###### Abstract  
Cloud computing is an operation carried out via networks to provide resources and information to end users according to their demands. The job scheduling in cloud computing, which is distributed across numerous resources for large-scale calculation and resolves the value, accessibility, reliability, and capability of cloud computing, is important because of the high development of technology and the many layers of application. An extended and revised study was developed in our last work, titled \"Multi Objective Scheduling in Cloud Computing Using Multi-Objective Simplified Swarm Optimization MOSSO\" in IEEE CEC 2018. More new algorithms, testing, and comparisons have been implemented to solve the bi-objective time-constrained task scheduling problem in a more efficient manner. The job scheduling in cloud computing, with objectives including energy consumption and computing time, is solved by the newer algorithm developed in this study. The developed algorithm, named two-objective simplified swarm optimization (tSSO), revises and improves the errors in the previous MOSSO algorithm, which ignores the fact that the number of temporary nondominated solutions is not always only one in the multi-objective problem, and some temporary nondominated solutions may not be temporary nondominated solutions in the next generation based on simplified swarm optimization (SSO). The experimental results implemented show that the developed tSSO performs better than the best-known algorithms, including nondominated sorting genetic algorithm II (NSGA-II), multi-objective particle swarm optimization (MOPSO), and MOSSO in the convergence, diversity, number of obtained temporary nondominated solutions, and the number of obtained real nondominated solutions. The developed tSSO accomplishes the objective of this study, as proven by the experiments. e 2023 13 2007  
model of computing tasks includes cloud platforms, users, applications, virtual machines, etc. Within each cloud platform, there are multiple platform users, each with the ability to run multiple applications on the platform. Each application corresponds to the jobs requested by a user and uses a certain quota of virtual machines, through which the application finishes and returns the jobs, thus completing the procedure. Countless discussions and research have been conducted on the two prevailing issues in both grid and cloud computing: resource allocation and job scheduling [2; 3; 4]. The job scheduling problem revolves around exploring how the provider of cloud computing services assigns the jobs of each client to each processor according to certain rules and regulations to ensure the cost-effectiveness of the job scheduling process. The efficiency and performance of cloud computing services are usually associated with the efficiency of job scheduling, which affects not only the performance efficiency of users' jobs but also the utilization efficiency of system resources. Hence, the interdependent relationship between the efficiency and performance of cloud computing services and the efficiency of job scheduling necessitates research on the job scheduling problem of the computing tasks of cloud computing. The job scheduling problem of the computing tasks of cloud computing services assign jobs to individual processors. It is an NP-hard combinatorial problem, which renders it difficult to obtain the global optima within polynomial time. The greater the problem size, e.g., the number of resources or jobs, the more difficult it is for traditional job scheduling algorithms to solve.