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The purpose of this paper is to formally verify the composition of web services to reduce inconsistencies in software architectures.

In order to check the web services composition, the authors use a model‐driven engineering (MDE)‐based approach and to achieve the formalization of web service composition in ACME and check the consistency of this composition, the authors introduce the pattern mediation to formalize web services composition with the ADL ACME, using the concept of architectural style of ACME. Subsequently, a scenario shows how this style can be used in ACMEStudio to detect inconsistencies. The example shows a web travel organization application.

The authors ensure reliability defined through non‐functional properties. To do so, use ACME was used to check assembling consistency of web service composition. In a second part, a SWC2ACME tool was designed and implemented to check if the web services meta‐model conforms to ACME model.

The paper describes a framework which has proven to be useful to ensure a safe design and execution of software architectures, specifically web services composition.

The cloud is a network of servers to share computing resources to run applications and data storage that offers services in various flavours, namely, infrastructure as a service, platform as a service and software as a service. The containers in the cloud are defined as “standalone and self-contained units that package software and its dependencies together”. Similar to virtual machines, the virtualization method facilitates the resource on a specific server that could be used by numerous appliances.

This study introduces a new Dragon Levy updated squirrel algorithm (DLU-SA) for container aware application scheduling. Furthermore, the solution of optimal resource allocation is attained via defining the objective function that considers certain criteria such as “total network distance (TND), system failure (SF), balanced cluster use (BC) and threshold distance (TD)”. Eventually, the supremacy of the presented model is confirmed over existing models in terms of cost and statistical analysis.

On observing the outcomes, the total cost of an adopted model for Experimentation 1 has attained a lesser cost value, and it was 0.97%, 10.45% and 10.37% superior to traditional velocity updated grey wolf (VU-GWO), squirrel search algorithm (SSA) and dragonfly algorithm (DA) models, respectively, for mean case scenario. Especially, under best case scenario, the implemented model has revealed a minimal cost value of 761.95, whereas, the compared models such as whale random update assisted lion algorithm, VU-GWO, SSA and DA has revealed higher cost value of 761.98, 779.46, 766.62 and 766.51, respectively. Thus, the enhancement of the developed model has been validated over the existing works.

This paper proposes a new DLU-SA for container aware application scheduling. This is the first work that uses the DLU-SA model for optimal container resource allocation by taking into consideration of certain constraints such as TND, SF, BC and TD.