Open AccessPriority Intensed Meta Task Scheduling Algorithm for Multi Cloud Environment (PIMTSA)
Author(s) -
Bj. Hubert Shanthan,
L. Arockiam,
A. Cecil Donald,
Arun Kumar,
Raj.S Stephen
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1427/1/012007
Subject(s) - cloud computing , computer science , distributed computing , priority inheritance , priority inversion , scalability , scheduling (production processes) , priority ceiling protocol , cloud testing , earliest deadline first scheduling , virtual machine , deadline monotonic scheduling , task (project management) , execution time , dynamic priority scheduling , operating system , rate monotonic scheduling , computer network , quality of service , cloud computing security , operations management , management , economics
Cloud Computing plays a momentous role for the speedy computation and provides resources to the user on-demand. The conventional single cloud has assorted number of confrontations and they are named as vendor lock-in, resource un-availability, latency, etc. To resolve these conflicts, multi cloud computing is utilized to provide heterogeneous services to the cloud users on demand. Task scheduling is a mechanism used to assign user requests to the cloud server. The proposed PIMTSA algorithm works well for the independent batch of tasks in the static environment. The algorithm is constituted to miniaturize the makespan of the multi cloud systems. It is designed for the batch mode offline multi cloud systems. The priority based approach works to cater the needs of the cloud user. High priority and low priority tasks are the two different priority levels defined by the user. High priority tasks are given more importance and are executed in the high speed VM with minimum completion time. But, the High priority tasks with maximum execution time are given more priority. After the execution of the high priority tasks, the low priority tasks with minimum execution time are allocated to VMs with minimum completion time in the clouds. This algorithm provides a high reliable and scalable computing service to the user with minimum time. In future this work can be extended for dynamic multi cloud environment.