An objective study on improvement of task scheduling mechanism using computational intelligence in cloud computing

Cloud Computing facilitates delivery of various types of computational services through the internet. These services can be availed according to the user demand. The resource scarcity problems within the Service Providers are met using Virtualization technique, which allows scalability of resources and thereby helps to meet the client requirements. Allocation of resources to client tasks is an issue that is being addressed for a long time. Due to the increased complexity in the area, there has not yet been a perfect scheduling mechanism. Practices have been done profusely in order to find solutions for scheduling that nears optimality. A single scheduling mechanism may not always give the expected outcome. The task scheduling mechanisms are designed in a manner as to optimize some metrics related to the Cloud. This paper overviews various literature associated with task scheduling and resource scheduling in Cloud Computing. An examination of the techniques is done and a proposal is made, which will allow to further improve the scheduling mechanism.

[1]  David Vengerov,et al.  A reinforcement learning framework for utility-based scheduling in resource-constrained systems , 2009, Future Gener. Comput. Syst..

[2]  Ji Li,et al.  An Greedy-Based Job Scheduling Algorithm in Cloud Computing , 2014, J. Softw..

[3]  Jie Xu,et al.  Analysis, Modeling and Simulation of Workload Patterns in a Large-Scale Utility Cloud , 2014, IEEE Transactions on Cloud Computing.

[4]  Satyendra Singh Chouhan,et al.  OFDTs:-An optimally fair dynamic task scheduling algorithm in cloud environment , 2014, 2014 Seventh International Conference on Contemporary Computing (IC3).

[5]  Seyedmehdi Hosseinimotlagh,et al.  Migration-less Energy-Aware Task Scheduling Policies in Cloud Environments , 2014, 2014 28th International Conference on Advanced Information Networking and Applications Workshops.