Local martingale difference approach for service selection with dynamic QoS

A High Performance Random Forest Machine Learning Algorithm for QoS- Based Web Services Composition

“…In order to reduce high computational complexity, authors have created a reliable service selection algorithm with Markov's better decision-making process. In comparison, the LMD approach [27] does not need a pre-determined threshold nor does the distribution of the provider require accurate parameters.…”

Advances on QoS‐aware web service selection and composition with nature‐inspired computing

“…Aiming at reducing the high computation complexity, authors designed a reliable service selection algorithm using an improved Markov decision process. Differently, local martingale difference (LMD) approach [37] requires neither predetermined threshold nor accurate parameters of the providers’ distribution. LMD becomes the first self‐adaptable methodology in the domain since it makes the decision based on the dynamic information all gathered during the process of waiting, without any manual intervention.…”

An efficient deadlock prevention approach for service oriented transaction processing

“…With the success of SOA, m any large-scale inform at ion syst em s have been set up to provide business services sim ultaneously [2,3]. In SOA environments, the deadlock problem due to the resource competition among multiple (sub)transactions gets w orse because of t he randomicit y of transaction request s and t he uncont rollabilit y of transaction execution order [4,5]. The deadlock in SOA environments w ill occur more often than that in traditional distributed systems.…”