Cloud computing has gained widespread recognition for facilitating myriad online services and applications. However, the current stages of commercial cloud computing employ a moderate design, wherein computational resources like storage and servers are housed in a few sizable worldwide data centers. System reliability, efficiency, and low latency are all goals of virtual machine (VM) placement. Load balancing has emerged as a crucial challenge for attaining energy efficiency in a fictitious grid computing architecture where a variety of users’ workloads are distributed across several virtual machines. We propose a more effective optimization technique known as the twin fold moth flame algorithm. This algorithm considers multiple constraints, including computation time, stability, and placement cost. The proposed model’s effectiveness will be evaluated based on relocation costs, reaction times, and stability assessments. The most significant gains of the presented work are 4.24%, 9.73%, 11.10%, 28.83%, 7.63%, and 10.62% for 20 count data of nodes for artificial bee colony–bat algorithm, ant colony optimization, crow search algorithm, krill herd, whale optimization genetic algorithm, and improved Lévy-based whale optimization algorithm, respectively.

In the contemporary landscape of advanced technological ecosystems, the ubiquity of cloud computing serves as a cornerstone for orchestrating and maintaining a myriad of global datasets, information repositories, and service infrastructures. This widespread adoption of cloud computing, however, has brought forth a pressing need to confront and mitigate security challenges, necessitating the formulation of resilient protocols to safeguard the sanctity and confidentiality of data. In response to this imperative, our research endeavors to propose an innovative and secure paradigm for cloud data access, harnessing the synergies of attribute-based cryptography (ABC) and quantum key distribution (QKD). This methodology capitalizes on the intrinsic security of quantum channels to facilitate the secure transmission of cryptographic keys across nodes. We validate the effectiveness of our model through extensive simulations. This comparison, which is based on state-of-the-art methods, looks at a wide range of performance metrics, such as the time it takes to create a key, the time it takes to decrypt and encrypt, and other important factors. Compared to state-of-the-art algorithms, our proposed algorithm performs better with better encoding times (2.6s to 5.2s) and decryption timings (1.2s to 3.6s). While QKD-ABC regularly obtains the shortest analysis times (29.1s to 149.1s), it is clear that QKD-ABC is the best method for safe cloud data access. The particular numerical metrics demonstrate the unmatched performance of QKD-ABC in a variety of activities.

In the age of big data, artificial intelligence (AI) algorithms play an important role in demonstrating the value of data, but its negative effects are increasingly prominent. Researchers focus more on the obvious issues like privacy protection these days, and we assume that technologies like AI will make people wiser. But due to the not-so-obvious limits of AI, it may push us to the opposite side–artificial unintelligence. In this paper, a detailed analysis of the process of value creation from data is presented first. Then, based on the above process, the role played by AI algorithms is analyzed. Next, the anti-intelligence features of AI algorithms, such as the tendency of ‘fatuous King’, the generalization of quick thinking, a faster transformation of master/slave at the individual level, and retribalization at the group level, are deeply discussed. Finally, some possilbe ways of circumventing the aforementioned problems are offered.