Autonomic Computing. Pablo Chacin

Transcription

1 Autonomic Computing Pablo Chacin

2 Acknowledgements Some Slides taken from Manish Parashar and Omer Rana presentations

3 Agenda Fundamentals Definitions Objectives Alternative approaches Examples Research agenda Challenges The Canonical View Roots Reference architecture Limitations

4 Challenges: Complex & Uncertainty System Uncertainty Very large scales Dynamic: entities join, leave, move, change behavior Heterogeneous: capability, connectivity, reliability, guarantees, QoS Lack of common/complete knowledge (LOCK): number, type, location, availability, connectivity, protocols, semantics, etc. Information Uncertainty Availability, resolution, quality of information Devices capability, operation, calibration Trust in data, data models Semantics Application Uncertainty Dynamic behaviors: spacetime adaptivity Dynamic and complex couplings: multi-physics, multimodel, multi-resolution, Dynamic and complex (ad hoc, opportunistic) interactions Acknowledgement: O. Rana and M. Parashar

5 Complexity Dimensions Environment Complexity Cluster P2P CDN LSDS Grid Management Complexity

6 Self-Adaptive Software Software that evaluates its own performance and changes behaviour when the evaluation indicates that it is not accomplishing what the software is intended to do.... Managing complexity is a key goal of self-adaptive software. If a program must match the complexity of the environment in its own structure it will be very complex indeed! Somehow we need to be able to write software that is less complex than the environment in which it is operating yet operate robustly. [Ladd00].

7 The Vision of AC It s time to design and build computing systems capable of running themselves, adjusting to varying circumstances, and preparing their resources to handle most efficiently the workloads we put upon them. They must anticipate needs and allow users to concentrate on what they want to accomplish Paul Horn, IBM

8 Autonomic Properties Self-configuring: Adapt automatically to the dynamically changing environment Self-healing: Discover, diagnose and react to disruptions Self-optimizing: Monitor and tune resources automatically Self-protecting: Anticipate, detect, identify, and protect against attacks from anywhere

9 Biological roots The body s internal mechanisms continuously work together to maintain essential variables within physiological limits that define the viability zone directly linked with the survivability Ashby's Ultra-stable system. S. J. Holmes classification of animal behavior

10 Self-reflective Self-aware Self-governing Self-* Soup Self-anticipating Self-situated Self-diagnosis Self-directed Self-organization Self-monitoring Self-destructing Self-recovering Self-adjusting

11 Autonomic Characteristics Automatic: being able to self-control its internal functions and operations without any manual intervention or external help Adaptive: able to change its operation (i.e., its configuration, state and functions) to cope with temporal and spatial changes in its operational context Aware: able to monitor (sense) its operational context as well as its internal state

12 Challenges Conceptual Challenges: defining appropriate abstractions and models for specifying, understanding, controlling, and implementing autonomic behaviors Architecture Challenges: design system and software architectures to guide the specification and implementation of self-managing behaviors of constituent autonomic elements and their interactions. Middleware Challenges: provide the core services required to realize autonomic behaviors in a robust, reliable and scalable manner, in spite of the dynamism and uncertainty of the system. Application Challenges: build programming models and frameworks that support the development of autonomic elements Source: [PaHa05]

13 The Canonical View

14 MAPE-K Cycle Fundamental atom of the architecture Managed element(s) Database, storage system, server, software app, etc. Plus one autonomic manager Responsible for: Providing its service Managing its own behavior in accordance with policies Interacting with other autonomic elements Autonomic Manager Analyze Plan Monitor Execute Knowledge S E Managed Element An Autonomic Element Acknowledgement: O. Rana and M. Parashar

15 MAPE-K Structure Architectural Model Black Box models Introspection Knowledge Goals Rules Utility function Objective function Analytical (e.g. queuing theory) Semantic SLA Logic constrains Formal algebras

16 MAPE-K Monitor Events Probes Signals (e.g. prices) Plan Optimization Local search Evolutionary algorithms Rules Planning Logical reasoning Analise Mining Machine learning Filters Rules Assisted learning Execute Workflows Mobile agents Component rewiring Mostly domain specific

17 Reference Architecture Acknowledgement: O. Rana and M. Parashar

18 Limitation of Canonical View Not a sound theoretical background Biology as an Inspiring metaphor Gap between goals and model (e.g. feedback loops alone can't achieve self-awareness) Don't address distributed adaptation Scalability Lack of global/accurate information Delays Failures applying changes Coordination

19 Alternative Approaches Biologically Inspired Socially Inspired Economic Theory

20 Biology Inspired Exploits very rich set of biological phenomena Stigmergy: indirect coordination using the environment (e.g. pheromones) Emergent Self-organization (e.g. swarm intelligence, epidemic algorithms) Evolutionary adaptation (e.g. genetic algorithms) Not very formal: Ad hoc modeling requires identifying analogies between target system and biological systems, mapping goals, environment, interactions, etc.

21 Social Models Exploit social phenomena Social networks and interactions Learning Information dissemination (e.g. gossiping) Collective behaviors (crowds) Organization and coordination (e.g. norms) Use concepts that are close to computer systems management: preferences, organization, coordination Unfortunately, we are still learning about how society works:lack of comprehensive theories

22 Economic Models Based on the observation that economy can achieve social goals of millions of individuals sort of Model self-adaptation as an economy on which selfish agents pursue their individual goals by trading Not necessarily modeled as markets Money is not necessary (e.g. bartering) Leverage powerful theoretical body: game theory, mechanism design, value theory Needs the mapping of adaptation policies to pricing policies, trading preferences It's not clear to what extend computer economies follow the same assumptions than human economies What happens with market regulations? Taxes, monetary polices

23 Examples

24 Autonomia [DHX+03]

25 Autonomia Straightforward implementation of the MAPE-K model Agent based architecture Key components Application Management Editor: develop application from a library of components. Define management policies Application Delegated Manager: monitors application, selects resources, trigger agents Autonomic Middleware Service: supporting services for application's agents Knowledge repository

26 Unity [TCW+04]

27 Unity Uses application dependent Utility Functions to express the preferences over alternative resource allocations Application Environment Manager: responsible for predicting how a variation in the allocated resources will impact the achievement of the application goals and for obtaining the resources that the environment needs The Resource Arbiter element is responsible for deciding which resources from a pool should be assigned to which application environment. Very centralized. Scalability?

28 Automate [PPL+06]

29 Automate Semantic middleware: offers services for P2P semantic messaging and topology maintenance. Decentralized Coordination Engine: supports a decentralized decision making process by means of an agent based deductive rule execution engine. Programming System: supports the definition and execution of autonomic components, capable of selfconfiguration, adaptation and optimization. Offers services for dynamic component composition. Fully decentralized architecture based on P2P model. However, scalability of coordination is a issue.

30 eudon

31 eudon Based on Epidemic overlays Self-organizing Resilient, low overhead, scalable Utility-Function driven Self-adaptation using local information Accommodate local policies/conditions Emergent coordination Model-less adaptation Don't assume global/accurate information Reactive Robust

32 Challenges

33 Challenges A sound theory of Autonomicity Up to know, we have been borrowing metaphors and isolated techniques from other fields A more generic reference architecture Consider large scale distributed systems Tools Frameworks, languages, platforms Metrics to measure fitness of adaptation Testbeds and benchmarks

34 Conclusions A new class of large scale distributed applications impose significant challenges for the management Autonomic Computing emerged as a vision to address these challenges The Autonomic Computing vision is yet to be realized The canonical view has important limitations for large scale distributed systems Approaches that exploit emergent properties seems the more promising alternative

35 References [ChNa11] P. Chacin,L. Navarro. Utility Driven Elastic Services. 11th IFIP International Conference on Distributed Applications and Interoperable Systems (DAIS). LNCS [DHX+03] X. Dong et al. (2003). Autonomia: an autonomic computing environment. IEEE International Performance, Computing, and Communications Conference, 2003 [Ladd00] Laddaga, R. (2000) Active Software. First International Workshop on Self-Adaptive Software (IWSAS 2000). LNCS [PaHa05 ] M. Parashar, S. Hariri. (2005). Autonomic Computing: An Overview. In LNCS Proceedings of the Workshop on Unconventional Programming Paradigms 2004 ( ) [PLL+06] Parashar et al. (2006). AutoMate: Enabling Autonomic Grid Applications. Cluster Computing, The Journal of Networks, Software Tools, and Applications, 9(1). [TCW+04] G. Tesauro et al. (2004) A Multi-Agent Systems Approach to Autonomic Computing. Third International Joint Conference on Autonomous Agents and Multiagent Systems,

36 Questions?... No, really, Questions? Thanks! Pablo Chacin