A Framework to Model Self-Adaptive Computing Systems

Transcription

1 A Framework to Model Self-Adaptive Computing Systems AHS di Torino - Italy June 25, 2013 Cristiana BOLCHINI Matteo CARMINATI Antonio MIELE Elisa QUINTARELLI mcarminati@elet.polimi.it

2 Motivations 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 2

3 Context 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,

4 Context acquisition context [2] representation usage interpretation reasoning [2] M. Baldauf, S. Dustdar, and F. Rosenberg - A survey on context-aware systems - International Journal Ad Hoc and Ubiquitous Computing (IJAHUC),

5 Context acquisition modeling context [2] representation usage interpretation reasoning [2] M. Baldauf, S. Dustdar, and F. Rosenberg - A survey on context-aware systems - International Journal Ad Hoc and Ubiquitous Computing (IJAHUC),

6 Context user physical [3] context [2] acquisition representation modeling computing usage interpretation reasoning [2] M. Baldauf, S. Dustdar, and F. Rosenberg - A survey on context-aware systems - International Journal Ad Hoc and Ubiquitous Computing (IJAHUC), [3] B. Schilit, N. Adams, and R. Want - Context-aware computing applications - Workshop on Mobile Computing Systems and Applications (HotMobile),

7 Context user physical [3] context [2] acquisition representation modeling computing usage interpretation reasoning [2] M. Baldauf, S. Dustdar, and F. Rosenberg - A survey on context-aware systems - International Journal Ad Hoc and Ubiquitous Computing (IJAHUC), [3] B. Schilit, N. Adams, and R. Want - Context-aware computing applications - Workshop on Mobile Computing Systems and Applications (HotMobile),

8 Self-Adaptiveness [4] Sensor Sensor Sensor Private Self-Awareness Engine State & Context Learnt Model(s) Public Self-Awareness Engine Environment Monitor/ Controller Actuator Goals Values Objectives Constraints Self-Expression Engine Actuator External Actions [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),

9 Self-Adaptiveness [4] Sensor Sensor Sensor Private Self-Awareness Engine State & Context Learnt Model(s) Public Self-Awareness Engine Environment Monitor/ Controller Actuator Goals Values Objectives Constraints Self-Expression Engine Actuator External Actions [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),

10 Self-Adaptiveness [4] Sensor Sensor Sensor Private Self-Awareness Engine State & Context Learnt Model(s) Public Self-Awareness Engine Environment Monitor/ Controller Actuator Goals Values Objectives Constraints Self-Expression Engine Actuator External Actions [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),

11 Self-Adaptiveness [4] Sensor Sensor Sensor Private Self-Awareness Engine State & Context Learnt Model(s) Public Self-Awareness Engine Environment Monitor/ Controller Actuator Goals Values Objectives Constraints Self-Expression Engine Actuator External Actions [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),

12 Self-Adaptiveness [4] Sensor Sensor Sensor Private Self-Awareness Engine State & Context Learnt Model(s) Public Self-Awareness Engine Environment Monitor/ Controller Actuator Goals Values Objectives Constraints Self-Expression Engine Actuator External Actions [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),

13 Self-Adaptiveness [4] Sensor Sensor Sensor Private Self-Awareness Engine State & Context Learnt Model(s) Public Self-Awareness Engine Environment Monitor/ Controller Actuator Goals Values Objectives Constraints Self-Expression Engine Actuator External Actions [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),

14 Work Goals 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 14

15 What is relevant? Self-Adaptive Computing Systems (SACS) 15

16 What is relevant? Self-Adaptive Computing Systems (SACS) elements ODA control loop O: high-level quantities D: aspects to reason on A: knobs and strategies 16

17 What is relevant? Self-Adaptive Computing Systems (SACS) elements ODA control loop O: high-level quantities D: aspects to reason on relations Both direct and indirect effects of planned actions A: knobs and strategies 17

18 Context Dimensions Main Dimensions goals requirements observations 18

19 Context Dimensions Main Dimensions goals requirements observations Collected Data Dimensions raw data measures metrics 19

20 Context Dimensions Main Dimensions goals requirements observations Collected Data Dimensions raw data measures metrics Self- Expression Dimensions control actions methods 20

21 Context Meta-Model goals control actions methods requirements metrics measures raw data observations driving dimensions 21

22 Context Meta-Model goals control actions methods requirements metrics measures raw data observations driving dimensions may not exist in some contexts 22

23 Context Meta-Model segments represent relations between dimensions goals control actions methods requirements metrics measures raw data observations driving dimensions may not exist in some contexts 23

24 Context Meta-Model OR relation segments represent relations between dimensions AND relation goals control actions methods requirements metrics measures raw data observations driving dimensions may not exist in some contexts 24

25 Context Meta-Model OR relation segments represent relations between dimensions AND relation goals control actions methods requirements metrics measures raw data observations driving dimensions may not exist in some contexts New dimensions can be added by simply connecting them to the existing ones through (possibly new) relations 25

26 Context Model driving dim. secondary dim. Each dimension has a domain of values whose selection will define the specific context model control actions methods goals/requirements/observations metrics measures raw data #Allocated Cores Off-Chip Memory Bandwidth Shared Cache 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 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... 26

27 Context Model For a given SACS, the context model is defined as a set of <dimension, value> pairs where each is a dimension and is its value 27

28 Context Model For a given SACS, the context model is defined as a set of <dimension, value> pairs where each is a dimension and is its value satisfying the following constraints 28

29 METE [4] control actions methods goals requirements metrics measures raw data #Allocated Cores Off-Chip Memory Bandwidth Shared Cache SIMO controller & ARMA model Exploitation Performance Weighted IPC Harmonic Speed-Up Utilization Instructions per Cycle (IPC) Allocated Cores Off-Chip Memory Bandwidth Shared Cache #Executed Instructions Execution Time #Allocated Cores/App Off-Chip Memory Bandwidth/App Shared Cache /App [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),

30 METE [4] control actions methods goals requirements metrics measures raw data #Allocated Cores Off-Chip Memory Bandwidth Shared Cache SIMO controller & ARMA model Exploitation Performance Weighted IPC Harmonic Speed-Up Utilization Instructions per Cycle (IPC) Allocated Cores Off-Chip Memory Bandwidth Shared Cache #Executed Instructions Execution Time #Allocated Cores/App Off-Chip Memory Bandwidth/App Shared Cache /App goals Exploitation requirements Performance [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),

31 METE [4] control actions methods goals requirements metrics measures raw data #Allocated Cores Off-Chip Memory Bandwidth Shared Cache SIMO controller & ARMA model Exploitation Performance Weighted IPC Harmonic Speed-Up Utilization Instructions per Cycle (IPC) Allocated Cores Off-Chip Memory Bandwidth Shared Cache metrics measures raw data #Executed Instructions Execution Time #Allocated Cores/App Off-Chip Memory Bandwidth/App Shared Cache /App Weighted IPC Harmonic Speed-Up Utilization Instructions per Cycle (IPC) Allocated Cores Off-Chip Memory Bandwidth Shared Cache #Executed Instructions Execution Time #Allocated Cores/App Off-Chip Memory Bandwidth/App Shared Cache /App [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),

32 METE [4] control actions methods goals requirements metrics measures raw data #Allocated Cores Off-Chip Memory Bandwidth Shared Cache SIMO controller & ARMA model Exploitation Performance Weighted IPC Harmonic Speed-Up Utilization Instructions per Cycle (IPC) Allocated Cores Off-Chip Memory Bandwidth Shared Cache #Executed Instructions Execution Time #Allocated Cores/App Off-Chip Memory Bandwidth/App Shared Cache /App control actions #Allocated Cores Off-Chip Memory Bandwidth Shared Cache methods SIMO controller & ARMA model [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),

33 METE [4] control actions methods goals requirements metrics measures raw data #Allocated Cores Off-Chip Memory Bandwidth Shared Cache SIMO controller & ARMA model Exploitation Performance Weighted IPC Harmonic Speed-Up Utilization Instructions per Cycle (IPC) Allocated Cores Off-Chip Memory Bandwidth Shared Cache #Executed Instructions Execution Time #Allocated Cores/App Off-Chip Memory Bandwidth/App Shared Cache /App [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),

34 Re-cap Context Meta-Model control actions methods goals requirements observations metrics measures raw data Context Model control actions methods goals requirements metrics measures raw data #Allocated Cores Off-Chip Memory Bandwidth Shared Cache SIMO controller & ARMA model Exploitation Performance Weighted IPC Harmonic Speed-Up Utilization Instructions per Cycle (IPC) Allocated Cores Off-Chip Memory Bandwidth Shared Cache #Executed Instructions Execution Time #Allocated Cores/App Off-Chip Memory Bandwidth/App Shared Cache /App Context Instance control actions methods goals requirements metrics measures raw data #Allocated 25 Cores Off-Chip 325Memory Bandwidth Shared 0.8 Cache SIMO controller & ARMA model Exploitation Performance Weighted IPC Harmonic 1.4 Speed-Up 15 Utilization Instructions 58.1 per Cycle (IPC) Allocated 6 Cores Off-Chip 15 Memory Bandwidth Shared 4 Cache #Executed Instructions Execution 300 Time #Allocated 2 Cores/App Off-Chip 680.2Memory Bandwidth/App Shared 0.8Cache /App 34

35 Why do we need it? Context representation: Help the computing system designer in understanding which resources are needed to be able to pursue its goal control actions methods goals requirements metrics measures raw data #Allocated Cores Off-Chip Memory Bandwidth Shared Cache SIMO controller & ARMA model Exploitation Performance Weighted IPC Harmonic Speed-Up Utilization Instructions per Cycle (IPC) Allocated Cores Off-Chip Memory Bandwidth Shared Cache #Executed Instructions Execution Time #Allocated Cores/App Off-Chip Memory Bandwidth/App Shared Cache /App 35

36 Why do we need it? Context representation: Help the computing system designer in understanding which resources are needed to be able to pursue its goal control actions methods goals requirements metrics measures raw data #Allocated Cores Off-Chip Memory Bandwidth Shared Cache SIMO controller & ARMA model Exploitation Performance Weighted IPC Harmonic Speed-Up Utilization Instructions per Cycle (IPC) Allocated Cores Off-Chip Memory Bandwidth Shared Cache sensors #Executed Instructions Execution Time #Allocated Cores/App Off-Chip Memory Bandwidth/App Shared Cache /App 36

37 Why do we need it? Context representation: Help the computing system designer in understanding which resources are needed to be able to pursue its goal control actions methods goals requirements metrics measures raw data #Allocated Cores Off-Chip Memory Bandwidth Shared Cache SIMO controller & ARMA model actuators Exploitation Performance Weighted IPC Harmonic Speed-Up Utilization Instructions per Cycle (IPC) Allocated Cores Off-Chip Memory Bandwidth Shared Cache #Executed Instructions Execution Time #Allocated Cores/App Off-Chip Memory Bandwidth/App Shared Cache /App 37

38 Why do we need it? Context representation: Help the computing system designer in understanding which resources are needed to be able to pursue its goal 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 38

39 What can be done? Context usage: Development of a framework able to generate, in a template fashion, the main control loop of the selfadaptive 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 39

40 Questions? Cristiana BOLCHINI Matteo CARMINATI Antonio MIELE Elisa QUINTARELLI 40

41 Questions? Cristiana BOLCHINI Matteo CARMINATI Antonio MIELE Elisa QUINTARELLI 41

42 Into The Wild [5] control actions methods goals metrics measures raw data Cores Frequency Display Brightness On Demand DFS Gradual Reduction Exploitation Power 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),

43 Into The Wild [5] control actions methods goals metrics measures raw data Cores Frequency Display Brightness On Demand DFS Gradual Reduction Exploitation Power 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), Metronome [6] control actions methods goals requirement metrics measures raw data Virtual Run-Time Scheduling Policy Exploitation Performance 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),