{'fraction_non_alphanumeric': 0.019796380090497737, 'fraction_numerical': 0.0, 'mean_word_length': 4.857615894039735, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
A Dynamic Task Scheduling Algorithm for Cloud Computing Environment



Cloud computing environment is a novel paradigm in which the services
are hosted, delivered and managed over the internet. Tasks scheduling problem in the cloud has become
a very interesting research area. However, the problem is more complex and challenging due
to the dynamic nature of cloud and users’ needs as well as cloud providers’ requirements including
the quality of service, users’ priorities and computing capabilities.



The main objective is to solve the problem of tasks scheduling through an algorithm
which can not only improves the client satisfaction, but also allows cloud service provider to gain
maximum profit and ensure that the cloud resources are utilized efficiently.
Method: (a) Optimization of the waiting time and the queue length.



(a) Optimization of the waiting time and the queue length.





(b) Distribution of all requests into a novel queueing system in a dynamic manner based on a decision
threshold.





(c) Assignment of requests to VMs based on Particle Swarm Optimization and Simulated Annealing
algorithms.





(d) Incorporation of the priority constraint in the scheduling process by considering three priorities
levels including the tasks, queues and VMs.



The results comparison of our algorithm with particle swarm optimization and First Come
First Serve algorithms demonstrate the effectiveness of our algorithm in terms of waiting time,
makespan, resources utilization and degree of imbalance.



This study introduces an efficient strategy to schedule users’ tasks by using dynamic
dispatch queues and particle swarm optimization with simulated annealing algorithms. Moreover, it
incorporates the priority issue in the scheduling process.
