Open AccessALLOCATION OF HETEROGENOUS CLOUDLETS ON PRIORITY BASIS IN CLOUD ENVIRONMENT
Author(s) -
Sumanpreet Kaur,
Navtej Singh Ghumman
Publication year - 2017
Publication title -
international journal of computer and technology
Language(s) - English
Resource type - Journals
ISSN - 2277-3061
DOI - 10.24297/ijct.v16i3.6177
Subject(s) - computer science , virtual machine , load balancing (electrical power) , cloud computing , distributed computing , workload , scheduling (production processes) , fifo and lifo accounting , operating system , temporal isolation among virtual machines , priority inheritance , class (philosophy) , fifo (computing and electronics) , grid , dynamic priority scheduling , virtualization , schedule , rate monotonic scheduling , operations management , geometry , mathematics , economics , artificial intelligence
Load balancing is one of the main challenges in cloud computing which is required to distribute the dynamic workload across multiple nodes to ensure that no single node is overwhelmed. It helps in optimal utilization of resources and hence in enhancing the performance of the system. In the natural environment, the cloudlets will be processed in the FIFO (First in First Out approach). We propose an improved load balancing algorithm for job scheduling in the Grid environment. Hence, in this research work, various types of leases have been assigned to the cloudlets like cancellable, suspendable and non-preemtable. The leases have been assigned on the basis of cost assigned to them and the requirement specified by the user. The datacenter broker will receive the list of all the virtual machines and will categorize them into two classes i.e. Class A and Class B. Class A will have high end virtual machines and will process the non-preemptable cloudlets. Class B will contain the low end virtual machines and will process the suspendable and cancellable cloudlets. The machines in each class will be further sorted in descending order according to their MIPS. Multiple parameters have been evaluated like waiting time, turnaround time, execution time and processing cost. Further, this research also provides the anticipated results with the implementation of the proposed algorithm. In the cloud storage, load balancing is a key issue. It would consume a lot of cost to maintain load information, since the system is too huge to timely disperse load. The main contributions of the research work are to balance the entire system load while trying to minimize the make span of a given set of jobs. Compared with the other job scheduling algorithms, the improved load balancing algorithm can outperform them according to the experimental results.