Introduction to Autonomic Computing Johan Tordsson Department of - - PowerPoint PPT Presentation

Introduction to Autonomic Computing

Johan Tordsson Department of Computing Science

www.cloudresearch.org

About me

• MSc (Civ.Ing) Computer Science (2004)
• PhD Umeå, Grid computing (2009)
• Postdoc in Madrid Spain (2009), OpenNebula
• Architect etc. in misc. EC projects (2009-2013)
• Associate professor (2014 - now)
• Research

– Autonomic cloud and data center management – How to make clouds run themselves faster/better/cheaper?

• Spare time job:

– CTO & co-founder for Elastisys (UMU cloud research spinoff) – Evangelizing that computers (will) beat humans at IT operations

Outline

• Why

– do we need autonomic computing?

• What

– are autonomic systems?

• How

– to build these autonomic systems?

• When

– will they happen?

• Who

– will build them?

Motivation: software complexity

Motivation: scale

• Enorma byggnader med servrar,

lagringsutrustning, nätverk, kylning

• En fabrik för IT-tjänster

5

Motivation: faults

Question: what is the probability of a hard drive failure? In my laptop? Will happen every few years,

hopefully not right now…

In a large supercomputer or data center?

More than 100k nodes Will happen during this talk!

Motivation: costs

• Question: How many servers can be handled by a

system administrator?

• Very old question…
• Some numbers:

– 10 - very complex systems – ~300 - standard large-scale organization – Several 1000s – virtualized data center – 26k (Facebook 2013)

• Highel-level management and better abstractions

are needed

– Alternative: exponential increase in need for systems management

Autonomic option

• Autonomic computing

– Named after autonomic nervous system – Systems manage themselves according to admin goals – Self-governing operation of entire system, not just parts of it – New components integrate effortlessly - as a new cell establishes itself in the body

Autonomic Computing

• IBM initiative in early 2000’s
• Landmark paper published 2003

in IEEE Computer by Kephart and Chess @ IBM

• Active research field since,

during 2003-2013:

– 200 conferences/workshops – 8000+ papers

• Lots of funding

– EC FP6, FP7, H2020 – WASP…

• Industry uptake

– Many big IT vendors & startups

• Key point

– Self-management of IT systems

Self-management (1/3)

• Self-management

– Changing components – External conditions – Hardware/software failures

• Ex. component upgrade

– Continually check for component upgrades – Download and install – Reconfigure itself – Run a regression test – When it detects errors, revert to the older version

Self-management (2/3)

• Four aspects of self-management

– Self-configuration

• Configure themselves automatically
• High-level policies (what is desired, not how)

– Self-optimization

• Continually seek ways to improve their operation
• Hundreds of tunable parameters

– Self-healing

• Handle faults and errors
• Analyze information from logs and monitors

– Self-protection

• Malicious attacks
• Cascading failures
• Admin mistakes

Self-management (3/3)

• Autonomic computing achievable

without self-awareness? – Without hard artificial intelligence

• (Hollywood) Misconception:

machines will take over all human tasks – AI could be a “real danger” (S. Hawking) – Unemployment? –

• Actual idea:

Machines will free people to manage systems at higher level

Hal 9000, 2001

Terminator

g!

Autonomic elements

• Fundamental atom of

the architecture

– Managed element(s)

• Server, database,

storage system, etc.

– Autonomic manager

• Responsible for:

– Providing its service – Managing behavior according to goals Interacting with other autonomic elements

Autonomic manager Knowledge Managed element Analyze Plan Monitor Execute

Autonomic element details

• Sensors: monitor environment
• Effectors: tune managed element
• MAPE loop:

– Process for self-management of autonomic element Monitor Analyze Sensors Execute Plan Effectors Knowledge

Autonomic Manager Managed Element

Sensors Effectors

The MAPE loop

• 1. Monitor:

– Collect information about state of system – Lot of metrics around – Which ones to gather? – How often to monitor?

• 4. Execute

– Turn the “knobs” of the managed element – Interactions between knobs?

• Unknown, even to human operators
• At Google, 238 knobs in each managed entity

The MAPE loop (cont.)

• 2. Analyze

– Estimate current state based on monitoring data – Commonly use model of the world for this

• “All models are wrong, but some are useful”
• What part of system to model? How?
• Correlations?
• 3. Plan

– Select action(s), i.e., which knobs to turn? – Can be formulated as optimization problem – Reactive vs. Predictive/Proactive methods

• Knowledge management

– Update model dynamically (monitoring) – Evaluate effects of actions (execution)

Engineering challenges (1/3)

• Life cycle of an autonomic element

– Design, test, and verification

• Testing autonomic elements a challenge

– Installation and configuration

• Element registers itself in a directory service

– Monitoring and problem determination

• Elements will continually monitor themselves
• Adaptation, optimization, reconfiguration

– Upgrading – Uninstallation or replacement

Engineering challenges (2/3)

• Relationships among autonomic elements

– Specification

• Set of output/input services of autonomic elements
• Expressed in a standard format
• Description syntax and semantics

– Location

• Find input services that autonomic element needs

– Negotiation – Provision – Operation

• Autonomic manager oversees the operation

– Termination

Engineering challenges (3/3)

• System-wide issues

– Authentication, encryption, signing – Autonomic elements can identify themselves – Autonomic system must be robust against insidious forms of attack

• Goal specification

– Humans provide the goals and constraints – Ensure that goals are specified correctly in the first place – Autonomic systems need to protect themselves from bad input goals:

• Inconsistent, implausible, dangerous, or unrealizable

Specifying goals (1/3)

• Rules

– Often simple condition-action pairs

• If something happens, do this
• If something else happens, do that
• …

– Can use more complex languages to express states, context, etc. – Explicit enumeration tedious – Very limited ability to express complex actions

Specifying goals (2/3)

• Utility functions

– Mathematical expressions – Maps system state to scalar value – Represents high-level objectives – What parts of system state to include? – What should function look like?

Specifying goals (3/3)

• Policies

– (higher-level) descriptions of goals and constraints for operation – How to map to lower-level behavior? – Composition of multiple policies – What high-level language to use?

• Turing-complete?
• No widely used languages available today
• Human operators used to explicit steering

– Not used to indirect goal specification

Autonomic management techniques - requirements

• Robustness

– Avoid oscillations or behavioral changes

• Scalability

– Internet-scale: millions of servers and networks, even more autonomic agents (50 billion devices?)

• Adaptive to changing workloads

– Some methods reliable for certain load patterns, but unstable once the load or system dynamics change

• Performance

– Need to make decisions fast enough to react timely – Optimal solutions vs. approximations

• Simplicity

– Key to adoption – Complex models vs. model-free? – Learning phase required before deployment?

Autonomic management - sample techniques

• Heuristic frameworks

– Fast and simple, rules of thumb

• Control theory

– Used to steer, e.g., industrial plants, embedded systems, etc. – Discretization for data packet flows (queuing theory)

• Machine learning

– Evolve behavior based on empirical (monitor) data – Examples: Neural networks, genetic algorithms, reinforcement learning

Heuristics

• Rules of thumb

– Often lack theoretic background

• Often used to handle very complex (NP-hard)

problems

– Scalable, find fast solutions

• Greedy:
• Local decisions that make sense right here/now
• May not result in optimal solution

– Hill climbing

• Steer search (manage system in this case) towards steepest

slope

– Often no upper bound

• Not possible to know distance from optimal solution

– ”The O-word…”

Control theory

• Mathematical models to monitor and steer

dynamic systems

– Real-time allocation of CPU, memory, etc.

• Some simple examples:

– Proportional control

• Adjust signal proportionally to compensate error

– PID (Proportional Integral Derivative) control:

• Integral: adjustment w.r.t. error over time
• Derivative: adjustment w.r.t. error trend

Neural networks

• Mimics the brain’s neuron systems
• Input/hidden/output layers of neurons:

– Neurons in hidden layer: activation functions maps input signal to output signal – Action functions tuned upon error in output layer (errors are propagated back for tuning)

• Often used to capture multi-dimensional problems that are

hard to model with other techniques

• Hard to train (need representative training data)
• Hard to understand cause/effect (hidden layers)

& Deep learning

Genetic algorithms

• Inspired by natural evolution
• Ingredients:

– Population with genetic representations (behaviors) for candidate solutions (can be hard) – Inheritance, crossover, and mutation operators – A rating function to compare solutions and select

• Termination?

– Only compares to prev. generation – Optimal solution?

• Adaptable to

dynamic environments?

Reinforcement learning

• Previous methods use a model (internal

representation) of the world

• Reinforcement learning (can be) model free
• System learns dynamically to

– select the best action for a given state – based on reward (reinforcement) function

• How to:

– Assign value to actions? – Balance exploration (learning) vs. exploitation (benefit from good, known actions)

• What if environment is too dynamic?

– Most states have not been seen before?

Autonomic element(s)

• Autonomic element seems doable
• Autonomic elements?
• Multi-agent systems as inspiration

– Behaviors and goals of the systems – Pattern and type of interactions among agents

• How to decentralized achieve

high-level goals? – Understand, control, and exploit emergent behavior – Convergence?

Autonomic elements interaction

• Relationships

– Dynamic, short-lived – Formed by agreement?

• May be negotiated

– Full spectrum

• Peer-to-peer
• …
• Hierarchical

– Subject to policies

• Compare single-element

policies

Interacting control/optimization loops

Transaction Requests

Increase demand

Server 1

DB Service

Server 2 File System Storage Service 2

Storage Service 1 Increase service Feedback control &

• ptimization of single

autonomic elements

• Done for 1-2

variables What happens when feedback loops interact?

Interacting control/optimization loops

Transaction Requests

Increase demand

Server 1 DB Service Storage Service 1 Capacity limit reached: Get more storage

X

Server 2 File System Storage Service 2

Interacting control/optimization loops

Demand not being met: Find alternate supplier Getting more storage

X

Transaction Requests

Server 1 DB Service Storage Service 1

Server 2 File System Storage Service 2

Interacting control/optimization loops

Transaction Requests

Server 1 DB Service Storage Service 1

Server 2 File System Storage Service 2

Sorry; already found an alternative Ready to give you that extra service

X

Transaction Requests Server 1 DB 1 Server 2 File System 1 Storage 2 Storage 1

Negotiation and resource allocation

Request( QueryService, Queries = 800/sec, Type = 2, RT = 5 sec) Request( QueryService, Queries = 400/sec, Type = 5, RT = 3 sec) Request( TableSpace, Size = 3 GBytes, Reads = 2000/sec, Writes = 100/sec) Request( LogicalVolume, Size = 12 Gbytes, Reads = 500/sec, Writes = 500/sec) Counterpropose( TableSpace, Size = 3 GBytes, Reads = 1600/sec, Writes = 100/sec) Counterpropose( QueryService, Queries = 320/sec, Type = 5, RT = 4 sec)

Should all requests be met? Compute costs and benefits, propagate them down Forms of negotiation:

• Bilateral
• Multilateral
• Auction
• Supply chain
• Competitive/coop

Learning

• During negotiation
• Strategy evolution
• Collective behavior?

Autonomic Computing Adaptation?

• Fully autonomic computing

– Evolve as increasingly sophisticated autonomic managers are to existing managed elements

• Autonomic elements will function at many levels

– At the lower levels

• Limited range of internal behaviors
• Hard-coded behaviors

– At the higher levels

• Increased dynamism and flexibility
• Goal-oriented behaviors
• Hard-wired relationships will evolve into flexible

relationships that are established via negotiation

Adaptation (cont.)

• 1. Collect and aggregate information

– Support decisions by human administrators

• 2. Advisors suggesting possible actions by

humans

• 3. Autonomic systems entrusted with lower-

level decisions

• 4. Over time, less frequent and more high-

level decisions by operator

– Carried out by numerous autonomic actions at lower level

Autonomic computing – a developer perspective

• Delegation of human operator responsibility

– Trust

• A breakdown of the MAPE loop breakdown:

– Monitoring: Delayed? Missing? Incorrect? – Analyze & Plan: model is wrong! – Execute:

• What if actuators (knobs) do not act as expected?
• The underlying system is likely (autonomically) trying

to counteract actuators

• And your autonomic system is being steered by a

higher-level one

Developer perspective (cont.)

• In autonomics, so much more can go wrong

– All computer systems fail – Autonomic systems actively steer other systems, i.e., can actively make other systems fail

• “Intelligent” actions harmful
• Cascading failures
• #1 feature: turn if off
• #2 feature: add a “I don’t understand” mode
• “What can go wrong?”

– If your automated system cannot handle odd inputs/ configs/etc, you should not build it…

Autonomic Computing Research Trends

(Selected) Research trends

• Cyber-physical systems

– Datacenters: building + hardware + software

• Interacting autonomic systems

– Hierarchical & distributed – Understanding and controlling these

• Multi-criteria

– Multiple goals (cost, energy, performance, …) – Multiple stakeholder

• Datacenter owners, Application owners, end-users

Even more research trends

• Data-driven, predictive, & proactive

– Feedback control not enough

• Self-aware systems

– Self-reflective, self-predictive, self-adaptive – Context, correlations, and online models

• Need for benchmarks

– Not only performance, but other self-* aspects

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Summary

• Autonomic computing needed for management
• f complex systems such as clouds
• Systems manage (config, repair, optimize,

protect) themselves according to admin goals

– Achievable w/o solving hard AI problem

• Many different techniques for autonomic

management

• Goal-specification can be hard
• Interacting autonomic elements complicate
• Great care needed to build autonomic systems
• Many unsolved research questions

Thanks! Questions?

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Capacity planning is hard!