Cloud computing describes computer services provided through the internet and includes a wide range of virtualization resources. Because cloud computing is made up of a sizable number of heterogeneous autonomous systems with an adaptable computational architecture, it has been widely adopted by many businesses. The scheduling and management of resource utilization, however, have become more difficult as a result of cloud computing. Task scheduling is crucial, and this procedure must schedule tasks on the virtual machine while using the least amount of time possible. Utilizing an effective scheduling strategy enhances and expedites cloud computing services. Optimization techniques are used to resolve cloud scheduling problems. The purpose of this research is to address tasks distribution within the system to improve system performance overall and reduce task execution time. Two well-known optimization algorithms (the Bat, and Harmony search algorithms) were used in this approach as well as a combination approach Bat Algorithm Harmony Search ( BAHS ) that integrates the two. When compared to the other algorithms used for task scheduling, the (BAHS) method was chosen because it is flexible and produces effective results. Tests were run on a dataset that was created randomly. The suggested algorithm results were compared to the other popular algorithms in the field. The results show that the suggested swarm-based scheduling techniques can produce more accurate results than those of the competing algorithms in terms of the makespan , mean, and standard division.
The researcher studied transportation problem because it's great importance in the country's economy. This paper which ware studied several ways to find a solution closely to the optimization, has applied these methods to the practical reality by taking one oil derivatives which is benzene product, where the first purpose of this study is, how we can reduce the total costs of transportation for product of petrol from warehouses in the province of Baghdad, to some stations in the Karsh district and Rusafa in the same province. Secondly, how can we address the Domandes of each station by required quantity which is depending on absorptive capacity of the warehouses (quantities supply), And through r
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