Exploiting Equivalence to Efficiently Enhance the Accuracy of Cognitive Services Equivalent services deliver the same functionality with dissimilar nonfunctional characteristics, including latency, accuracy, and cost. With these dissimilarities in mind, developers can exploit the combined execution of equivalent services to increase accuracy, shorten latency, or reduce cost. However, it remains unknown how to effectively combine equivalent services to satisfy application requirements. With the recent surge in popularity of machine learning, different vendors offer a plethora of equivalent services, whose characteristics are mostly undocumented. As a result, developers cannot make an informed decision about which service to select from a set of equivalent services. To address this problem, we explore different service combination strategies (i.e., majority voting, weightedmajority voting, stacking, and custom) to ascertain their impact on nonfunctional characteristics. In particular, we study how these strategies impact the accuracy, cost, and latency of the face detection task and validate our findings on the sentiment analysis task. We consider the combined executions of commercial web services, deployed in the cloud, and opensource implementations, deployed as edge services. Our evaluation reveals that the combined execution of equivalent services is most effective for improving cost and latency. Informed by our experimental results, we formulate practical guidelines to help developers identify the best execution strategy for a given set of services.

Integrating Reinforcement Learning and Skyline Computing for Adaptive Service Composition Abstract   In service computing, combining multiple services through service composition to address complex user requirements has become a popular research topic. QoSaware service composition aims to find the optimal composition scheme with the QoS attributes that best match user requirements. However, certain QoS attributes may continuously change in a dynamic service environment, so service composition methods need to be adaptive. Furthermore, the large number of candidate services poses a key challenge for service composition, where existing service composition approaches based on reinforcement learning (RL) suffer from low efficiency. To deal with the problems above, in this paper, a new service composition approach is proposed which combines RL with skyline computing where the latter is used for reducing the search space and computational complexity. A WSCMDP model is proposed to solve the largescale service composition within a dynamically changing environment. To verify the proposed method, a series of comparative experiments are conducted, and the experimental results demonstrate the effectiveness, scalability and adaptability of the proposed approach.