a framework to model self-adaptive computing...

A Framework to ModelSelf-Adaptive Computing Systems

Cristiana BOLCHINIMatteo CARMINATIAntonio MIELEElisa [email protected]

AHS 2013 @Politecnico di Torino - ItalyJune 25, 2013

mailto:[email protected]




M. CARMINATI - A Framework to Model Self-Adaptive Computing Systems June 25, 2013

Motivations

2

‣ Context-awareness and self-adaptiveness are growing trends in designing new computing systems

‣ But rigorous definitions and formal models are usually neglected or overlooked

‣ This becomes a limitation when the number of elements determining the context grows: the quest for a flexible and powerful support arises


Context

3

Lot of attention from SE and DB research areas

‣ context: information that can be used to characterize situation of an entity [1]

‣ context-aware system: uses context to provide relevant information and/or services to the user, where relevancy depends on the user’s task [1]

[1] A.K. Dey and G.D. Abowd - Towards a better understanding of context and context-awareness - Workshop on the What, Who, Where, When, and How of Context-Awareness, 2000


Context

4

[2] M. Baldauf, S. Dustdar, and F. Rosenberg - A survey on context-aware systems - International Journal Ad Hoc and Ubiquitous Computing (IJAHUC), 2007.

acquisition

representation

usage interpretation

reasoning

context [2]


Context

5

[2] M. Baldauf, S. Dustdar, and F. Rosenberg - A survey on context-aware systems - International Journal Ad Hoc and Ubiquitous Computing (IJAHUC), 2007.

acquisition

representation


reasoning

modeling

context [2]


Context

6

[2] M. Baldauf, S. Dustdar, and F. Rosenberg - A survey on context-aware systems - International Journal Ad Hoc and Ubiquitous Computing (IJAHUC), 2007. [3] B. Schilit, N. Adams, and R. Want - Context-aware computing applications - Workshop on Mobile Computing Systems and Applications (HotMobile), 1994.

[3] context [2]

acquisition


reasoning

user

physical

computing

representationmodeling


Context

7

[2] M. Baldauf, S. Dustdar, and F. Rosenberg - A survey on context-aware systems - International Journal Ad Hoc and Ubiquitous Computing (IJAHUC), 2007. [3] B. Schilit, N. Adams, and R. Want - Context-aware computing applications - Workshop on Mobile Computing Systems and Applications (HotMobile), 1994.

[3] context [2]

acquisition


reasoning

user

physical

computing

representationmodeling


Self-Adaptiveness[4]

8

[4] T. Becker, A. Agne, P. R. Lewis, R. Bahsoon, F. Faniyi, L. Esterle, A. Keller, A. Chandra, A. R. Jensenius, and S. C. Stilkerich - EPiCS: Engineering Proprioception in Computing Systems - International Conference on Computational Science and Engineering (CSE), 2012.

Sensor

State & ContextPrivate

Self-AwarenessEngine

LearntModel(s)

PublicSelf-Awareness

Engine

Self-ExpressionEngine

Monitor/Controller

GoalsValues

ObjectivesConstraints

Sensor

Sensor

Actuator

Actuator

Environment

ExternalActions



9

Sensor



LearntModel(s)


Engine


Monitor/Controller

GoalsValues


Sensor

Sensor

Actuator

Actuator

Environment

ExternalActions




10


Sensor



LearntModel(s)


Engine


Monitor/Controller

GoalsValues


Sensor

Sensor

Actuator

Actuator

Environment

ExternalActions



11


Sensor



LearntModel(s)


Engine


Monitor/Controller

GoalsValues


Sensor

Sensor

Actuator

Actuator

Environment

ExternalActions



12


Sensor



LearntModel(s)


Engine


Monitor/Controller

GoalsValues


Sensor

Sensor

Actuator

Actuator

Environment

ExternalActions



13


Sensor



LearntModel(s)


Engine


Monitor/Controller

GoalsValues


Sensor

Sensor

Actuator

Actuator

Environment

ExternalActions


Work Goals

14

‣ The definition of a model for self-adaptive Computing Systems to express:

‣ the elements affecting their behavior, including existing relations and constraints

‣ the conditions that trigger adaptation

‣ The validation of the completeness and flexibility of the model, by applying it to self-adaptive systems from literature


What is relevant?

15

Self-AdaptiveComputing Systems

(SACS)


What is relevant?

16

elements

ODA control loop

O: high-level quantitiesD: aspects to reason onA: knobs and strategies


(SACS)


What is relevant?

17

elements relations

ODA control loop

O: high-level quantitiesD: aspects to reason onA: knobs and strategies

Both direct and indirect effects of planned actions


(SACS)


Context Dimensions

18

MainDimensions

goals

requirements

observations


Context Dimensions

19

MainDimensions

goals

requirements

observations

Collected DataDimensions

raw data

measures

metrics


Context Dimensions

20

MainDimensions

goals

requirements

observations

Collected DataDimensions

raw data

measures

metrics

Self-ExpressionDimensions

controlactions

methods


observations

measuresmetrics raw datacontrol actions methods requirements

goals

Context Meta-Model

21

drivingdimensions


observations


goals

Context Meta-Model

22

drivingdimensions

may not existin some contexts


observations


goals

Context Meta-Model

23

drivingdimensions


segments representrelations between

dimensions


observations


goals

Context Meta-Model

24

drivingdimensions


segments representrelations between

dimensionsOR relation AND relation


observations


goals

Context Meta-Model

25

drivingdimensions


OR relation AND relationsegments representrelations between

dimensions

New dimensions can be added by simply connecting them to the existing ones through (possibly new) relations


Context Model

26

control actions methods metrics measures raw data goals/requirements/observations✦ #Allocated Cores✦ Off-Chip Memory Bandwidth✦ Shared Cache Space✦ Idle Cycle Injection✦ Core Frequency✦ . . .

✦ SIMO controller & ARMA model✦ Controlled Task Scheduling✦ Round Robin✦ FIFO✦ Constant Value✦ . . .

✦ Performance✦ Resource Exploitation✦ Power✦ Temperature✦ Reliability✦ Area✦ Manufacturability✦ . . .

✦ Weighted IPC✦ Harmonic Speed-Up✦ Resources Utilization✦ Mean Time To Failure✦ . . .

✦ Instructions per Cycle (IPC)✦ Average Resource Usage✦ Execution Time✦ Transactions per Second✦ . . .

✦ Cores Temperature✦ Screen Brightness✦ Acceleration✦ Execution Time✦ Memory Usage✦ CPU Utilization✦ Cache Hits✦ . . .

Each dimension has a domain of values whose selection will define the specific context model

driving dim. secondary dim.


Context Model

27

‣ For a given SACS, the context model is defined as

‣ a set of <dimension, value> pairs

where each is a dimensionand is its value


‣ a set of <dimension, value> pairs

where each is a dimensionand is its value

Context Model

28

‣ For a given SACS, the context model is defined as

‣ satisfying the following constraints


goals requirementscontrol actions methods metrics measures raw data✦ #Allocated Cores✦ Off-Chip Memory Bandwidth✦ Shared Cache Space

✦ SIMO controller & ARMA model

✦ Performance✦ Resources Exploitation

✦ Weighted IPC✦ Harmonic Speed-Up✦ Resources Utilization

✦ Instructions per Cycle (IPC)

✦ ∑ Allocated Cores✦ ∑ Off-Chip Memory Bandwidth✦ ∑ Shared Cache Space

✦ #Executed Instructions✦ Execution Time✦ #Allocated Cores/App✦ Off-Chip Memory Bandwidth/App✦ Shared Cache Space/App

METE[4]

29

[4] A. Sharifi, S. Srikantaiah, A. K. Mishra, M. Kandemir, and C. R. Chita - METE: meeting end-to-end QoS in multicores through system-wide resource management - International Conference on Measurement and Modeling of Computer Systems (SIGMETRICS), 2011.


goals requirements✦ Performance

✦ Resources Exploitation

METE[4]

30

















METE[4]

31


metrics measures raw data✦ Weighted IPC✦ Harmonic Speed-Up✦ Resources Utilization












METE[4]

32


control actions methods✦ #Allocated Cores✦ Off-Chip Memory Bandwidth✦ Shared Cache Space



METE[4]

33











58.1

6 15

4

621.9 1.4 15

10245 300 2 680.2

0.8


25 325 0.8













Re-cap

34

observations


goalsContextMeta-Model

ContextModel

ContextInstance









Why do we need it?

35

Context representation:

‣ Help the computing system designer in understanding which resources are needed to be able to pursue its goal









Why do we need it?

36



sensors









Why do we need it?

37



actuators


Why do we need it?

38



‣ Document the system to provide a common and systematic classification of self-adaptive computing systems

‣ Describe, at run-time, the current context of the considered system


What can be done?

39

Context usage:

‣ Development of a framework able to generate, in a template fashion, the main control loop of the self-adaptive engine, according to the driving dimensions value

‣ Automate the testing phase, by creating optimal test sets to stimulate all possible system configurations

‣ Exploit the knowledge contained in our formalization to keep track of the context evolution, to deal with unforeseen contexts


Questions?

40

Cristiana BOLCHINIMatteo CARMINATIAntonio MIELEElisa QUINTARELLI

[email protected]




Questions?

41

Cristiana BOLCHINIMatteo CARMINATIAntonio MIELEElisa QUINTARELLI

[email protected]




Into The Wild[5]

42

goalscontrol actions methods metrics measures raw data✦ Cores Frequency

✦ Display Brightness

✦ On Demand DFS✦ Gradual Reduction

✦ Resources Exploitation✦ Power

✦ Resources Utilization✦ Power Model

✦ Average Resource Usage

✦ #Used Resources✦ CPU Utilization✦ System Up-Time✦ Screen Brightness✦ Connections Utilization✦ SD Card Accesses

[5] A. Shye, B. Scholbrock, and G. G. Memik - Into the wild: studying real user activity patterns to guide power optimizations for mobile architectures - International Symposium on Microarchitecture (MICRO), 2009.


Into The Wild[5]

43

goalscontrol actions methods metrics measures raw data✦ Cores Frequency

✦ Display Brightness

✦ On Demand DFS✦ Gradual Reduction

✦ Resources Exploitation✦ Power

✦ Resources Utilization✦ Power Model

✦ Average Resource Usage

✦ #Used Resources✦ CPU Utilization✦ System Up-Time✦ Screen Brightness✦ Connections Utilization✦ SD Card Accesses

[5] A. Shye, B. Scholbrock, and G. G. Memik - Into the wild: studying real user activity patterns to guide power optimizations for mobile architectures - International Symposium on Microarchitecture (MICRO), 2009.

Metronome[6]

goals requirementcontrol actions methods metrics measures raw data✦ Virtual Run-Time

✦ Scheduling Policy


✦ Application Progress

✦ Heartrate ✦ Heartbeat✦ Execution Time

[6] F. Sironi, D. B. Bartolini, S. Campanoni, F. Cancare, H. Hoffmann, D. Sciuto, and M. D. Santambrogio - Metronome: operating system level performance management via self-adaptive computing - Design Automation Conference (DAC), 2012.

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