification of safety-critical systems. Softw. Pract. Exper. 20, 9, 799-811.
  • www.aadl.info
definition stha
Definition STHA

The model M, called the interaction model of individual computing unit in a CPS is a tuple M = {Q,X, F, Init,E,G} where:

  • is a set of n + 1 discrete states.
  • is a set of m continuous variables associated with the model. These variables are functions of time and space.
  • denotes a set of spatio temporal partial differential equations for each state in Q which governs the variation of elements in X
  • is a set of initial states.
  • is a set of discrete transition relations between different discrete states in the model
  • is a set of conditions on the continuous variables associated with each edge in the model
composite model definition
Composite Model Definition

Composition of models and

will result in the model

following a model composition relation R. The relation R consists of the following clauses

  • Clause 1: The set of discrete states Qc in the composite model Mc, is the Cartesian product of the two sets Q1 and Q2. However there is only one blocking state
  • Clause 2: The set of continuous variables Xc is the union of the two sets X1 and X2.
  • Clause 3: The set of functions specifies a method to combine the functions in the individual models to determine the cumulative effects of cyber-physical interactions.
composite model definition1
Composite Model Definition
  • Clause 4: set of initial states
  • Clause 5: set of edges in Mc
  • Clause 6: Gc specifies the conditions for state transition. Gc is a union of four sets
cps annex extension
CPS Annex extension
  • Specification of partial differential equation not supported in AADL
  • CPSAnnex was developed to extend AADL with the facility to specify multi-dimensional partial and total differential equations

New Constructs

Del, Pdel for representing total and partial derivatives

Grammar