الفهرس | Only 14 pages are availabe for public view |
Abstract Cloud computing (CC) has become one of the most required platforms in Information Technology (IT) due to providing inexpensive services with scalability and high availability. Most of large companies move toward cloud computing companies to host the platforms of different services due to their flexibility and scalability features. CC reacts faster to the needs of business and the resources can be scaled up/down according to the load beaks. The dynamic nature of the cloud computing systems makes scheduling of workflow tasks which is an important issue. Task scheduling is the allocation of the incoming tasks to the available resources virtual machines (VMs) aiming to reduce the overall time to finish the tasks. Task scheduling algorithms are broadly classified into two categories namely; static and dynamic task scheduling algorithms. In static task scheduling, all information regarding the state of the resources and the tasks are known before the execution. While in the dynamic scheduling, all information of the tasks are not known beforehand. Thus, execution time of the task may not be known, and the allocation of the tasks is done during run time. The main objective of this thesis is to develop an algorithm that schedules applications’ tasks of customers to the virtual machines of the cloud with the objective of minimizing both time and monetary costs. This objective represents a major challenge because of the competition between the two objectives. In this thesis, we presented evaluate a proposed algorithm, called Improved Cost Task Scheduling (ICTS) algorithm, for scheduling tasks in cloud computing environment. The algorithm considers minimizing both the time and monetary cost when using cloud resources. The algorithm contains three phases: level sorting, task-prioritizing and VM selection. from the experimental results, it is concluded that the ICTS algorithm provides an improvement in scheduling length as well as significant monetary cost saving. iii It minimizes make-span by about 28.94 % and decreasing the monetary cost by about 16.2 %. |