AN OPTIMIZED INTELLIGENT FRAMEWORK FOR SUSTAINABLE AND ENERGY-EFFICIENT DATA CENTER OPERATIONS USING SECURESUSTAINNET

Gayathri A; M. Muzaffar  Hussain; Srinivasan V; Ambeth Raja  A; Jayanthi  R

doi:10.30572/2018/KJE/170105

Authors

Gayathri A Department of Data Science, SRM IST Science and Humanities, Ramapuram, Chennai, India
M. Muzaffar Hussain Department of Artificial Intelligence and Data science, C.Abdul Hakeem College of Engineering and Technology, Melvisharam, India
Srinivasan V Department of MCA, Dayananda Sagar College of Engineering, Kumaraswamy Layout, Bangalore, India
Ambeth Raja A Department of Computer Science, Thiruthangal Nadar College,Chennai, India
Jayanthi R Department of MCA, Dayananda Sagar College of Engineering, Kumaraswamy Layout, Bangalore, India

DOI:

https://doi.org/10.30572/2018/KJE/170105

Keywords:

Sentimental analysis, Machine learning, LSTM, Attention mechanism

Abstract

The SecureSustainNet Framework- a novel technique for enhancing security and sustainability in data centers is introduced in this paper. Given the security and sustainability concerns with data centers, this paper tackles the challenge of balancing data center security and energy efficiency. Existing solutions are unable to provide the required security level without large processing and power costs. States existing approaches that combine efficient resource utilization with high security performance is identified as a research gap. The multi-objective optimization of those approaches is designed to incorporate energy, efficient techniques and security functionalities. The approach has 6 main algorithms; Intrusion Detection System with Anomaly Detection; AES, 256 encryptions with virtualization, based key management; Role Based Access Control RBAC with dynamic policy tuning; dynamic resource allocation; energy consumption monitoring and management; and renewable source integration. The framework has been implemented into the network simulator ns, 3. A remarkably high 98.7% Anomaly Detection Rate ADR was elicited through an interpreter Intrusion Detection System IDS against Gaussian Mixture Models GMM, 6.4% above existing approaches, with a mean 0.4% False Positive Rate FPR. It achieved a 2.5% increase in processing time due to its AES, 256 Encryption system through which employ a dynamically managed key management system. It attained a 97.4% Access Control Effectiveness (ACE) and an 8.8% increase against traditional models through its dynamic policy adaptations based on user context. It obtained an 85.2% Resource Utilization Efficiency RUE and a 7% increase over its competitors while reducing the energy consumption by 15.6% through its efficient resource utilization. The power usage effectiveness PUE of the system was calculated as 1.25, a far cry from current models that achieved it as high as 1.47

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