Cloud computing has become an essential platform for business data storage and application management due to its scalability, accessibility, and cost-effectiveness. However, ensuring the security and privacy of sensitive cloud data remains a major challenge because cloud users do not have direct control over their stored information. Traditional single-biometric encryption approaches often suffer from issues such as biometric variability, spoofing risks, and single-point failure. To address these limitations, this paper proposes a multi-user multi-modal biometric encryption framework integrated with threshold-based access control for securing business data stored in cloud environments. In the proposed approach, fingerprint and audio biometric modalities are pre-processed to extract privacy-preserving feature vectors, which are fused to generate individual user-keys using a Hash-Based Key Derivation Function (HKDF). Subsequently, a master encryption key is generated through Shamir’s Secret Sharing and Lagrange interpolation mechanisms, where only a predefined threshold number of valid user-keys can reconstruct the master key for decryption. AES symmetric encryption is employed to secure the business data before cloud storage. Experimental evaluation was performed using the FVC2002 fingerprint dataset and Mozilla CV-Corpus audio dataset. The generated biometric master-key successfully passed Shannon Entropy, Chi-Square, Monte Carlo Pi, and decryption validation tests, demonstrating improved randomness, reliability, and resistance against unauthorized access. The proposed framework effectively eliminates single-point failure issues and enhances secure cloud data access through threshold-based biometric authentication.

Container-based virtualization has become prominent as lightweight virtualization due to its scalability, resource utilization, and portability, especially in microservices. Container scheduler plays an essential role in Container services to optimize performance to reduce the overall cost by managing load balancing. Although Containers serve a lot of benefits, resource allocation is one of the major concerns associated with Container technology. This paper systematically reviews the distinct scheduling techniques used for Containers in a cloud environment, where existing scheduling techniques and their shortcomings have been discussed in detail. In the review process, the various issues and challenges in the Container technology have been identified, and the same has been discussed in this paper. Based on crucial elements including different performance metrics like CPU utilization, memory utilization, load balancing, and many others, it gives an in-depth comparison of the existing scheduling techniques like Ant Colony Optimization(ACO), Particle Swarm Optimization(PSO), Bee Colony Optimization(BCO), Chicken Swarm Optimization(CSO) and Genetic Algorithm(GA) outlining their benefits and drawbacks. This study also proposes a hybrid framework for secure and efficient Container scheduling. This framework can be implemented in the future to provide better results compared to existing approaches.