SFL Algorithm for QoS-based Cloud Service Composition

Abstract

With the advent of cloud service-based applications and Software as a Service (SaaS), new applications have recently known an increasing use of service-oriented architecture (SOA). This model has allowed computer science and associated industries to build new customized applications, by using the available and the existing cloud services bridged together dynamically to form a complex workflow process with more functionalities. However cloud services with similar and compatible functionalities may be offered by multiple providers but may also be offered at different QoS levels. Hence, to build a composite service with a high QoS, a decision should be made based on end-to-end QoS. This work proposes a new approach, for QoS-aware cloud service composition, which addresses a universal model, with end-toend QoS. It also proposes an effective evolutionary method based on Shuffled Frog Leaping Algorithm (SFLA), which is satisfying global and local constraints. Therefore, in order to evaluate the robustness of the proposed approach, we have evaluated the impact of several parameters that are highly significant in evolutionary methods, such as the impact of the population size, number of candidate services per task and number of criteria. The experimental results show that the chosen algorithm performs better than the ones based on Genetic Algorithm (GA).

Bringing semantics to multicloud service compositions

“…Existing research studies' major focus was on reducing the number of hosting clouds and, consequently, the intercloud communication cost, in addition to the composition sequences' total execution time. To meet these goals, several studies have considered MCSC as a combinatorial optimization problem, like in References and . For this purpose, they have used different algorithms such as genetic algorithm (GA), ant colony optimization (ACO), particle swarm optimization (PSO), greedy algorithm, and so forth.…”

“…As for the adopted composition methods, we distinguish SFLA, skyline, gravitational attraction search, imperialist competitive algorithm, Petri net, FCA, game theory, and so forth. Combinatorial techniques, like in References , and , have also been applied, as the MCSC can be seen as an optimization problem. The greedy approach can offer an optimal solution, but it requires a high computation time .…”

“…For this reason, most of them proceed to reduce the search space by selecting the optimal combination of clouds, before making the service selection and composition. Other approaches have focused on optimizing the QoS levels of MCSCs, by considering attributes like execution time, network latency, and service location. However, these approaches totally ignore the environmental constraints that may affect the services involved in a composition.…”

“…Taking as an example the combination of several SaaS applications (horizontal composition), which will be aggregated later with some IaaS services (eg, VMs and storage servers) that are requested by the user (vertical composition). In the literature, most works have treated the horizontal service composition, whereas few attempts have addressed the vertical service composition . However, there are some few works that have treated both vertical and horizontal service compositions …”

“…In the literature, existing cloud service description approaches support some aspects and neglect others. For example, research studies in References , and are focalized on technical and operational aspects, but they neglect business and semantic ones. Works such as Reference have addressed only the business aspect.…”