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Scheduling of resources in cloud environments requires design of multiple pattern analysis models that include but are not limited to, inter‐task dependency pattern analysis, make‐span pattern analysis, virtual machine (VM or resource) based capacity analysis, deadline analysis, and VM‐to‐task compatibility analysis. Existing scheduling models are either highly complex, or do not integrate comprehensive analysis modules for efficient scheduling of resources to tasks. Moreover, some of these models showcase limited scalability when applied to large‐scale deployment scenarios. To overcome these issues, this text proposes design of a multimodal bioinspired model to improve resource‐scheduling efficiency with differential task‐level constraints. The proposed model initially collects multimodal information sets about tasks and underlying resources in order to augment analysis efficiency for different task types. These information sets are initially processed by a Grey Wolf Optimization (GWO)‐based scheduling model that assists in grouping tasks based on their make‐span, deadline and dependency levels. The grouped tasks are then scheduled via an incremental learning Elephant Herding Optimization (EHO) model that assists in assigning grouped tasks to capacity‐tuned resources (or VMs). Due to integration of these optimization methods, the proposed model is capable of improving the efficiency of resource scheduling by 8.5%, while reducing computational complexity by 4.3%, while improving the deadline hit ratio by 5.9%, and lowering energy consumption by 1.5% when compared with standard machine learning based scheduling techniques. Due to which the proposed model is capable of deployment for a wide variety of real‐time scheduling scenarios. [ABSTRACT FROM AUTHOR]

In the realm of blockchain technology, the quest for scalable and high-quality-of-service (QoS) systems remains a formidable challenge. Existing blockchain models often grapple with trade-offs between efficiency, speed, and security, particularly in the face of growing demands for energy-efficient and swift data processing. This paper introduces an innovative approach to address these limitations, leveraging Q Learning for context-based shard management, coupled with Elephant Herd Fish Swarm Optimization (EHFSO) process. This novel combination is specifically engineered to dynamically select hashing and encryption techniques tailored to each context-based shard, thereby enhancing the overall performance of blockchain systems. The proposed model marks a significant departure from traditional blockchain architectures. By integrating Q Learning, the system intelligently adapts to varying data contexts, ensuring optimal shard management operations. The EHFSO algorithm, drawing inspiration from natural swarm behavior, further refines the selection process for hashing and encryption techniques. This dual approach not only fortifies security but also augments efficiency levels. The practical efficacy of this model is underscored by its performance on multiple medical datasets & its samples. The results are compelling: an 8.5% improvement in mining energy efficiency, a 4.9% increase in mining speed, 5.9% higher throughput, 4.5% more consistent mining, and a noteworthy 5.9% reduction in storage costs compared to existing methods. Primarily, the work paves the way for more sustainable and efficient blockchain operations, particularly crucial in energy-sensitive sectors. Additionally, the enhanced throughput and mining consistency significantly improve the blockchain's applicability in real-world scenarios, where speed and reliability are paramount in real-time use cases. This research not only addresses the current limitations of blockchain technology but also sets a new benchmark for future developments in this field for different scenarios. [ABSTRACT FROM AUTHOR]