1. 06/12/2015Page 1 Rule-based SLA mediation Andras Micsik, Henar Muñoz Frutos

2. 06/12/2015Page 2 2 Problem Area Negotiation Different languages, different metrics ANSYS request CPUName: IntelCore Duo CPU Speed: 2 GHz Capacity: 400 MB Price: 27 euros per day DiskSpace: 250GB CUSTOMER Software 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

3. 06/12/2015Page 3 Architecture Comon conceptual model SLA Mediator SLA framework SA-SLA (WS-Agreement WSLA) Local domain knowledge Local domain knowledge

4. 06/12/2015Page 4 Service ConsumerService Provider MinCacheSize 4 MB GuaranteeTerm MinimumCacheSize MB UnitsMetrics CacheSizeMin 4096 kB GuaranteeTerm local mapping1 local mapping2 Common SLA & QoS ontology negotiation Use of local mappings

5. 06/12/2015Page 5 Quality Factor or SLO Measurement Unit Metric Main concepts of common ontology Quality Model or SLA Request Agreement Offer BusinessQoS ImplementationQoS InfrastructureQoS ApplicationQoS NetworkQoS InfrastructureMetric MemoryMetric StorageMetric PerformanceMetric PriceMetric ResponseTime MonetaryUnit StorageUnit TimeUnit

6. 06/12/2015Page 6 OWL-S S-BPMN Business layer Implementation layer Business Ontology OWL-WS QoS Ontology Technical ontology GROS-OGSA IT workflowsServices Semantic bindingsGrid resources SLAs Business processes Organization of BREIN ontologies

7. 06/12/2015Page 7 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

8. 06/12/2015Page 8 Local interpretation of SA-SLA <wsla:SLAParameter name=“Capacity" type="double" unit="MB" sawsdl:modelReference="#RAMMemory"> memory …… slaParameter1 GB RAMMemory Capacity name unit type slaParameter1 GB RAMMemory Capacity name unit type SLO1 4 value slaParameter1 GB SLO1 4 value 4096 kB >= unit local value local unit

9. 06/12/2015Page 9 Evaluation of offers vs. bid SLO1 Bid1 Offer1 SLO2 Offer2 SLO3 SLO4 SLO5 SLO6 0.5 1.0

10. 06/12/2015Page 10 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

11. 06/12/2015Page 11 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?