The concept of cloud computing has completely changed how computational resources are delivered and used. By enabling on-demand access to collective computing resources through the internet. While this technological shift offers unparalleled flexibility, it also brings considerable challenges, especially in scheduling and resource allocation, particularly when optimizing multiple objectives in a dynamic environment. Efficient allocation and scheduling of resources are critical in cloud computing, as they directly impact system performance, resource utilization, and cost efficiency in dynamic and heterogeneous conditions. Existing approaches often face difficulties in balancing conflicting objectives, such as reducing task completion time while staying within budget constraints or minimizing energy consumption while maximizing resource utilization. As a result, many solutions fall short of optimal performance, leading to increased costs and degraded performance. This systematic literature review (SLR) focuses on research conducted between 2019 and 2023 on scheduling and resource allocation in cloud environment. Following preferred reporting items for systematic reviews and meta-analyses guidelines, the review ensures a transparent and replicable process by employing systematic inclusion criteria and minimizing bias. The review explores key concepts in resource management and classifies existing strategies into mathematical, heuristic, and hyper-heuristic approaches. It evaluates popular algorithms designed to optimize key metrics such as energy consumption, resource utilization, cost reduction, makespan minimization, and performance satisfaction. Through a comparative analysis, the SLR discusses the strengths and limitations of various resource management schemes and identifies emerging trends. It underscores a steady growth in research within this field, emphasizing the importance of developing efficient allocation strategies to address the complexities of modern cloud systems. The findings provide a comprehensive overview of current methodologies and pave the way for future research aimed at tackling unresolved challenges in cloud computing resource management. This work serves as a valuable resource for practitioners and academics seeking to optimize scheduling and allocation in dynamic cloud environments, contributing to advancements in resource management strategies of cloud computing.

This paper develops and analyzes 6-DOF robotic arm kinetic models. The planned model permits skylights to be controlled to achieve any position and direction ac

For the first time, dual-performance perfection technologies were used to kinematically operate sophisticated robots. In this study, the trajectory development of a robot arm is optimized using a dual-performance perfection technique. The proposed approach alters the robot arm's Kinematics by creating virtual points even if the robotic system is not redundant to make it kinematically suitable for biomedical applications. In the suggested method, an appropriate objective function is chosen to raise one or maybe more performance measures while lowering one or more kinematic characteristics of a robot arm. The robot arm's end effector is set in place at the crucial locations, and the dual performance precision algorithm changes the joints and virtual points due to the robot arm's self-motion. As a result, the ideal values for the virtual points are established, and the robot arm's design is changed. Accordingly, this method's ability to visualize modifications made to the processor's design during the optimization problem is one of its benefits. The active robotic arm is used as a case study in this article. The task is defined as choosing the best path based on the input target's position and direction and is used in X-ray robot systems. The outcomes demonstrate the viability of the suggested approach and can serve as a useful prototype for an intelligent X-ray robot.

In cloud computing environments, the representation and management of data through workflows are crucial to ensuring efficient processing. This paper focuses on securing scientific workflow scheduling, which involves executing complex data-processing tasks with specific dependencies. The security of intermediate data, often transmitted between virtual machines during workflow execution, is critical for maintaining the integrity and confidentiality of scientific workflows. This review analyzes methods for securing scientific workflow scheduling in cloud environments, emphasizing the application of security principles such as confidentiality, authentication, and integrity. Various scheduling algorithms, including heuristics and metaheuristics, are examined for their effectiveness in balancing security with constraints like execution time and cost.

Over the last decade, many nature-inspired algorithms have been received considerable attention among practitioners and researchers to handle several optimization problems. Lion optimization algorithm (LA) is inspired by a distinctive lifestyle of lions and their collective behavior in their social groups. LA has been presented as a powerful optimization algorithm to solve various optimization problems. In this paper, the LA is proposed to investigate its performance in solving one of the most popular and widespread real-life optimization problems called team orienteering problem with time windows (TOPTW). However, as any population-based metaheuristic, the LA is very efficient in exploring the search space, but inefficient in exploiting it. So, this paper proposes enhancing LA to tackle the TOPTW by utilizing its strong ability to explore the search space and improving its exploitation ability. This enhancement is achieved via improving a process of territorial defense to generate a trespass strong nomadic lion to prevail a pride by fighting its males. As a result of this improving process, an enhanced LA (ILA) emerged. The obtained solutions have been compared with the best known and standard results obtained in the former studies. The conducted experimental test verifies the effectiveness of the ILA in solving the TOPTW as it obtained a very competitive results compared to the LA and the state-of-the-art methods across all tested instances.

Observation has been made that in Arab countries, students find it challenging to learn to program in a different language other than Arabic. Existing literature contains only a few Arabic programming languages, most of which have never been or have partially been executed. In this article, a novel Arabic programming language known as Alf..Eih language is proposed. The proposed programming language is to be used in teaching pupils and schoolchildren in Arabic countries programming. This Arabic Programming language converts the Arabic language presumed to code in (C++ language) with its implementation. After the code written in Arabic passes through the first three stages of compiler (lexical, syntax and semantic), the final phase converts code to (C++). Alf..Eih language covers many of the directives in the presumed Arabic Language such as, header file, functions, if condition, loops and etc.