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The present study introduces a mechanism design theory framework to address the recruitment problem. Moreover, the study examines the features of the mechanism for the payment function based on value engineering. A two-stage model is also proposed for the mechanism based on the investigation of the initial documentation of the workers at the time of recruitment and their performance over the probationary period. The mechanism takes advantage of the concept of value engineering to account for the critical factors in selecting a firm for collaboration from a worker’s perspective, such as wage and subjective preferences. Various features of the mechanism, such as incentive compatibility (honesty) of the worker and the firm, the individual rationality of the worker and the firm, and the ex-post efficiency of the mechanism were investigated based on the proposed wage by introducing the utility function of each agent. Implementing recruitment in the form of a mechanism revealed that value engineering based pricing is a weighted VCG mechanism, and one of the factors in value engineering success would thus be the practical implementation of an optimized model. Examining recruitment in the simple framework of the mechanism alongside the introduction of the proper structure to study the influence of various features of the worker and the firm indicated that it is possible to implement more complex models such as payment function change, message exchange, and release of some assumptions regarding the workers and the firm.

In recent years, researchers have introduced many different mechanisms to improve resource allocation in the cloud. One of these resource allocation methods is market-based resource allocation which exploits different models used in exchanging goods and services. In this research, a two-way auction model is used for allocating cloud resources based on the market model. In the case of federated clouds, as the providers may face a shortage of resources during their operation; therefore, the continuous double auction model is suggested to create a cloud federation environment to support a suitable resource allocation among different providers. In our experiment 1, fixed pricing with Reputation-Aware Continuous Double Auction, Continuous Double Auction, and Market-Driven Continuous Double Auction models will be executed for resource allocation. It shows that both the resource efficiency and the income of the providers are improved in the federated clouds using these models. In experiment 2, with changing the type and number of the requested resources by customers and providers, the proposed federated model is also tested. The results of the experiments show that our proposed model for implementing federated clouds based on the continuous double auction model, in terms of successful allocation rates, resource efficiency and provider revenue, is better than other marketoriented models.

The use of cloud computing to implement business processes is becoming increasingly important because users can benefit from the economic and technical advantages of this technology. The concept of the Business Process as a Service (BPaaS) is a new solution in the use of specific business processes as a medium for aligning information technology and business. However, managing and deploying business processes on heterogeneous Cloud providers is still a challenge for organizations due to interoperability concerns. This paper suggests an algorithm for optimizing the resource allocation of the business process in Extended Federated BPaaS model in accordance with the requirements of the user’s policy. The developed model has been compared with other popular models supporting the service/business process policy and shows that the proposed model can effectively execute business processes with regards to infrastructure and data transfer costs. To evaluate the model, we implemented this algorithm on WorkflowSim tool, taking into account the challenges of data transfer in the cloud federation environment. The results of the experiments highlight the algorithm’s efficiency compared with initial deployment in a way that the proposed optimization mechanism reduces the cost of transferring data between clouds and can improve performance of workflow execution in cloud federation in terms of makespan time and cost