Residue Number Systems (RNS) provide notable advantages in digital signal processing and error detection due to their inherent parallelism and fault tolerance. However, designing efficient reverse converters, particularly those based on the Chinese Remainder Theorem, remains a complex and labour-intensive. This paper presents an innovative automated methodology for developing simplified residue-to-binary converters by integrating Cartesian Genetic Programming (CGP) with Simulated Annealing (SA). The proposed approach employs a two-phase optimization framework. In the first phase, a hybrid CGP-SA algorithm generates module-level architectures by evolving computational structures using arithmetic and bitwise operations. Then, SA is then applied to optimize operator parameters, ensuring functional accuracy and enhanced efficiency. In the second phase, the high-level architecture is refined into an optimized gate-level design using adaptive evolutionary strategies to minimize latency, area, and power consumption. Simulation results demonstrate that the proposed framework consistently outperforms conventional handcrafted methods, offering improved computational efficiency, reduced hardware complexity, and greater design flexibility. This advancement provides a promising solution for practical, high-performance RNS reverse converter implementations.

Summary IOTA is a distributed ledger technology with a new structure called Tangle, which offers high scalability, no fees, and near‐instant transfers for the internet‐of‐things (IoT) networks. The most important issue of IOTA is consensus achievement, which is handled by a single node acting as a coordinator. A single and default coordinator node exposes IOTA to the single point of failure and incomplete distribution issues. In this paper, a novel algorithm for selecting multiple coordinators to participate in the consensus process at milestones is proposed (i.e., MCS algorithm) to overcome the problem of a single coordinator in IOTA network. The MCS algorithm is applied in a two‐layered architecture including IoT devices (i.e., Layer 1) and fog nodes (i.e., Layer 2). We define and formulate four different properties as metrics to select multiple coordinators in this architecture. Moreover, a collusion list is defined to decrease the risk of collusion between fog nodes in the network. Experimental results show that using multiple fog nodes as coordinators in IOTA network can improve the average response time and average throughput while does not considerably sacrifice the total cost and system utilization in comparison with the use of a single default coordinator in IOTA network.

Summary Ensuring the seamless operation of cloud computing services is paramount for meeting user demands and ensuring business continuity. Fault‐tolerant self‐healing techniques play a crucial role in enhancing the reliability and availability of cloud platforms, minimizing downtime and ensuring uninterrupted service delivery. This article systematically categorizes and analyzes existing research on fault‐tolerant self‐healing techniques published between 2005 and 2024. We provide a comprehensive technical taxonomy organizing self‐healing techniques based on fault tolerance processes, encompassing considerations for both reliability and availability. Additionally, we evaluate applications of proactive self‐healing techniques, highlighting their achievements, and limitations in enhancing service continuity. Strategies to address identified weaknesses are discussed, alongside future research challenges and open issues in the domain of cloud resilience. Through this analysis, the article contributes to understanding self‐healing techniques in cloud computing, offering insights into their effectiveness in ensuring service continuity. The findings aim to guide future research efforts in developing more robust and resilient cloud infrastructures, ultimately enhancing overall service reliability and availability. By emphasizing the importance of fault tolerance and self‐healing techniques, this article lays the foundation for advancing the state‐of‐the‐art in cloud computing.

Abstract The energy demand for Internet of Things (IoT) applications is increasing with a rise in IoT devices. Rising costs and energy demands can cause serious problems. Fog computing (FC) has recently emerged as a model for location‐aware tasks, data processing, fast computing, and energy consumption reduction. The Fog computing model assists cloud computing in fast processing at the network's edge, which also exerts a vital role in cloud computing. Due to the fast computing in fog servers, different quality of service (QoS) approaches have been proposed in various sections of the fog system, and several quality factors have been considered in this regard. Despite the significance of QoS in Fog computing, no extensive study has focused on QoS and energy consumption methods in this area. Therefore, this article investigates previous research on the use and guarantee of Fog computing. This article reviews six general approaches that discuss the published articles between 2015 and late May 2023. The focal point of this paper is evaluating Fog computing and the energy consumption strategy. This article further shows the advantages, disadvantages, tools, types of evaluation, and quality factors according to the selected approaches. Based on the reviewed studies, some open issues and challenges in Fog computing energy consumption management are suggested for further study.

Abstract Cloud systems have become an essential part of our daily lives owing to various Internet‐based services. Consequently, their energy utilization has also become a necessary concern in cloud computing systems increasingly. Live migration, including several virtual machines (VMs) packed on in minimal physical machines (PMs) as virtual machines consolidation (VMC) technique, is an approach to optimize power consumption. In this article, we have proposed an energy‐aware method for the VMC problem, which is called energy‐aware virtual machines consolidation (EVMC), to optimize the energy consumption regarding the quality of service guarantee, which comprises: (1) the support vector machine classification method based on the utilization rate of all resource of PMs that is used for PM detection in terms of the amount' load; (2) the modified minimization of migration approach which is used for VM selection; (3) the modified particle swarm optimization which is implemented for VM placement. Also, the evaluation of the functional requirements of the method is presented by the formal method and the non‐functional requirements by simulation. Finally, in contrast to the standard greedy algorithms such as modified best fit decreasing, the EVMC decreases the active PMs and migration of VMs, respectively, 30%, 50% on average. Also, it is more efficient for the energy 30% on average, resources and the balance degree 15% on average in the cloud.

Cloud computing offers a vast number of processing opportunities and heterogeneous resources and meets the requirements of numerous applications at various levels. Thus, the allocation and management of resources are vital in cloud computing. Resource allocation is a technique in which the available resources such as central processing unit, random-access memory, storage, and network bandwidth in cloud data centres are divided among users in a way that facilitates resource utilisation, provider profit, and user satisfaction. Integration and interaction with other modules of the resource management system, security, privacy, fairness, non-fragmentation of resources, resource utilisation, provider profit, user satisfaction, reducing energy consumption, load balancing, flexibility, scalability, availability, improvement the number and time of virtual machine migrations, and the number of overloaded resources are considered as challenges for the resource allocation mechanism. A systematic resource allocation survey with innovations in resource management system architecture, categorising mechanisms, addressing the challenges, and issues is presented. In addition to introducing the existing resource allocation mechanisms, other similar survey papers have been reviewed. Finally, there are some suggested topics for future work.

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.