Analysis of Cost-Aware Load Balancing Framework for Cloud Computing using Optimized Scheduling Algorithms
Navpreet Kaur Walia1, Navdeep Kaur2
1Navpreet Kaur Walia, Research Scholar, Assistant Professor, Department of Computer Science, Sri Guru Granth Sahib World University, Chandigarh University, Gharaun (Punjab), India.
2Navdeep Kaur, Professor Department of Computer Science, Sri Guru Granth Sahib World University, Fatehgarh Sahib (Punjab), India.
Manuscript received on 07 April 2019 | Revised Manuscript received on 20 April 2019 | Manuscript published on 30 April 2019 | PP: 873-882 | Volume-8 Issue-6, April 2019 | Retrieval Number: F3667048619/19©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: With the increasing demand of the cloud, load on the cloud server increases and lead to high cost consumption for computations of the tasks. So, to deal with this challenge a lot of researchers develop load balancing algorithms, scheduling algorithms. The current existing algorithms handles the issues of load balancing but the cost of computation is not reduced till now. So, the main focus of this paper to propose a Cost-Aware load Balancing Framework for different existed optimized scheduling algorithms like GA, PBO, and ACO and proposed EPC Scheduling algorithm. EPC is enhanced PBO algorithm where combination of PBO and GA algorithm is used for enhancement. This combination helps to select the appropriate resources for the tasks for its execution and better allocation. In this framework tasks are divided into groups and accordingly resources will be provided to them which increases the utilization factor and reduces cost. For analysis of this proposed framework, homogeneous and heterogeneous cloud environments are generated using local cloud environment and analysis is done on the basis of load balancing factor and cost of computation where minimum 50 and maximum 500 jobs are allocated to 100 resources. Results of the proposed algorithm achieved better results along with the optimization algorithms.
Keyword: Cloud Computing, Task Scheduling, Load Balancing, PBO, ACO, GA, Task Grouping, Cost.
Scope of the Article: Cloud Computing and Networking