Reinforcement Learning Based APO-PTIRIAL for Load Balancing in Cloud Computing Environment
V. Radhamani1, G. Dalin2
1V. Radhamani, Department of Computer Science, Hindustan College of Arts and Science, Coimbatore, (Tamil Nadu), India.
2G. Dalin, Department of Computer Science, Hindustan College of Arts and Science, Coimbatore, (Tamil Nadu), India.
Manuscript received on 15 August 2019. | Revised Manuscript received on 20 August 2019. | Manuscript published on 30 September 2019. | PP: 8527-8531 | Volume-8 Issue-3 September 2019 | Retrieval Number: B3014078219/19©BEIESP | DOI: 10.35940/ijrte.B3014.098319
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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: Power consumption-Traffic aware-Improved Resource Intensity Aware Load balancing (PT-IRIAL) method was proposed to balance load in cloud computing by choosing the migration Virtual Machines (VMs) and the destination Physical Machines (PMs). In this paper, an Artificial Intelligence (AI) technique called Reinforcement Learning (RL) is introduced to find out an optimal time to migrate the selected VM to the selected destination PM. RL enables an agent to find out the most appropriate time for VM migration based on the resource utilization, power consumption, temperature and traffic demand. RL is incorporated into the cloud environment by creating multiple state and action space. The state space is obtained through the computation of resource utilization, power consumption, temperature and traffic of selected VMs. The action space is represented as wait or migrate which is learned through a reward function. Based on the action space, the selected VMs are waiting or migrating to the selected destination PMs.
Index Terms: Load Balancing, Optimized Power Consumption- Traffic Aware- Improved Resource Intensity Aware Load Balancing, Artificial Intelligence, Reinforcement Learning.
Scope of the Article: Cloud Computing