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SLA-aware operational efficiency in AI-enabled service chains: challenges ahead

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Abstract

Service providers compose services in service chains that require deep integration of core operational information systems across organizations. Additionally, advanced analytics inform data-driven decision-making in corresponding AI-enabled business processes in today’s complex environments. However, individual partner engagements with service consumers and providers often entail individually negotiated, highly customized Service Level Agreements (SLAs) comprising engagement-specific metrics that semantically differ from general KPIs utilized on a broader operational (i.e., cross-client) level. Furthermore, the number of unique SLAs to be managed increases with the size of such service chains. The resulting complexity pushes large organizations to employ dedicated SLA management systems, but such ‘siloed’ approaches make it difficult to leverage insights from SLA evaluations and predictions for decision-making in core business processes, and vice versa. Consequently, simultaneous optimization for both global operational process efficiency and engagement-specific SLA compliance is hampered. To address these shortcomings, we propose our vision of supplying online, AI-supported SLA analytics to data-driven, intelligent core workflows of the enterprise and discuss current research challenges arising from this vision. Exemplified by two scenarios derived from real use cases in industry and public administration, we demonstrate the need for improved semantic alignment of heavily customized SLAs with AI-enabled operational systems. Moreover, we discuss specific challenges of prescriptive SLA analytics under multi-engagement SLA awareness and how the dual role of AI in such scenarios demands bidirectional data exchange between operational processes and SLA management. Finally, we discuss the implications of federating AI-supported SLA analytics across organizations.

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Notes

  1. The reader is referred to Sect. 2.2 for real-world examples of arbitrary notions of incident severity documented in current literature.

  2. See e.g., Engel et al. (2018) for an example of how different incident classes can be modeled in a formal SLA representation.

  3. ML models may inadvertently leak detailed information about training data (cf. Nasr et al. 2019).

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  1. Contributions to this article by R. Engel and A. Megahed have been carried out during employment at IBM Research - Almaden.

Authors and Affiliations

  1. IBM Research - Almaden, San Jose, CA, USA

    Robert Engel & Aly Megahed

  2. SCORE Lab., Universidad de Sevilla, Seville, Spain

    Pablo Fernandez, Antonio Ruiz-Cortes & Juan Ojeda-Perez

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  1. Robert Engel
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  2. Pablo Fernandez
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Correspondence to Robert Engel.

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Engel, R., Fernandez, P., Ruiz-Cortes, A. et al. SLA-aware operational efficiency in AI-enabled service chains: challenges ahead. Inf Syst E-Bus Manage 20, 199–221 (2022). https://doi.org/10.1007/s10257-022-00551-w

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