ASD mutations in the ciliary gene CEP41 impact development of projection neurons and interneurons in a human cortical organoid model.

In recent years, the exponential growth of internet usage has significantly increased the demand for cloud computing services, which cater to a wide spectrum of users—ranging from individuals and small businesses to large enterprises. Cloud computing enables users to access computational resources on-demand through virtualization technology, which abstracts the complexity of underlying hardware and system configurations. As users continuously submit a large volume of tasks for execution, efficiently allocating these tasks to limited resources such as Virtual Machines (VMs) becomes a major challenge. Hence, task scheduling plays a vital role in ensuring effective resource utilization, reduced execution time, and enhanced system performance. One of the primary objectives of cloud task scheduling is to minimize the makespan, which refers to the total time required to execute all tasks on available VMs. In addition, task scheduling must ensure compliance with Service Level Agreements (SLAs) that define performance and reliability standards for end-users. Efficient scheduling also contributes to better computational risk management, supports market-based strategies, and improves customer-based service provisioning. Cloud service platforms allow organizations to scale dynamically by buying and selling computational resources like storage and processing power. Moreover, with the rise of green cloud computing, efforts are made to minimize energy consumption and reduce the number of active servers, thereby promoting sustainability. Given the NP-hard nature of the task scheduling problem, traditional optimization methods often fall short when dealing with large and dynamic environments. Consequently, heuristic and metaheuristic algorithms have gained popularity due to their ability to provide near-optimal solutions within a reasonable computational time frame. Over time, several such algorithms have been developed to enhance throughput, reduce energy consumption, optimize cost, and maintain system stability—making cloud computing highly attractive for both academic and industrial research. Initial efforts focused on classical optimization techniques such as Hill Climbing, Gradient Descent, Newton’s Method, and Lagrange Multipliers, which are effective for convex and continuous optimization problems. However, their applicability is limited in complex and non-linear scheduling scenarios. This led to the emergence of hybrid metaheuristic algorithms that combine the strengths of multiple optimization strategies to solve NP-complete scheduling problems more effectively. Swarm intelligence algorithms such as Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), and Whale Optimization Algorithm (WOA) are particularly suitable due to their robustness, scalability, and simplicity. These algorithms excel in exploration and exploitation of the search space and do not require detailed knowledge of the problem. For instance, WOA has demonstrated superior performance over existing metaheuristics across various benchmark and structural optimization problems.