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This paper presents an iterative mathematical decision model for organizations to evaluate whether to invest in establishing information technology (IT) infrastructure on-premises or outsourcing IT services on a multicloud environment. This is because a single cloud cannot cover all types of users' functional/nonfunctional requirements, in addition to several drawbacks such as resource limitation, vendor lock-in, and prone to failure. On the other hand, multicloud brings several merits such as vendor lock-in avoidance, system fault tolerance, cost reduction, and better quality of service. The biggest challenge is in selecting an optimal web service composition in the ever increasing multicloud market in which each provider has its own pricing schemes and delivers variation in the service security level. In this regard, we embed a module in the cloud broker to log service downtime and different attacks to measure the security risk. If security tenets, namely, security service level agreement, such as availability, integrity, and confidentiality for mission-critical applications, are targeted by cybersecurity attacks, it causes disruption in business continuity, leading to financial losses or even business failure. To address this issue, our decision model extends the cost model by using the cost present value concept and the risk model by using the advanced mean failure cost concept, which are derived from the embedded module to quantify cloud competencies. Then, the cloud economic problem is transformed into a bioptimization problem, which minimizes cost and security risks simultaneously. To deal with the combinatorial problem, we extended a genetic algorithm to find a Pareto set of optimal solutions. To reach a concrete result and to illustrate the effectiveness of the decision model, we conducted different scenarios and a small-to-medium business IT development for a 5-year investment as a case study. The result of different implementation shows that multicloud is a promising and reliable solution against IT on-premises deployment.

Nowadays, fog computing as a complementary facility of cloud computing has attracted great attentions in research communities because it has extraordinary potential to provide resources and processing services requested for applications at the edge network near to users. Recent researchers focus on how efficiently engage edge networks capabilities for execution and supporting of IoT applications and associated requirement. However, inefficient deployment of applications’ components on fog computing infrastructure results bandwidth and resource wastage, maximum power consumption, and unpleasant quality of service (QoS) level. This paper considers reduction of bandwidth wastage in regards to application components dependency in their distributed deployment. On the other hand, the service reliability is declined if an application’s components are deployed on a single node for the sake of power consumption management viewpoint. Therefore, a mechanism for tackling single point of failure and application reliability enhancement against failure are presented. Then, the components deployment is formulated to a multi-objective optimization problem with minimization perspective of both power consumption and total latency between each pair of components associated to applications. To solve this combinatorial optimization problem, a multi-objective cuckoo search algorithm (MOCSA) is presented. To validate the work, this algorithm is assessed in different conditions against some state-of the arts. The simulation results prove the amount 42%, 29%, 46%, 13%, and 5% improvement of proposed MOCSA in terms of average overall latency respectively against MOGWO, MOGWO-I, MOPSO, MOBA, and NSGA-II algorithms. Also, in term of average total power consumption the improvement is about 43%, 28%, 41%, 30%, and 32% respectively.