With the rapid growth of the Internet of Things (IoT), traditional infrastructures have evolved into intelligent systems that generate large volumes of dynamic and heterogeneous data, significantly increasing the need for efficient processing, analysis, and storage. Transmitting such data to traditional cloud data centers has led to challenges such as network congestion, increased latency, and reduced Quality of Service (QoS). In response, fog computing has emerged as a novel distributed architecture that brings computational resources closer to the network edge and end‐users. One of the key challenges in this domain is the optimal placement of services within the fog environment in a way that optimizes execution time, QoS, and reliability. In this study, we propose a smart improved version of the imperialist competitive algorithm (ICA), named improved imperialist competitive algorithm service placement (IICASP), which leverages adaptive search mechanisms, dynamic allocation strategies, and revolution rate control based on problem‐specific conditions to optimize the service placement process in fog computing environments. The performance of this algorithm is evaluated against various versions of ICA, hybrid algorithms such as genetic algorithm (GA)–PSO, and other metaheuristic approaches. Evaluations conducted across scenarios with varying numbers of tasks and fog nodes demonstrate that IICASP significantly outperforms other methods in most performance indicators. Experimental results show that compared with the standard ICA, IICASP reduces total execution time by 35.2% and decreases the number of failed services by 50%. Additionally, compared with the GA, it achieves a 46.6% improvement in execution time and a 66.6% reduction in the service failure rate. The proposed algorithm not only executes the highest number of successful services but also proves robust in maintaining performance stability under high‐load conditions. Finally, despite certain limitations—such as the assumption of static resources and the omission of task migration—the paper proposes future research directions aimed at enhancing intelligence and adaptability in resource allocation for fog computing environments.

ABSTRACT Cloud computing offers various services, with web services distinguished by their non‐functional attributes like implementation cost, reliability, and availability, which affect solution quality. Combining services to achieve optimal quality is an NP‐hard problem, best tackled with meta‐heuristic algorithms. A review of the literature identified 61 papers from authentic and well‐reputed publications addressing web service composition in cloud environments via such algorithms. This review categorizes these algorithms, evaluated implementation methods, techniques, criteria, and limitations, and compares their effectiveness. Furthermore, it outlines future research directions to enhance solutions for the web service composition challenge in cloud settings.

Abstract Cloud computing is utilised for information technology outsourcing of either industries or organisations. There are several inhibitors and motivations related factors to determine whether one can embrace the cloud services or not. Therefore, this paper presents a holistic and flexible cloud decision framework by taking a wide spectrum weighted factors related to the acceptance or denial of the cloud services. To have sustainable decision and obviating the shortcomings of the existing approaches on the cloud service adoption, a deep understanding of organisation's business process requirements and cost implications is required. To utilise the proposed model, the functional and non‐functional requirements associated to the business process of an adopter organisation must be specified. To reach a concrete decision, a hybrid approach is applied by incorporating the analytic hierarchy process and Delphi methods to prevent subjective outcomes and to have diverse experiences at the same time. To support the decision model, some economic theories and Moore law are used. To verify the proposed model, a Telecommunication Company is considered as a case study for its 6‐year plan of investment. The simulation results of conducted scenarios for the mentioned mid‐scale case study prove that it is logical to establish on‐premises a private datacenter and utilising the hybrid deployment once it encounters abrupt burst of resource demand. Altogether, the proposed holistic model can be customised for different users with different scales.

Cloud computing became an inevitable information technology industry. Despite its several plus points such as economy of scale and rapid elasticity, it suffers from vendor lock-in, resource limitation and cybersecurity attacks in which it leads business discontinuity or even business failure. Multi-cloud, on the other hand, can be trustable paradigm to obviate obstacles such as aforesaid unpleasant features of a single cloud. One of the biggest challenges is to know which cloud is commensurate with user’s business process with regards to security objectives. To this end, the new method is presented to quantify the amount of cloud security risk (CSR) in regards to user’s business process. Therefore, in this paper, the web service composition problem is formulated to bi-objective optimisation problem with service cost and multi-cloud risk viewpoints in ever-increasing multi-cloud environment (MCE) in which each provider has its variable pricing policy and different security level. It is obviously an NP-Hard problem. To solve the combinatorial problem, we develop a bi-objective time-varying particle swarm optimisation (BOTV-PSO) algorithm. The parameters are tuned based on elapsed time so a good balance between exploration and exploitation is achieved. To illustrate the effectiveness of proposed algorithm, we defined several scenarios and compared the performance of proposed algorithm with multi-objective GA-based (MOGA) optimiser, a single objective genetic algorithm (SOGA) that only optimises cost function and neglects CSR, and multi-objective simulated annealing algorithm (MOSA). The experimental results showed the superiority of proposed BOTV-PSO against other approaches in terms of convergence, diversity, fitness, performance, and even scalability.

Edge computing extends cloud computing capabilities to the edge of the network, allowing for instance Internet-of-Things (IoT) applications to process computation more locally and thus more efficiently. We aim to minimize latency and delay in edge architectures. We focus on an advanced architectural setting that takes communication and processing delays into account in addition to an actual request execution time in a performance engineering scenario. Our architecture is based on multi-cluster edge layer with local independent edge node clusters. We argue that particle swarm optimisation as a bio-inspired optimisation approach is an ideal candidate for distributed load processing in semi-autonomous edge clusters for IoT management. By designing a controller and using a particle swarm optimization algorithm, we can demonstrate that processing and propagation delay and the end-to-end latency (i.e., total response time) can be optimized.

By advent of multi cloud computing, abundant web services are published by several providers to their world-wide users. Web service composition technology attracted a lot of attention for the sake of reduction in software development cost. In multi cloud environment (MCE), each atomic web service published by any cloud provider with same functionality has different price and quality of service (QoS). Each cybersecurity attack on security tenets can make business financial loss or even failure. Literature review in this ambit indicates that the current techniques seldom solve a mission-critical business process since majority of them pay attention only on QoS and network parameters and do not take security tenets into consideration. A bi-objective genetic optimization algorithm is presented in which it solves web service composition problem in MCE by cost and risk viewpoints. The result of implementations show that the proposed bi-objective genetic algorithm is sustainable against a single objective algorithm which minimizes only service costs and disregards security risks.

Summary 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.