Revenue maximization with quality assurance for composite web services

Abstract

Service composition is one of the major approaches in service oriented architecture (SOA) based systems. Due to the inherent stochastic nature of services execution environment the issue of composite service quality assurance within SOA is a very challenging one. Such heterogeneous environment requires dynamic, run-time composition of services. We show how to determine a policy that satisfies the quality assurance for the composite service provider with the aim of revenue maximization for this provider. The quality assurance is defined as the probability that end-to-end deadline will be met, while taking into account service availability, (composite) service response-time and costs. The calculated policy is determined using dynamic programming and allows fast decision making for run-time composition. Besides, we determine the end-to-end response-time distributions resulting from determined policies. We illustrate the proposed solution with a number of experiments.

Multicloud service composition: A survey of current approaches and issues

“…Several approaches have been proposed to solve the cloud service composition problem. These approaches are classified by Jula et al into 5 categories: the first one includes classic algorithms and graph‐based algorithms . The second one contains combinatorial algorithms .…”

“…These approaches are classified by Jula et al 1 into 5 categories: the first one includes classic algorithms and graph-based algorithms. 3,5,6,21,[28][29][30][31][32][33][34] The second one contains combinatorial algorithms. 7,8,[35][36][37] In the third one, machine-based algorithms are mentioned, 9 the forth class regroups structures, 38 and the last one concerns frameworks methods.…”

Evaluating the QoS Cognizance in Composition of Cloud Services: A Systematic Literature Review

“…Worm et al in [21] proposed a technique based on three decision criteria that aims to maximize the revenue and quality assurance. The concept of dynamic programming is utilized to evaluate the decision criteria based on execution cost, service availability at the particular instant of time.…”

“…QoS Parameters Considered Kofler et al (2009) [15] Response time (RT), Cost Zeng et al (2009) [16] Response time (RT), Availability (Avail), Reliability (Reli) Zhu et al (2012) [17] Response time (RT), Cost, Availability (Avail), Reputation (Reput) Liu et al (2012) [19] Cost, Throughput(Thr) [20] Trust, Maintainability (Maint), Function similarity (FS) Worm et al(2012) [21] Response time (RT), Cost, Availability (Avail) Ludwig et.al (2012) [22] Response time (RT), Cost, Availability (Avail), Reliability (Reli) Bao and Dou (2012) [23] Response time (RT), Cost, Reliability (Reli), Availability (Avail), Reputation (Reput) Qi and Bouguettaya (2013) [43]…”

An optimization algorithm for service composition based on an improved FOA

“…Thus, SC is known as an optimization problem. Some types of classic optimization decision methods, such as the Analytic Hierarchy Process (AHP) [18] , Linear Programming (LP) [19] , dynamic programming [20] , and TOPSIS [21] , can be used to solve this problem. In general, for generating optimized service composition using this method, a several phase or multi-layer hierarchical structure should be divided.…”