# QoS-Based Pattern Recognition Approach for Web Service Discovery: Ar_WSDS
## 2 Background and Related Work  
### Web Service Composition  
Service composition-oriented systems are widely linked to cloud computing. Beyond analyzing the cloud paradigm as a deployment platform, a series of relevant performance and accessibility requirements is established when selecting services. A cloud service and composition lookup proposal has been presented in [9], based on the Cuckoo lookup algorithm through constraint-satisfaction techniques. Its test scenarios were based on distributed cloud environments with WS replicas having different QoS values. This paper provided insight into the use of multi-clouds with their respective data sets, highlighting the use of filters for the parsing process and for the functional part of each candidate WS to be selected by the composition model to be satisfied. Likewise, in [10], a pattern-based selection method using a composition and ontology-level search has been presented. The WS formalization was highlighted, in this work, in terms of enabling the accurate description of its interfaces and parameters, circumstantially streamlining the WS selection process at the syntactic level. In [11], a method to discover and select services using machine learning has been proposed. The system predicts new QoS values from information found in its web service description language (WSDL). Its analysis was based on obtaining data directly from the WSDL, as it describes how to compose a web service request and describes the interface provided by the provider to consume it. The analysis allows for filtering those WSs that are available at a given time. Research that developed the notion of pattern discovery and classifications in WS has been proposed in [12]. The authors used data mining techniques to classify services through specific non-functional requirements. Their study concluded that the main points of analysis in a WS are the response time, performance, and availability, which are critical metrics for a composition activity. In [13], a service discovery method based on Petri nets was used, developing a whole composition system modeled on a cluster of services in real-time. Its system provides the service with a semantic level, which allows for labeling it with information from the analysis of its QoS values, in order to create groupings of services according to a particular non-functional requirement. In [14], an algorithm has been presented for WS selection in repositories containing a large number of services. Their computational strategy was based on the theory of equivalence relations, and they modeled a system for storing services that avoids redundancy in its registration semantics. Their experiments were based on sequential composition models, where each service matches or connects to its predecessor service. [PERSON] et al. [4] have emphasized the composition problem in cloud environments at the WS selection time with the description of scenarios with replicas. An algorithm and mathematical model were developed using differential evolution-based knowledge-learning heuristics, based on searches on three QoS criteria: Response time, service availability, and reliability. The testing scenarios used random data to verify the performance of the algorithm. This aspect is essential, as data repositories in this field are scarce, and many researchers have chosen to create their own data sets. [PERSON] et al. [15] proposed analyses on different data sets, presenting the main parameters considered for the selection of WS, including the response time, latency, and reliability. They also described the importance of conducting studies on QoS parameters which can impact the cloud environment. [PERSON] et al. [16] have developed an algorithm called OAEQoS. Its important contribution consists of the analysis of web services by monitoring QoS parameters performed through the access of information to their providers, a concept which is important when considering the type of repository to choose for the search of services.