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Rule-based SLA mediation

Andras Micsik, Henar Muñoz Frutos

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Problem Area

Negotiatio

n

Negotiation

Different languages, different metrics

ANSYS request

•CPUName: IntelCore Duo•CPU Speed: 2 GHz•Capacity: 400 MB•Price: 27 euros per day•DiskSpace: 250GB

CUSTOMERSoftware engineering company

-RAMMemory: 7.5 GB-ComputeUnit: 4 ECU-Storage: 850GB-Platform: 64 bit-Price: $0.4 per instance hour

AMAZON

-MemoryPerTask: 7.5 GB-ClockCPUSpeed: 100 MHz / process-StorageCapability: 850GB-Cost: 5 euros/task/hour

BSC

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Architecture

Comon conceptual

model

SLA Mediator SLA Mediator

SLA framework SLA framework

SA-SLA(WS-Agreement

WSLA)

Local domain

knowledge

Local domain

knowledge

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Service Consumer Service Provider

MinCacheSize

4

MBGuaranteeTerm

MinimumCacheSizeMB

Units Metrics

CacheSizeMin

4096

kB

GuaranteeTerm

local mapping1 local mapping2

Common SLA & QoS ontology

negotiation

Use of local mappings

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Quality Factoror SLO

Measurement Unit

Metric

Main concepts of common ontology

Quality Modelor SLA

Request

Agreement

Offer BusinessQoS

ImplementationQoS

InfrastructureQoS

ApplicationQoS

NetworkQoS

InfrastructureMetric

MemoryMetric

StorageMetric

PerformanceMetric

PriceMetric

ResponseTime

MonetaryUnit

StorageUnit

TimeUnit

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OWL-SOWL-S

S-BPMNS-BPMN

Business layerBusiness layer

Implementation layerImplementation layer

Business OntologyBusiness Ontology

OWL-WSOWL-WS

QoS OntologyQoS Ontology

Technical ontology

Technical ontology

GROGRO S-OGSAS-OGSA

IT workflowsServices

Semantic bindingsGrid resources

SLAs

Business processes

Organization of BREIN ontologies

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Implementation of SLA Mediator

• Local knowledge is collected in OWL:– SLA templates,– Available resources, resource types

• Mapping between common model and local model is provided in the form of– Conversion definitions (e.g. rates in OWL)– Conversion rules (in SWRL)

• Translation of incoming SLA bids is done as a sequence of– Conversion of XML data into OWL data– Conversion of common OWL data to local OWL data (i.e. using local

metrics)– Matching and ranking SLA bid with SLA templates

• Implementation uses a mixture of custom Java code, SWRL and OWL DL reasoning

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Local interpretation of SA-SLA

<wsla:SLAParameter name=“Capacity" type="double" unit="MB"

sawsdl:modelReference="#RAMMemory"> <wsla:Metric>memory</wsla:Metric>

</wsla:SLAParameter>……

slaParameter1

GB

RAMMemory

Capacity

name unit

type

slaParameter1

GB

RAMMemory

Capacity

nameunit

type

SLO1

4

value

slaParameter1

GB

SLO1

4

value

4096

kB>=>=

unit

localvalue

localunit

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Evaluation of offers vs. bid

SLO1

Bid1

Offer1SLO2

Offer2

SLO3

SLO4

SLO5

SLO6

0.5

1.0

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Evaluation of SLA Mediator

• Advantages– Incoming data is converted and processed in a “localized”

format, applying locally used metrics and measuring units– The matching and ranking of suitable templates is

customizable to match specific local needs– Using rules helps to avoid changing code

• Challenges– Reasoning technology is slow– Expressivity of SWRL is weak

• Ongoing work– Matching SLA requests with current availability of

resources– Improving performance of reasoning

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Issues for discussion

• Already several terminologies exist: WSQM, WSLA, TMF, ... etc. – How to harmonize these in a common QoS ontology?– Which terminology to use to name concepts in a common

QoS ontology?

• Performance and maturity problems of OWL– Do we need safety or non-monotonicity with rules?– Getting good GUIs for OWL and rules management– Do we need slow OWL or fast RDF repositories?