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Distributed Systems: Architectures, Principles and Scenarios

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Title: Distributed Systems: Architectures, Principles and Scenarios


1
Session 2 Distributed Systems Architectures,
Principles and Scenarios Monday 10th July
Malcolm Atkinson
2
Distributed Systems Introduction, Principles
Foundations
3
Principles of Distributed Computing
  • Issues you cant avoid
  • Lack of Complete Knowledge (LoCK)
  • Latency
  • Heterogeneity
  • Autonomy
  • Unreliability
  • Change
  • A Challenging goal
  • balance technical feasibility
  • against virtual homogeneity, stability and
    reliability
  • Appropriate balance between usability and
    productivity
  • while remaining affordable, manageable and
    maintainable

This is NOT easy
4
Lack of Complete Knowledge
  • Technical origins of LoCK
  • Dynamics of systems involve very large state
    spaces
  • Cant track or explore all the states
  • Latency prevents up-to-date knowledge being
    available
  • By the time a notification of a state change
    arrives the state may have changed again
  • Failures inhibit information propagation
  • Unanticipated failure modes
  • If you ask a remote system
  • By the time the answer arrives it may be wrong

Never assume you know the state of a remote
system
5
Lack of Complete Knowledge 2
  • Human origins of LoCK
  • lack of understanding
  • Incomplete simplified models
  • Intractable models
  • Poor incomplete descriptions
  • Erroneous descriptions
  • Socio-Economic effects generate LoCK
  • Autonomous owners do not reveal all
  • About services, resources and performance
  • Intermediaries aggregate simplify

6
LoCK Counter Strategies
  • Improve the quality of the available knowledge
  • Better static information
  • Better information collection dissemination
  • Improve quality of Distributed System Models
  • Prove invariants that algorithms can exploit
  • Test axioms with real systems
  • Build algorithms that behave reasonably well
  • When they have incomplete knowledge

7
Latency
  • It is always going to be there
  • Consequence of signal transmission times
  • Consequence of messages / packets in queues
  • Consequence of message processing time
  • Errors cause retries
  • It gets worse
  • Geographic scale increases latency
  • System complexity increases number of queues
  • Scale complexity increase processing time
  • Think about
  • How many operations a system can do while a
    message it sent reaches its destination, a reply
    is formed and the reply travels back

8
Latency Counter Strategies
  • Design algorithms that require fewer round trips
  • This is THE complexity measure!
  • Batch requests and responses
  • Shorten distance to get information
  • Caching, pre-fetching replication
  • But may be stale data!
  • Move data to computation
  • But be smart about which data when
  • Move computation to data
  • Succinct computation volumes of data
  • But safety and privacy issues arise

Communication is very expensive
9
Heterogeneity
Some of the variation is wanted and exploited
  • Hardware variation
  • Different computer architectures
  • Big endians v little endians
  • Number representation
  • Address length
  • Performance
  • Different Storage systems
  • Architectures
  • Technologies
  • Available operations
  • Different Instrument systems
  • Accepting different control inputs
  • Generating different output data streams

10
Heterogeneity 2
Some of the variation is just make work
  • Operating System variation
  • Different O/S architectures
  • Unix families versions
  • Windows families and versions
  • Specialised O/S, e.g. for Instruments Mobile
    devices
  • Implementation system variation
  • Programming languages
  • Scripting languages
  • Workflow systems
  • Data models
  • Description languages
  • Grid systems
  • Many implementations of same functionality

11
Heterogeneity Counter Measures
  • Invest in virtual Homogeneity
  • Agree standards (formally or de facto)
  • Introduce intermediate code
  • That hides unwanted variation
  • Presenting it in standard form
  • But this has high cost
  • Developing the standard
  • Developing the intermediate code
  • Executing the intermediate code
  • It may hide variations some want
  • Provide direct access to facilities as well
  • But this may inhibit optimisation automation

12
Heterogeneity Counter Measures 2
  • Automatically manage diversity
  • Manual agreement and construction of virtual
    homogeneity will not scale compose
  • Develop abstract and higher level models
  • Describe each component
  • Generate the adaptations as needed from these
    descriptions
  • Not yet achievable for the general complete
    systems
  • Relevant for specific domains

13
Autonomy and Change
  • Necessary
  • To persuade organisations individuals to engage
  • They need to control their own facilities
  • They have best knowledge to develop their
    services
  • Their business opportunity
  • Because coordinated change is unachievable
  • Systems workloads are busy
  • Service commitments must be met
  • Large-scale scheduling of work is very hard
  • To correct errors
  • To plug vulnerabilities
  • To obtain new capabilities

14
Autonomy and Change 2
  • What changes local decisions
  • The underlying technology delivering a service
  • The operations available from a service
  • The semantics of the operations
  • Policy changes, e.g. authorisation rules, costs,
  • What changes corporate decisions
  • Some agreed standard is changed
  • E.g. a new version of a protocol is introduced

15
Autonomy and change Counter Measures
  • Users other providers expect stability
  • Agree some standards that are rarely changed
  • As a platform framework
  • As a means of communicating change
  • Introduce change-absorbing technology
  • Mark the protocols and services with version
    information
  • Transform between protocols when changes occur
  • Anneal the change out of the system
  • Develop algorithms tolerant to change
  • Revalidate dependencies where they may change
  • Handle failures due to change

Change is an asset Embrace and Manage it Ignore
it atyour peril
16
Unreliability
  • Failures are inevitable
  • Equipment, software operations errors
  • Network outages, Power outages,
  • Their effects must be localised
  • Cannot afford total system outages
  • This is not easy
  • Each error may occur when system is in any state
  • The system is an unknown composition of
    subsystems
  • Errors often occur while other errors are still
    active
  • Errors often occur during error recovery actions
  • Errors may be caused by deliberate attack
  • Attackers may continue their attack

17
Unreliability Counter Measures
  • Requires much RD
  • Continuous arms race as scale of Grids grow
  • Ideal of a continuously available stable service
  • Not achievable recognise that drops in response
    and local failures must be dealt with
  • Design resilient architectures
  • Design resilient algorithms
  • Improve reliability of each component
  • Distribute the responsibility
  • For failure detection
  • For recovery action

Invest heavily in error detection and recovery
18
Service Oriented Architectures
19
Three Components
Registries
Register an available service Send name
description
Service Consumers
Services
20
Three Components
Registries
Request a service Send a description
Service Consumers
Services
21
Three Components
Registries
Set (possibly empty)of matching services
Service Consumers
Services
22
Three Components
Registries
Service Consumers
Request service operation
Services
23
Three Components
Registries
Service Consumers
Services
Return result or Error
24
Composed behaviour
  • Services are themselves consumers
  • They may compose and wrap other services
  • The registry is itself a consumer
  • A federation of registries may deal with registry
    services reliability performance
  • Observer services may report on quality of
    services and help with diagnostics
  • Agreements between services may be set up
  • Service-Level Agreements
  • Permitting sustained interaction

25
Composed behaviour
  • Services are themselves consumers
  • They may compose and wrap other services
  • The registry is itself a consumer
  • A federation of registries may deal with registry
    services reliability performance
  • Observer services may report on quality of
    services and help with diagnostics
  • Agreements between services may be set up
  • Service-Level Agreements
  • Permitting sustained interaction

Requires Organising as an Architecture
26
OGF Open Grid Services Architecture
27
The Open Grid Services Architecture
  • An open, service-oriented architecture (SOA)
  • Resources as first-class entities
  • Dynamic service/resource creation and destruction
  • Built on a Web services infrastructure
  • Resource virtualization at the core
  • Build grids from small number of standards-based
    components
  • Replaceable, coarse-grained
  • e.g. brokers
  • Customizable
  • Support for dynamic, domain-specific content
  • within the same standardized framework

Hiro Kishimoto Keynote GGF17
28
Why Use an SOA?
  • Logical view of capabilities
  • Relatively coarse-grained functions
  • Reusable and composable behaviors
  • Encapsulation of complex operations
  • Naturally extendable framework
  • Platform-neutral
  • machine and OS

Hiro Kishimoto Keynote GGF17
29
SOA Web Services Key Benefits
  • SOA
  • Flexible
  • Locate services on any server
  • Relocate as necessary
  • Prospective clients find services using
    registries
  • Scalable
  • Add remove services as demand varies
  • Replaceable
  • Update implementations without disruption to
    users
  • Fault-tolerant
  • On failure, clients query registry for alternate
    services
  • Web Services
  • Interoperable
  • Growing number of industry standards
  • Strong industry support
  • Reduce time-to-value
  • Harness robust development tools for Web services
  • Decrease learning implementation time
  • Embrace and extend
  • Leverage effort in developing and driving
    consensus on standards
  • Focus limited resources on augmenting adding
    standards as needed

Hiro Kishimoto Keynote GGF17
30
Virtualizing Resources
Access
Type-specific interfaces
Storage
Sensors
Applications
Information


Computers
Common Interfaces
Resource-specific Interfaces
Hiro Kishimoto Keynote GGF17
31
Specifications Landscape April 2006
Warning Volatile data!
SYSTEMS MANAGEMENT
UTILITY COMPUTING
GRID COMPUTING
Use Cases Applications
Distributed query processing
Data Centre
ASP
Collaboration
Multi Media
Persistent Archive
VO Management
OGSA-EMS
OGSA Self Mgmt
WS-DAI
WSDM
Discovery
Information
WS-BaseNotification
Naming
GGF-UR
Core Services
Privacy
GFD-C.16
Trust
Data Model
WSRF-RL
WSRF-RP
Web ServicesFoundation
WSRF-RAP
WS-Security
SAML/XACML
X.509
WS-Addressing
CIM/JSIM
HTTP(S)/SOAP
WSDL
Data Transport
Standard
Evolving
Gap
Hole
Hiro Kishimoto Keynote GGF17
32
Summary Conclusions
33
Grids
  • Many reasons motivating investment in grids
  • Collaboration for Global Science Business
  • Resource integration sharing
  • New approach to large scale distributed systems
  • Large coordinated effort
  • Industry Academia
  • Many technical and socio-economic challenges
  • Work for you all
  • Many new opportunities
  • Work for you all

34
Summary Take home message
  • E-Infrastructure is arriving
  • Built on Grids Web Services
  • Data and Information grow in importance
  • It must include user support
  • It must be based on good socio-economic
    understanding
  • There is a dramatic rate of change
  • An opportunity for everyone

Can you ride the wave?
35
Scenarios
36
Why Scenarios
  • Abstraction of what people want to do
  • Catches the essence of their requirement
  • Framework for
  • Discussion
  • Comparison
  • Elaboration
  • Check how technologies cover scenarios
  • Opportunity not part of the scenario
  • Scenarios should not be about implementation
  • Scenario can be decomposed into steps
  • Possibly in many ways
  • These are less abstract requirements

37
Job submission scenario
1 Create or revise a job description Q In what
language? Q What must it / can it say?
38
Job submission scenario
2 Submit the job description Q How? Q With what
extra parameters?
39
Job submission scenario
3 Ask about progress Q How? Q What can they learn
and when? Q Is the reply in user or system terms?
40
Job submission scenario
4 Retrieve results Q How? Q Where can they be
found? Q Are there helpful diagnostics?
41
Job submission scenario
Q Who provides and runs this system? Q How does
it get paid for? Q What are its policies for
allocating resources to JD submissions? Q How
reliable and efficient is it? Users view?
Managers view?
42
Job submission scenario
Q How much effort does it take to submit the same
job to another system? Q How does the code for
the application get to be executed? Q How are
data read or created during the computation
handled? Q How will this system evolve? Will
users need to learn new tricks?
43
Ensemble run scenario
Computing resources any type any where
44
Ensemble run scenario
Computing resources any type any where
Coordinationsystem
resultsstore
45
Ensemble run scenario
Computing resources any type any where
resultsstore
1 Create plan for the ensemble run, e.g.
parameter space to sweep and sampling method
46
Ensemble run scenario
Computing resources any type any where
resultsstore
2 Initiate the production and submission of jobs
47
Ensemble run scenario
Computing resources any type any where
resultsstore
3 Result accumulation
48
Ensemble run scenario
Computing resources any type any where
resultsstore
4 Researcher monitors and steers progress
49
Ensemble run scenario
Computing resources any type any where
resultsstore
5 Researcher recover and analyses results -
computes derivatives
50
Ensemble run scenario
Computing resources any type any where
resultsstore
6 Researcher completes analyses discards or
archives results
51
Ensemble run scenario with context
Computing resources any type any where
Everything asbefore, plusinterleavedrequests
forcontext datafrom eachjob as it runs
Runs draw data from context stores boundary
conditions, pre-computed data, observations
52
Ensemble run scenario with metadata
Computing resources any type any where
Everything asbefore, plususe andgeneratemetada
ta aseach job runs
Runs organised using metadata and jobs generate
metadata helps manage 1000s of files
53
Repetition of Scenario
  • Normally, users repeatedly perform the same
    scenario
  • Analysis of the next sample
  • Re-analysis by other researchers designers
  • Calibration and normalisation of the latest
    observational run
  • Re-verification against the latest data
  • Evaluation of the risk of the next share purchase
  • (Revising the) design of an(other similar) engine
    component
  • Often with parametric variations
  • Often with progressive refinements
  • A better pattern recogniser
  • A refinement in calibration
  • Code fixes, updates to reference data,
  • How well do the solutions on offer support
    repetition?

54
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