department of informatics

Developing a method to define application and infrastructure relationships using intuitionistic fuzzy sets


Service Level Agreements (SLAs) related to customer satisfaction or other front end measures (response time, wait time, correctness, etc.) of the composed service are often used to manage delivery contracts and have revenue impacts for service providers.
When setting up an infrastructure to deliver specific SLAs for a business application we need to translate back the metrics related to individual components of the service, like accuracy, responsiveness, uptime, etc. (which are in a sense backstage metrics) to the front stage experienced by the client or business. This is required to assess if the business service will meet the SLAs, decide on what resources to allocate to it, and perhaps choose between providers when selecting component services to be composed.  

The goal of this broader work is to assess the dependency and relationships of backend services to the SLAs of the end-user service (or composite service) and to identify the Quality of Services (QoS) measures impacted by these backend services.  Service administrators can then pro-actively track and verify by periodically polling the measures of individual services and gathering the overall quality status of the business service. This allows administrators responsible for the functioning of a service to monitor its quality based on the measurements typically already done for the infrastructure components.

Application discovery is the process of automatically analyzing artifacts of a software application and physical elements that constitute a network (e.g., servers, firewalls, etc.). Dependency mapping creates visibility between discovered applications and infrastructure dependencies. Automated application discovery and subsequent  dependency mapping, can capture, connect and unveil relationships including the way in which applications behave and relate to the technology architecture on which they rely. This process can result to an automatic discovery of complex business applications and dependencies which can be done e.g. through application templates created by operations after deployment or through application descriptors that are created at development and deployment time.

As result of this work the direct impact relationships (expressed via tightly and loosely coupling)    between services which belongs to a business application or service is now defined. Now, the Indirect coupling can be calculated instead of entered/maintained by the operator. The indirect coupling between components or services has to be calculated considering the probabilities for direct coupling.

Using the standard means by fuzzy logic we can consider now also the attitude, importance and mitigation capabilities when doing an impact analysis on probalistic breeches for service levels on frontstage services when a component is not working correctly. Different types of impact analysis involve the usage of classical or probabilistic variants of the logical operations conjunction and disjunction in calculation of indirect impacts. Depending on which combination of operations will be used, the indirect impacts may be greater or smaller. Three basic types of impact analysis are introduced: worst case (pessimistic), best case (optimistic) and moderate impact analyses.

The basic approach for the indirect impact calculation is described in Boyan Kolev, Ivaylo Ivanov : Fault Tree Analysis in an Intuitionistic Fuzzy Configuration Management, Database, Thirteenth Int. Conf. on IFSs, Sofia, 9-10 May 2009, NIFS Vol. 15 (2009) 2, 10-17.

 This work will include both, conceptual analysis (fuzzy logic and business related evaluation) as well as technical implementation aspects using the IBM SW product Tivoli Application Dependency Discovery Manager (TADDM).

Project Type: 
Positions Available: 

[Joshi et al. 2009] Joshi, Karuna; Joshi, Anupam; Yesha, Yelena; Kothari, Ravi: A Framework for Relating Frontstage and Backstage Quality in Virtualized Services. Available:, accessed 25thMarch 2012.

[Tai et al. 2008] Tai, Ling; Baker, Ron; Edmiston, Elizabeth; Jeffcoat, Ben: IBM Tivoli Common Data Model: Guide to Best Practices. Available:, accessed 25thMarch 2012.

[Schönefeld 1996] Schönefeld, Marc: Entwurf und Realisierung einer auf Fuzzy-Logik basierenden Entscheidungskomponente zur Integration in eine Workflow-Entwicklungsumgebung. Available:, accessed 25th March 2012.

 [Kolev/Ivanov 2009] Kolev, Boyan; Ivanov Ivaylo: Fault Tree Analysis in an Intuitionistic Fuzzy Configuration Management Database. Available:, accessed 25th March 2012.

 [Jacob et al. 2009] Jacob, Bart; Adhia, Bhavesh; Badr, Karim; Huang, Qing Chun; Lawrence, Carol S.; Marino, Martin; Unglaub-Lloyd, Petra: IBM Tivoli Application Dependency Discovery Manager: Capabilities and Best Practices. Available:, accessed: 25th March 2012.

 [Robinson/Buros 2007] Robinson, David; Buros, Karen: Using the Data Model to display data in TADDM.

Available:, accessed 25th March 2012.

 [Rinaldi et al. 2001] Rinaldi, Steven M.; Peerenboom, James P.; Kelly, Terrence K.: Identifying, Understanding, and Analyzing Critical Infrastructure Interdependencies. Available:, accessed 25th March 2012.

 Atanassov K. On Intuitionistic Fuzzy Sets Theory (Studies in Fuzziness and Soft Computing) Springer Berlin Heidelberg; 1st ed. 1999 Edition: 2010