QoS-Aware Selection of Web APIs Based on ε-Pareto Genetic Algorithm

Ma, Shang‐Pin; Lan, Ci-Wei; Ho, Ching-Ting; Ye, Jiun-Hau

doi:10.1109/ics.2016.0122

Cited by 4 publications

(5 citation statements)

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“…Sawczuk da Silva et al [25] use directed acyclic graphs as genotypes to increase computational efficiency. Papers [26], [33], and [34] present methods which results are Pareto-optimal for developer to select. Radwan et al in [28] presented a method that requires a listing of all risks and their impact and then they are optimized using the standard method.…”

Section: ) Genetic Algorithmsmentioning

confidence: 99%

Optimization of Business Process Execution in Services Architecture: A Systematic Literature Review

Górski

Woźniak

2021

IEEE Access

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Web services have become a standard way to provide functions of information systems. The number of web services grows rapidly with the increasing popularity of microservices architecture. In consequence, many business processes are executed entirely through web services. Therefore, optimizing the performance of business process execution may bring many benefits. There are many optimization methods in this area. Our systematic literature review aims to introduce available methods to researchers interested in the optimization of business process execution. We queried four databases: ACM, IEEE Xplore, Science Direct, and Springer. Out of 12150 initially found papers, we have selected 128 for the review. We have grouped methods presented in those papers into three stages of business process optimization: Resource Allocation, Service Composition, and Service Scheduling. Service Composition attracts the largest group of researchers with a vast majority of 119 articles in it. Moreover, the most popular are genetic algorithms. In general, researchers mainly propose heuristic methods that optimize business processes during run-time. We see the potential for further exploration at both Resource Allocation and Service Scheduling stages.

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Section: ) Genetic Algorithmsmentioning

confidence: 99%

Optimization of Business Process Execution in Services Architecture: A Systematic Literature Review

Górski

Woźniak

2021

IEEE Access

View full text Add to dashboard Cite

show abstract

“…where w 0 is the global bias, w i,j models the interaction between feature x i and x j , which is defined in Eq. (12).…”

Section: ) Model Buildingmentioning

confidence: 99%

“…  ee (12) where f denotes the number of latent factor, e i,k and e j,k denotes a specified latent factor of feature i and j respectively. It is possible that much higher-order interaction may generate much better results, but it is certain that this will increase the complexity of the model, and too complex models can hardly be applied to practical applications.…”

Section: ) Model Buildingmentioning

confidence: 99%

“…(11) AFM is a model that an attention part is added to FM [44]. (12) NFM is an optimized version of FM, in which a deep neural network is added to the end of FM in series for prediction [34,35]. (13) MLP is a deep model, in with embedding features are fed into the model to learn the nonlinear interaction between user and Web API and generate prediction [32].…”

Section: B Performance Comparison (Rq1)mentioning

confidence: 99%

“…To make appropriate Web API selection apart from functionality, quality of service (QoS) is then introduced for supporting high quality Web API selection and personalized Web API recommendation [12,13]. QoS is defined as a set of non-functional attributes of Web APIs, and each attribute represents the quality information of Web API in a certain aspect and has an attribute value, such as response time, throughput and cost [14].…”

Section: Introductionmentioning

confidence: 99%

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