Priority weighted round robin algorithm for loud balancing in the cloud
| dc.contributor.advisor | Katangur, Ajay K. | |
| dc.contributor.author | Vivekanandhan, Sadiskumar | |
| dc.contributor.committeeMember | Kar, Dulal | |
| dc.contributor.committeeMember | Li, Longzhuang | |
| dc.date.accessioned | 2018-03-23T20:52:36Z | |
| dc.date.available | 2018-03-23T20:52:36Z | |
| dc.date.issued | 2017-12 | |
| dc.description.abstract | Cloud computing, which helps in sharing resources through networks, has become one of the most widely used technologies in recent years. Vast numbers of organizations are moving to the cloud since it is more cost-effective and easy to maintain. An increase in the number of consumers using the cloud, however, results in increased traffic, which leads to the problem of balancing tasks on the loads. Numerous algorithms have been proposed and implemented to handle these loads in different ways. Various dynamic algorithms, which work better than static algorithms, are used by Amazon, Windows Azure, Rackspace, HP, GoGrid, etc \cite{lbaas}. The performance of these dynamic algorithms are scaled with different parameters, such as response time, throughput, utilization, efficiency, etc. Each algorithm works better than others in different parameters. The weighted round-robin algorithm is one of the most widely used load balancing algorithms. The proposed algorithm is an improvement of the weighted round-robin algorithm, which considers the priority of every task before assigning the tasks to different virtual machines (VMs). Usually, the priority of tasks in the weighted round-robin algorithm is used to sort incoming tasks from high to low priority. The proposed algorithm uses the priority of tasks to decide to which VMs the tasks should be assigned dynamically. The same process is used to migrate the tasks from overloaded VMs to under-loaded VMs. The simulations are conducted using CloudSim by varying cloud resources such as data centers, hosts, VMs and different cloudlets for performance analysis. Simulation results show that the proposed algorithm performs equivalent to the dynamic weighted round robin algorithm in all the QoS factors, but it shows significant improvement in handling high-priority tasks. | en_US |
| dc.description.college | College of Science and Engineering | en_US |
| dc.description.department | Computing Sciences | en_US |
| dc.format.extent | 50 pages | en_US |
| dc.identifier.uri | http://hdl.handle.net/1969.6/24399 | |
| dc.language.iso | en_US | en_US |
| dc.rights | This material is made available for use in research, teaching, and private study, pursuant to U.S. Copyright law. The user assumes full responsibility for any use of the materials, including but not limited to, infringement of copyright and publication rights of reproduced materials. Any materials used should be fully credited with its source. All rights are reserved and retained regardless of current or future development or laws that may apply to fair use standards. Permission for publication of this material, in part or in full, must be secured with the author and/or publisher. | en_US |
| dc.subject | priority | en_US |
| dc.subject | priority weighted round | en_US |
| dc.subject | PWRR | en_US |
| dc.title | Priority weighted round robin algorithm for loud balancing in the cloud | en_US |
| dc.type | Text | en_US |
| dc.type.genre | Thesis | en_US |
| thesis.degree.discipline | Computer Science | en_US |
| thesis.degree.grantor | Texas A & M University--Corpus Christi | en_US |
| thesis.degree.level | Masters | en_US |
| thesis.degree.name | Master of Science | en_US |