Adaptive Energy-Optimized Consolidation Algorithm

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Abhinandan P. Mangaonkar
Karuna C. Gull
Sushiladevi Vantamuri
Arpita Patil
Jaya M. Pattanshetti


We have been hearing about cloud computing for quite a long time now. This type of computing is booming and emerging as a popular computing paradigm for its scalability and flexibility in nature. Cloud computing provides the provision of service on-demand, on-demand resources supply and services to end-users. However, energy consumption and energy wastage are becoming a major concern for cloud providers due to its direct impression on costs required for operations and carbon emissions. To tackle this issue, Adaptive Energy-Optimized Consolidation Algorithm has been proposed to efficiently manage energy consumption in cloud environments. This algorithm involves sharing by dividing, in this process resource allocation is done into two different phases, those are, consolidation of tasks and consolidation of resources. Compared to single-task consolidation algorithms, the proposed two-phase Adaptive energy optimized consolidation algorithm shows improved performance in terms of energy efficiency and resource utilization. The results of experiments conducted using a cloud-sim show the effectiveness of the proposed algorithm in decreasing energy consumption while maintaining the quality-of-service requirements of computing in cloud. 

The need for an hour is to automate things without human intervention. Thus, using Autonomous computing refers to a type of computing system that is capable of performing tasks and making decisions without the intervention of humans. This type of system typically relies on Artificial.Intelligence, Machine.Learning, and other futuristic technologies to study the data, identify patterns, and make decisions based on that data. Cloud computing can certainly be incorporated into an autonomous computing system. The performance of an automated computing environment depends on a various factor, considering the quality of the different algorithms used, also the amount and quality of various data available to the system, the computational resources available, and the system's ability to learn and adapt over time. However, by incorporating cloud computing, an autonomous computing system can potentially access more resources and process data more quickly, which can improve its overall performance.

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Mangaonkar, A. P. ., Gull, K. C. ., Vantamuri, S. ., Patil, A. ., & Pattanshetti, J. M. . (2023). Adaptive Energy-Optimized Consolidation Algorithm. International Journal on Recent and Innovation Trends in Computing and Communication, 11(5s), 150–158.


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