Service functionality can be provided by more than one service consumer. In order to choose the service which creates the most benefit before its consumption, a selection based on previous measurable experiences by other consumers is beneficial. In this paper, we present the results of our analysis of two machine learning approaches to predict the best service within this selection problem. The first approach focuses on classification, predicting the best performing service, while the second approach focuses on regression, predicting service performances which can then be used for the determination of the best candidate. We assessed and compared both approaches for service recommendation w.r.t. The performance gain when selecting the recommended instead of a random service. Our evaluation is based on data measured on real Web services as well as on simulated data. The latter is needed for a more profound analysis of the strengths and weaknesses of each approach. The simulated data has similar statistical properties as the data measured on real Web services. In the real-world case, regression achieved a response time gain of over 92% of the optimum and classification over 83%. In case of simulated data, we could achieve an overall gain of up to 95% using classification, while regression achieved 89%.

Classification vs. Regression - Machine Learning Approaches for Service Recommendation Based on Measured Consumer Experiences

Cloud computing, mobile computing, Service-Oriented Computing (SOC), and Software as a Service (SaaS) indicate that the Internet emerges to an anonymous service market where service functionality c ...

/pdf/context-aware-optimized-service-selection-with-focus-on-4a2vwhn4bg.pdf

Context-Aware Optimized Service Selection with Focus on Consumer Preferences

Among functionally similar services, service consumersare interested in the consumption of the service that performsbest towards their optimization preferences. The experiencedperformance of a serv ...

https://www.thinkmind.org/articles/intsys_v8_n34_2015_11.pdf

Service Recommendation Using Machine Learning Methods Based on Measured Consumer Experiences Within a Service Market

In future service markets a selection of functionally equal services is omnipresent. The evolving challenge, finding the best-fit service, requires a distinction between the non-functional service characteristics (e.g., response time, price, availability). Service providers commonly capture those quality characteristics in so-called Service Level Agreements (SLAs). However, a service selection based on SLAs is inadequate, because the static SLAs generally do not consider the dynamic service behaviors and quality changes in a service-oriented environment. Furthermore, the profit-oriented service providers tend to embellish their SLAs by flexibly handling their correctness. Within the SOC (Service Oriented Computing) research project of the Karlsruhe University of Applied Sciences and the Linnaeus University of Sweden, a service broker framework for an optimized web service selection is introduced. Instead of relying on the providers’ quality assertions, a distributed knowledge is developed by automatically monitoring and measuring the service quality during each service consumption. The broker aims at optimizing the service selection based on the past real service performances and the defined quality preferences of a unique consumer.This thesis work concerns the design, implementation and evaluation of appropriate machine learning methods with focus on the broker’s best-fit web service selection. Within the time-critical service optimization the performance and scalability of the broker’s machine learning plays an important role. Therefore, high- performance algorithms for predicting the future non-functional service characteristics within a continuous machine learning process were implemented. The introduced so-called foreground-/background-model enables to separate the real-time request for a best-fit service selection from the time-consuming machine learning. The best-fit services for certain consumer call contexts (e.g., call location and time, quality preferences) are continuously pre-determined within the asynchronous background-model. Through this any performance issues within the critical path from the service request up to the best-fit service recommendation are eliminated. For evaluating the implemented best-fit service selection a sophisticated test data scenario with real-world characteristics was created showing services with different volatile performances, cyclic performance behaviors and performance changes in the course of time. Besides the significantly improved performance, the new implementation achieved an overall high selection accuracy. It was possible to determine in 70% of all service optimizations the actual best-fit service and in 94% of all service optimizations the actual two best-fit services.

/pdf/evaluation-and-implementation-of-machine-learning-methods-1xarfndd8u.pdf

Evaluation and Implementation of Machine Learning Methods for an Optimized Web Service Selection in a Future Service Market

This paper focuses on the Inverse Stochastic Optimal Control (ISOC) problem for linear-quadratic Gaussian (LQG) tracking control, which defines and solves the problem of identifying the weight matrices in the LQG cost function using input and output trajectory data in the presence of unknown process and observation noises. The proposed method first estimates the covariance matrices of the process and observation noises, which are then used as prior knowledge to identify the weight matrix. As a result, the paper solves the noise covariance matrices and the weight matrix in two sequential steps (rather than iteratively), leading to higher computational efficiency. The first step uses an EM algorithm with guaranteed convergence, and the second step is a convex optimization problem. Simulation results show that the proposed method significantly outperforms methods that cannot compensate for noise in terms of parameter estimation accuracy when unknown levels of process noise are present.

Inverse Stochastic Optimal Control for Linear-Quadratic Tracking

The actual experience of the performance of services at consumers’ side is a desirable foundation for service selection. Considering the knowledge of previous performance experiences from a consume ...

/pdf/appropriate-machine-learning-methods-for-service-5a3whi8qli.pdf

Appropriate machine learning methods for service recommendation based on measured consumer experiences within a service market

In this paper, we define and solve the Inverse Stochastic Optimal Control (ISOC) problem of the linear-quadratic Gaussian (LQG) and the linear-quadratic sensorimotor (LQS) control model. These Stochastic Optimal Control (SOC) models are state-of-the-art approaches describing human movements. The LQG ISOC problem consists of finding the unknown weighting matrices of the quadratic cost function and the covariance matrices of the additive Gaussian noise processes based on ground truth trajectories observed from the human in practice. The LQS ISOC problem aims at additionally finding the covariance matrices of the signal-dependent noise processes characteristic for the LQS model. We propose a solution to both ISOC problems which iteratively estimates cost function and covariance matrices via two bi-level optimizations. Simulation examples show the effectiveness of our developed algorithm. It finds parameters that yield trajectories matching mean and variance of the ground truth data.

Inverse Stochastic Optimal Control for Linear-Quadratic Gaussian and Linear-Quadratic Sensorimotor Control Models

Stochastic Optimal Control models represent the state-of-the-art in modeling goal-directed human movements. The linear-quadratic sensorimotor (LQS) model based on signal-dependent noise processes in state and output equation is the current main representative. With our newly introduced Inverse Stochastic Optimal Control algorithm building upon two bi-level optimizations, we can identify its unknown model parameters, namely cost function matrices and scaling parameters of the noise processes, for the first time. In this paper, we use this algorithm to identify the parameters of a deterministic linear-quadratic, a linear-quadratic Gaussian and a LQS model from human measurement data to compare the models' capability in describing goal-directed human movements. Human steering behavior in a simplified driving task shown to posses similar features as point-ot-point human hand reaching movements serves as our example movement. The results show that the identified LQS model outperforms the others with statistical significance. Particularly, the average human steering behavior is modeled significantly better by the LQS model. This validates the positive impact of signal-dependent noise processes on modeling human average behavior.

https://export.arxiv.org/pdf/2210.17303v2.pdf

Validation of Stochastic Optimal Control Models for Goal-Directed Human Movements on the Example of Human Driving Behavior

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Papers

Appropriate machine learning methods for service recommendation based on measured consumer experiences within a service market

Inverse Stochastic Optimal Control for Linear-Quadratic Gaussian and Linear-Quadratic Sensorimotor Control Models

Validation of Stochastic Optimal Control Models for Goal-Directed Human Movements on the Example of Human Driving Behavior