Anureet A. Kaur

Work place: Chandigarh Engineering College, Landran / Deptt. Of Information Technology, Morinda, 140101, India



Research Interests: Autonomic Computing, Computing Platform, Mathematics of Computing


Anureet A. Kaur Morinda, April 10, 2017. She holds the degree of in Information Technology from Rayat and Bahra Engineering College Mohali, Punjab, India, 2013, from Chandigarh Engineering College, landran, Mohali, Punjab, India. She is an Active Researcher who has contributed 3 research papers in various national & international conferences. She also contributed 2 research papers in Journals .Her areas of interest are Cloud computing.
The lists of publications are: Cost Aware Load balanced Task scheduling with active VM Load Evaluation (Bikaner, Rajasthan, and ACM Conference ID: 39190, 2016). Load balancing in Tasks using Honeybee Behavior Algorithm in Cloud Computing (Rajpura, Punjab, IEEE Conference ID: 38683X, 2016). Energy Aware Task Scheduling with Adaptive Clustering method in cloud computing: (Gurgaon, Haryana, Taylor and Francis, pp. 989-992, 2016).

Author Articles
An Effective Technique to Decline Energy Expenditure in Cloud Platforms

By Anureet A. Kaur Bikrampal B. Kaur

DOI:, Pub. Date: 8 Feb. 2018

The cloud computing is the rapidly growing technology in the IT world. A vital aim of the cloud is to provide the services or resources where they are needed. From the user’s prospective convenient computing resources are limitless thatswhy the client does not worry that how many numbers of servers positioned at one site so it is the liability of the cloud service holder to have large number of resources. In cloud data-centers, huge bulk of power exhausted by different computing devices.Energy conservancy is a major concern in the cloud computing systems. From the last several years, the different number of techniques was implemented to minimize that problem but the expected results are not achieved. Now, in the proposed research work, a technique called Enhanced - ACO that is developed to achieve better offloading decisions among virtual machines when the reliability and proper utilization of resources will also be considered and will use ACO algorithm to balance load and energy consumption in cloud environment. The proposed technique also minimizes energy consumption and cost of computing resources that are used by different processes for execution in cloud. The earliest finish time and fault tolerance is evaluated to achieve the objectives of proposed work. The experimental outcomes show the better achievement of prospective model with comparison of existing one. Meanwhile, energy-awake scheduling approach with Ant colony optimization method is an assuring method to accomplish that objective.

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