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A meta-pattern for building QoS-optimal mobile services out of equivalent microservices

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Abstract

A QoS-optimal service balances reliability, execution cost, and latency to satisfy application requirements. In emerging distributed environments, with their unreliable and resource-scarce mobile/IoT devices, it is hard but essential to optimize the QoS of mobile services. Fortunately, these environments are characterized by ever-growing equivalent functionalities that satisfy the same requirements by different means. The combined execution of equivalent microservices has been used to improve QoS (e.g., majority voting for accuracy, speculative parallelism for latency, and failover for reliability). These executions are commonly described as workflow patterns, crude-grained recurring interactions across microservices within a service. However, as the number of equivalent microservices grows, applying a crude-grained pattern may cause severely unbalanced QoS, while nesting these patterns is convoluted to implement and expensive to maintain. In this article, we introduce a novel workflow meta-pattern for defining fine-grained workflow patterns that describe QoS-optimal combined executions of equivalent microservices. The meta-pattern employs a domain-specific algebraic expression to specify the invocation sequences of equivalent microservices, and a Boolean function to determine whether to terminate the execution. To evaluate the applicability of our meta-pattern, we build a Scala functional programming library, by which we further develop edge computing and cognitive service applications. Our experiments show that applying our meta-pattern to define such workflow patterns saves programmer effort, while the resulting patterns effectively improve the QoS of distributed applications.

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Notes

  1. https://www.ibm.com/watson/services/visual-recognition/.

  2. https://azure.microsoft.com/en-us/services/cognitive-services/face/.

  3. https://www.faceplusplus.com/face-detection/.

  4. https://github.com/ageitgey/face_recognition.

  5. https://docs.opencv.org/3.4/d1/de5/classcv_1_1CascadeClassifier.html.

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Acknowledgements

This research is supported by NSF through Grants #2104337, and #2232565.

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Authors and Affiliations

  1. Department of Computer and Information Science, University of Michigan at Dearborn, Dearborn, MI, 48128, USA

    Zheng Song & Zhengquan Li

  2. Department of Computer Science, Virginia Tech, Blacksburg, VA, 24061, USA

    Eli Tilevich

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  1. Zheng Song
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  2. Zhengquan Li
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Correspondence to Zheng Song.

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Song, Z., Li, Z. & Tilevich, E. A meta-pattern for building QoS-optimal mobile services out of equivalent microservices. SOCA 18, 109–120 (2024). https://doi.org/10.1007/s11761-024-00391-1

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