Load Balancing based on Bee Colony Algorithm with Partitioning of Public Clouds
Journal Title: International Journal of Advanced Computer Science & Applications - Year 2018, Vol 9, Issue 4
Abstract
Cloud computing is an emerging trend in the IT industry that provides new opportunities to control costs associated with the creation and maintenance of applications. Of prevalent issues in cloud computing, load balancing is a primary one as it has a significant impact on efficiency and plays a leading role in improved management. In this paper, by using a heuristic search technique called the bee colony algorithm, tasks are balanced on a virtual machine such that their waiting time in the queue is minimized. In the proposed model, the cloud is partitioned into several sectors with many nodes as resources of distributed computing. Furthermore, the indices of speed and cost are considered to prioritize virtual machines. The results of a simulation show that the proposed model outperforms prevalent algorithms as it balances the prioritization of tasks on the virtual machine as well as the entire cloud system and minimizes the waiting times of tasks in the queue. It also reduces the completion time of tasks in comparison with the HBB-LB, WRR, and FCFS algorithms.
Authors and Affiliations
Pouneh Ehsanimoghadam, Mehdi Effatparvar
Keywords
Related Articles
- EP ID EP286444
- DOI 10.14569/IJACSA.2018.090462
- Views 127
- Downloads 0
Clustering based Max-Min Scheduling in Cloud Environment
Cloud Computing ensures Service Level Agreement (SLA) by provisioning of resources to cloudlets. This provisioning can be achieved through scheduling algorithms that properly maps given tasks considering different heuris...
Comparative Study in Performance for Subcarrier Mapping in Uplink 4G-LTE under Different Channel Cases
In recent years, wireless communication has experienced a rapid growth and it promises to become a globally important infrastructure. One common design approach in fourth generation 4G systems is Single Carrier Frequency...
Selection of Eigenvectors for Face Recognition
Face recognition has advantages over other biometric methods. Principal Component Analysis (PCA) has been widely used for the face recognition algorithm. PCA has limitations such as poor discriminatory power and large co...
Scheduling of Distributed Algorithms for Low Power Embedded Systems
Recently, the advent of embedded multicore processors has created interesting technologies for power management. Systems consisting of low-power and high-efficient cores create new possibilities for the optimization of p...
Applying Cellular Automata for Simulating and Assessing Urban Growth Scenario Based in Nairobi, Kenya
This research explores urban growth based scenarios for the city of Nairobi using a cellular automata urban growth model (UGM). African cities have experienced rapid urbanization over the last decade due to increased pop...